413 lines
198 KiB
HTML
413 lines
198 KiB
HTML
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styled__ImageWrapperStyled-sc-2c2295e1-1 eSvYQq hKhSsm"><img loading="eager" src="https://d21hwc2yj2s6ok.cloudfront.net/shrine_store/uploads/networks/3057/communication_news/11454232/ultra_wide-ed0edc344721c0ad9c25bb91dc93cb91.webp" alt="" decoding="async" fetchPriority="high" class="ui__StyledImg-sc-9b9ac9c0-0 llRGgU styled__ImageStyled-sc-2c2295e1-2 kAqQFG"/></div><div data-testid="news-content" class="Box-sc-3e41dc6a-0 styled__WrapperContentStyled-sc-2c2295e1-0 ercVhJ eOJpUd"><div class="Box-sc-3e41dc6a-0 styled__HeaderStyled-sc-2c2295e1-3 fScRFm bsTAPu"><div class="Box-sc-3e41dc6a-0 fzdQzq"><div class="Box-sc-3e41dc6a-0 cNWsPK"><div class="Box-sc-3e41dc6a-0 bRccPD"><h1 class="Box-sc-3e41dc6a-0 biNsvt">Decompensation and Cascading Failures</h1><div class="Box-sc-3e41dc6a-0 ldkrga"></div></div><div class="Box-sc-3e41dc6a-0 jYXfit">Published on January 22, 2026</div><div style="height:48px"></div></div></div><div class="Box-sc-3e41dc6a-0 bWsukS"><div class="Box-sc-3e41dc6a-0 cXOiip"></div></div></div><div class="Box-sc-3e41dc6a-0 PublicationText__Wrapper-sc-272ab012-0 bSDPrX dSKAKJ"><div class="HtmlContentWrapperWithStyle-sc-d185182e-0 ckSTdk"><p dir="ltr">Consider the following scenario: A set of automated tasks has somehow failed to run to completion. Because a thorough fix will take some time and the tasks you need run are time-sensitive, you complete the work manually to keep things moving forward. If this sounds familiar, then you may have helped provide <em>compensation</em> for that system. Compensation is a very interesting mechanism in software systems because it can keep complex systems alive, but also because it can be a factor in how they quickly and unexpectedly collapse.</p>
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<p dir="ltr">There’s a tangling of closely related concepts in describing how complex systems collapse. Many of these lead to cascading failures, where a failure in one part of the system propagates to another one or to many others, which further causes other subsystems to fail.</p>
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<p dir="ltr">Past figuring out how individual failures happen, there are ways in which the system fragilizes itself, such as increasing coupling between parts by <a href="https://ferd.ca/notes/paper-going-solid.html" rel="nofollow noreferrer noopener">going solid</a>. A more subtle way coupling can happen is through compensation, which hides that anything might even be going wrong.</p>
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||
<p dir="ltr">In this post, we’ll take a look at what compensation means, and then how decompensation can rapidly happen and lead to a cascade of failures, and why this is relevant to Resilience Engineering.</p>
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<h3 dir="ltr">Compensation</h3>
|
||
<p dir="ltr">A basic rule of complex systems is that <a href="https://how.complexsystems.fail/#5" rel="nofollow noreferrer noopener">they always run in a degraded mode</a>, but they continue to function because the system has ways to deal with some of these situations. Degradation can take multiple forms. A partial loss of capacity or temporary overload can be handled by tradeoffs between efficiency and thoroughness (see the <a href="https://erikhollnagel.com/ideas/etto-principle/" rel="nofollow noreferrer noopener">ETTO principle</a>), or through structural elements like redundancy and building spare capacity, for example. A more subtle coping mechanism relies on adaptive patterns of compensation, a term borrowed from biology that indicates the mechanism through which an organism finds new ways to perform a capacity that has been lost.</p>
|
||
<p dir="ltr">Compensation is rarely seen in software as we often think of it, e.g. code running without humans, because software tends to be designed such that everything does the job it has been designed to do, and little else. Instead, compensation in this context, as an adaptive mechanism, is most likely to be observed in the human part of a sociotechnical system.</p>
|
||
<p dir="ltr">Examples of humans compensating for software faults or flaws could include:</p>
|
||
<ul>
|
||
<li dir="ltr">
|
||
<p dir="ltr">In provisioning new enterprise accounts, the system will automatically allocate sufficient resources for the contract. However, because new users tend to do large migration jobs when setting up their environment, the customer success team makes a habit of manually overriding parameters to grant more capacity for the first weeks of use, before returning it to normal once usage is steady.</p>
|
||
</li>
|
||
<li dir="ltr">
|
||
<p dir="ltr">During an outage where hardware capacity is limited, a team will decide to run their own services hotter by tweaking their config to do more work on fewer devices or on older hardware (possibly slightly hurting their own tail latencies in the process) in order to leave more room for other teams’ more critical services whose lack of resources would have a worse impact on users.</p>
|
||
</li>
|
||
<li dir="ltr">
|
||
<p dir="ltr">A cache is added in front of a database to speed up responses, and limit the impact of slow queries. As the database becomes reliant on the cache to protect it, teams gradually build utilities and ad-hoc processes to backfill and prewarm the cache in case of issues, rather than spending time on the more challenging task of optimizing queries or re-thinking their product design.</p>
|
||
</li>
|
||
</ul>
|
||
<p dir="ltr">Likewise, people can and will compensate <em>for each other</em>:</p>
|
||
<ul>
|
||
<li dir="ltr">
|
||
<p dir="ltr">An on-call worker has been on an incident at night, and as they are recovering or exhausted, someone else who is not currently on-call intercepts and fields alerts instead, just to make sure their colleague is not woken up.</p>
|
||
</li>
|
||
<li dir="ltr">
|
||
<p dir="ltr">Designers are unavailable, but a graphical bug has been ticketed. You decide to fix it to the best of your ability by looking at similar elements in your product despite not having design as a requirement or expectation of your role.</p>
|
||
</li>
|
||
<li dir="ltr">
|
||
<p dir="ltr">The platform team is swamped with work to support other internal customers. But deadline pressure in the product team cannot wait for the platform team to become available. So the product team spins up cloud services on their own to work around the perceived bottleneck of the platform team.</p>
|
||
</li>
|
||
</ul>
|
||
<p dir="ltr">You’ll note that all these examples of compensation require doing things such as a unit bending and unofficially extending their definition of work, using their own spare capacity to help or cover for another part of the system that is under stress, or doing a mix of both to provide capabilities that are desirable but currently not part of the nominal function. <strong>Compensation can be temporary, or, over time, become a permanent fixture of how the system works.</strong></p>
|
||
<h2 dir="ltr">Decompensation</h2>
|
||
<p dir="ltr">Compensatory mechanisms are often called on so gradually that your average observer wouldn't even know it's taking place. Systems (or organisms) that appear absolutely healthy one day collapse, and that is how we discover they were overextended for a long while. </p>
|
||
<p dir="ltr">To understand decompensation, let’s go back to biological systems, with an example of congestive heart failure provided by Dr. Richard I. Cook in a group discussion a few years ago.</p>
|
||
<p dir="ltr">Effects of heart damage accumulate gradually over the years—partly just by aging—and can be offset by compensatory mechanisms in the human body. As the heart becomes weaker and pumps less blood with each beat, adjustments manage to keep the overall flow constant over time. This can be done by increasing the heart rate using complex neural and hormonal signaling.</p>
|
||
<p dir="ltr">Other processes can be added to this: kidneys faced with lower blood pressure and flow can reduce how much urine they create to keep more fluid in the circulatory system, which increases cardiac filling pressure, which stretches the heart further before each beat, which adds to the stroke volume. Multiple pathways of this kind exist through the body, and they can maintain or optimize cardiac performance.</p>
|
||
<p dir="ltr">However, each of these compensatory mechanisms has other, less desirable consequences. The heart remains damaged and they offset it, but the organism remains unable to generate greater cardiac output, such as would be required during exercise. You would therefore see "normal" cardiac performance at rest, with little ability to deal with increased demand. If the damage is gradual enough, the organism will adjust its behavior to maintain compensation: you will walk slower, take breaks while climbing stairs, and will just generally avoid situations that strain your body. This may be done without awareness of the decreased capacity of the system, and you may even resist acknowledging that you ever slowed down.</p>
|
||
<p dir="ltr"><strong>Decompensation happens when all the compensatory mechanisms no longer prevent a downward spiral</strong>. If the heart can't maintain its output anymore, other organs (most often the kidneys) start failing. A failing organ can't overextend itself to help the heart; what was a stable negative feedback loop (slowing down some adverse effect) becomes a positive feedback loop (accelerating and amplifying things), which quickly leads to collapse and death.</p>
|
||
<p dir="ltr">Someone with a compensated congestive heart failure appears externally well and stable. They have gradually adjusted their habits to cope with their limited capacity as their heart weakened through life. However, looking well and healthy can hide how precarious of a position the person is in. Someone in their late sixties skipping their heart medication for a few days or ignoring their low-salt diet could be enough to tip the scales into decompensation.</p>
|
||
<p dir="ltr"><strong>Decompensation usually doesn’t happen because compensation mechanisms fail, but because their range is exhausted.</strong> A system that is compensating looks fine until it doesn’t. That's when failures may cascade and major breakdowns occur.</p>
|
||
<h2 dir="ltr">Decompensation and Resilience in Software</h2>
|
||
<p dir="ltr">A classic case of decompensation in software systems is introducing queues to handle temporary overload in front of services with fixed capacity. However, as the lulls that are used to catch up become rarer with increased usage, the software system reaches <a href="https://surfingcomplexity.blog/2025/12/23/saturation-waymo-edition/" rel="nofollow noreferrer noopener">saturation</a>. People might try to compensate by increasing the queue size or increasing job timeouts, but as delays get worse, tasks get re-enqueued by clients, which further saturates the queue; at this point the ability to delay jobs is no longer effective at controlling load, and the accelerating feedback loop tells us the system is on its way to collapse.</p>
|
||
<p dir="ltr">From that point on, we may encounter a cascading failure. Some <em>tipping point</em> is met, and one failure mode grows and propagates throughout the rest of the system. While cascading failures are fascinating and worth studying on their own, they can be caused by a variety of mechanisms. Decompensation is one of them, but so are <a href="https://www.sciencedirect.com/topics/engineering/common-mode-failure" rel="nofollow noreferrer noopener">common mode failures</a>—a simultaneous failure of several components due to a single shared dependency</p>
|
||
<p dir="ltr">Decompensation will tend to be more subtle because compensating behavior hides problems. We could think of auto-scaling reaching its limits and then killing a cluster because it falls behind on its ability to do work as a common mode failure. This could become decompensation if you discovered that whenever your system neared its scaling limits, engineers went in and made optimizations that kept squeezing the lemon further and further, trying to find ways to reduce demand.</p>
|
||
<p dir="ltr">Common mode failures are worth looking into because they too can cause cascades, and might reveal gaps in how we analyzed or planned our systems. While compensation and decompensation can reveal similar issues, they are of particular interest to Resilience Engineering because they are also signs of <em>adaptive behavior</em>.</p>
|
||
<h2 dir="ltr">Adaptive Behavior, Adaptation, and Resilience Engineering</h2>
|
||
<p dir="ltr">It is worthwhile to make a distinction between compensation, which is a type of adaptive process, and adaptation writ large. See compensation as coping with ongoing pressures or degradations (even if they never end!), whereas successful adaptation could be thought of as reaching new, more optimal operating points. Think of how someone driving a manual car with worn out brakes (or who fears their brakes might overheat) might use <a href="https://en.wikipedia.org/wiki/Engine_braking" rel="nofollow noreferrer noopener">engine braking</a> to slow the car down when going downhill; this is very different from <a href="https://en.wikipedia.org/wiki/Regenerative_braking" rel="nofollow noreferrer noopener">regenerative braking</a> in electric or hybrid vehicles, which is a designed feature.</p>
|
||
<p dir="ltr">The reason this distinction is useful is that while compensation has people work outside the design envelope, finding this behavior is often key to <em>redefining</em> the design envelope. Successful adaptation requires harnessing adaptive behaviors <em>and</em> reworking the system.</p>
|
||
<p dir="ltr">This fundamentally requires you to study <a href="https://humanisticsystems.com/2016/12/05/the-varieties-of-human-work/" rel="nofollow noreferrer noopener">work-as-done</a> (a topic for another blog post to come) to understand where people act with reciprocity, sacrificing bits of their performance to help the overall system, where they bend rules and definitions to provide success. In places where this is a habit, you will find both strength in how they adapt, and brittleness in the risk of becoming dependent on this hidden work without knowing about it, and exhausting all such capacity in the future. </p>
|
||
<p dir="ltr">Part of Resilience Engineering in practice is knowing this happens, that it is necessary to deal with the unexpected, and knowing how to both protect, foster, and build on this type of capacity to keep systems successful.</p>
|
||
<h3 dir="ltr">Further Reading</h3>
|
||
<p dir="ltr">If you’d like to learn more about decompensation, along with two other contributors to complex system failures, I recommend starting with <a href="https://www.researchgate.net/publication/284324002_Basic_patterns_in_how_adaptive_systems_fail" rel="nofollow noreferrer noopener">Basic Patterns in How Adaptive Systems Fail</a>.</p>
|
||
<p dir="ltr"> </p>
|
||
<p><img src="https://d21hwc2yj2s6ok.cloudfront.net/shrine_store/uploads/networks/3057/communication_news/11454232/compressed-cbe8d7ef02a736d3dce5cb949c6453fc.webp" alt="" width="134" height="134"></p>
|
||
<p><strong>Fred Hebert</strong></p>
|
||
<p>Staff SRE, Honeycomb</p>
|
||
<p> </p></div></div></div></div></div></div></div></div><div class="Box-sc-3e41dc6a-0 nSVk"><div class="Grid__GridStyled-sc-c7f19ef3-0 CbzOM"><div class="Box-sc-3e41dc6a-0 Row__RowStyled-sc-a5c2e20d-0 jSILAH ylcnO"><div tabindex="-1" class="Col__ColStyled-sc-df0bb68b-0 dagzBg"><div class="Box-sc-3e41dc6a-0 Col__ColInnerStyled-sc-df0bb68b-1 kxQFCu jHhhMk"><div class="Box-sc-3e41dc6a-0 goizoB">Other news</div></div></div></div><div class="Box-sc-3e41dc6a-0 Row__RowStyled-sc-a5c2e20d-0 jNYxIR hEDgQU"><div tabindex="-1" class="Col__ColStyled-sc-df0bb68b-0 dagzBg"><div class="Box-sc-3e41dc6a-0 Col__ColInnerStyled-sc-df0bb68b-1 kxQFCu jHhhMk"><a data-testid="default-link-element" href="/news/1288714"><div tabindex="0" class="Box-sc-3e41dc6a-0 Card-sc-2ed53ea2-0 styled__RelatedCardStyled-sc-2c2295e1-11 dQNeud jgrSBy jkJbIz"><div data-testid="news-cover-wrapper" class="Box-sc-3e41dc6a-0 styled__RelatedImgWrapper-sc-2c2295e1-7 hZbbrS cycDCt"><div class="Box-sc-3e41dc6a-0 styled__RelatedImgStyled-sc-2c2295e1-6 ehDvKl cnOSLX"></div></div><div class="Box-sc-3e41dc6a-0 styled__RelatedContentWrapper-sc-2c2295e1-4 hrksWC jCJtHJ"><div class="Box-sc-3e41dc6a-0 styled__RelatedAnimatedContentBox-sc-2c2295e1-5 bZeHqR jbrAqA"><div data-testid="link-to-news" class="Box-sc-3e41dc6a-0 eSvYQq"><h3 class="Box-sc-3e41dc6a-0 styled__RelatedLinkStyled-sc-2c2295e1-8 hVyqKD bdXqxJ"><div class="ClampedLinkWorkaround-sc-7be7fc1a-0 jYtrQc">Negotiating the Paradox We Face in Resilience Engineering—Lessons From an Engineering Leader</div></h3></div><div class="Box-sc-3e41dc6a-0 styled__RelatedDateStyled-sc-2c2295e1-9 jwSNmt">July 29, 2025</div><div class="Box-sc-3e41dc6a-0 styled__RelatedDescription-sc-2c2295e1-10 eSvYQq hfHojJ"><div>Author’s note: I need to credit my friend and former colleague Tim Nicholas for his review and contribution to this article and the insights within it. Tim has been pivotal in shaping how I understand failures in complex systems and Resilience Engineering, and how I incorporate this into my overall approach as an engineering leader. (Also, the spelling is New Zealand English.) So you’re an Enginee</div></div></div></div></div></a></div></div><div tabindex="-1" class="Col__ColStyled-sc-df0bb68b-0 ckeRsZ"><div class="Box-sc-3e41dc6a-0 Col__ColInnerStyled-sc-df0bb68b-1 kxQFCu jHhhMk"><a data-testid="default-link-element" href="/news/11453533"><div tabindex="0" class="Box-sc-3e41dc6a-0 Card-sc-2ed53ea2-0 styled__RelatedCardStyled-sc-2c2295e1-11 dQNeud jgrSBy jkJbIz"><div data-testid="news-cover-wrapper" class="Box-sc-3e41dc6a-0 styled__RelatedImgWrapper-sc-2c2295e1-7 hZbbrS cycDCt"><div class="Box-sc-3e41dc6a-0 styled__RelatedImgStyled-sc-2c2295e1-6 ehDvKl eiTvNp"></div></div><div class="Box-sc-3e41dc6a-0 styled__RelatedContentWrapper-sc-2c2295e1-4 hrksWC jCJtHJ"><div class="Box-sc-3e41dc6a-0 styled__RelatedAnimatedContentBox-sc-2c2295e1-5 bZeHqR jbrAqA"><div data-testid="link-to-news" class="Box-sc-3e41dc6a-0 eSvYQq"><h3 class="Box-sc-3e41dc6a-0 styled__RelatedLinkStyled-sc-2c2295e1-8 hVyqKD bdXqxJ"><div class="ClampedLinkWorkaround-sc-7be7fc1a-0 jYtrQc">Expertise and Overload</div></h3></div><div class="Box-sc-3e41dc6a-0 styled__RelatedDateStyled-sc-2c2295e1-9 jwSNmt">February 21, 2026</div><div class="Box-sc-3e41dc6a-0 styled__RelatedDescription-sc-2c2295e1-10 eSvYQq hfHojJ"><div>Resilience engineering views incidents through a different frame than the conventional approach in the software industry, which tends to treat incidents as an irritating interruption to our work. The unrelenting pressure of the roadmap hangs over retrospectives. The clock is always ticking. We focus on the quickest fixes that might prevent the failure in the future so we can “get back to work.” To</div></div></div></div></div></a></div></div></div></div></div><!--$--><!--/$--></div><footer data-testid="public-footer" class="Box-sc-3e41dc6a-0 gSMlAf"><div class="Box-sc-3e41dc6a-0 fzHJGr"><div class="Box-sc-3e41dc6a-0 PublicFooter__FooterGrid-sc-53239817-0 gVbWAc cfIpyX"><div class="Box-sc-3e41dc6a-0 jEIODi"><div class="Box-sc-3e41dc6a-0 bSDPrX"><img alt="" src="https://d21hwc2yj2s6ok.cloudfront.net/shrine_store/uploads/networks/3057/networks/3057/large-3f248e83a4a0d6bf5f88b14a073f1c2f.webp" class="PublicFooter__StyledImage-sc-53239817-1 hxTiJU"/></div></div><div class="Box-sc-3e41dc6a-0 kYiAMs"><div class="Box-sc-3e41dc6a-0 hluIfI"><div class="Box-sc-3e41dc6a-0 StyledBox-sc-c4ba1a96-0 khFeJe dGhvCS">Contact</div><div class="Box-sc-3e41dc6a-0 StyledBox-sc-c4ba1a96-0 gdnuBL dGhvCS"><a href="/cdn-cgi/l/email-protection" class="__cf_email__" 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dir=\"ltr\"\u003eConsider the following scenario: A set of automated tasks has somehow failed to run to completion. Because a thorough fix will take some time and the tasks you need run are time-sensitive, you complete the work manually to keep things moving forward. If this sounds familiar, then you may have helped provide \u003cem\u003ecompensation\u003c/em\u003e for that system. Compensation is a very interesting mechanism in software systems because it can keep complex systems alive, but also because it can be a factor in how they quickly and unexpectedly collapse.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eThere’s a tangling of closely related concepts in describing how complex systems collapse. Many of these lead to cascading failures, where a failure in one part of the system propagates to another one or to many others, which further causes other subsystems to fail.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003ePast figuring out how individual failures happen, there are ways in which the system fragilizes itself, such as increasing coupling between parts by \u003ca href=\"https://ferd.ca/notes/paper-going-solid.html\" rel=\"nofollow noreferrer noopener\"\u003egoing solid\u003c/a\u003e. A more subtle way coupling can happen is through compensation, which hides that anything might even be going wrong.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eIn this post, we’ll take a look at what compensation means, and then how decompensation can rapidly happen and lead to a cascade of failures, and why this is relevant to Resilience Engineering.\u003c/p\u003e\n\u003ch3 dir=\"ltr\"\u003eCompensation\u003c/h3\u003e\n\u003cp dir=\"ltr\"\u003eA basic rule of complex systems is that \u003ca href=\"https://how.complexsystems.fail/#5\" rel=\"nofollow noreferrer noopener\"\u003ethey always run in a degraded mode\u003c/a\u003e, but they continue to function because the system has ways to deal with some of these situations. Degradation can take multiple forms. A partial loss of capacity or temporary overload can be handled by tradeoffs between efficiency and thoroughness (see the \u003ca href=\"https://erikhollnagel.com/ideas/etto-principle/\" rel=\"nofollow noreferrer noopener\"\u003eETTO principle\u003c/a\u003e), or through structural elements like redundancy and building spare capacity, for example. A more subtle coping mechanism relies on adaptive patterns of compensation, a term borrowed from biology that indicates the mechanism through which an organism finds new ways to perform a capacity that has been lost.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eCompensation is rarely seen in software as we often think of it, e.g. code running without humans, because software tends to be designed such that everything does the job it has been designed to do, and little else. Instead, compensation in this context, as an adaptive mechanism, is most likely to be observed in the human part of a sociotechnical system.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eExamples of humans compensating for software faults or flaws could include:\u003c/p\u003e\n\u003cul\u003e\n\u003cli dir=\"ltr\"\u003e\n\u003cp dir=\"ltr\"\u003eIn provisioning new enterprise accounts, the system will automatically allocate sufficient resources for the contract. However, because new users tend to do large migration jobs when setting up their environment, the customer success team makes a habit of manually overriding parameters to grant more capacity for the first weeks of use, before returning it to normal once usage is steady.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli dir=\"ltr\"\u003e\n\u003cp dir=\"ltr\"\u003eDuring an outage where hardware capacity is limited, a team will decide to run their own services hotter by tweaking their config to do more work on fewer devices or on older hardware (possibly slightly hurting their own tail latencies in the process) in order to leave more room for other teams’ more critical services whose lack of resources would have a worse impact on users.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli dir=\"ltr\"\u003e\n\u003cp dir=\"ltr\"\u003eA cache is added in front of a database to speed up responses, and limit the impact of slow queries. As the database becomes reliant on the cache to protect it, teams gradually build utilities and ad-hoc processes to backfill and prewarm the cache in case of issues, rather than spending time on the more challenging task of optimizing queries or re-thinking their product design.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"ltr\"\u003eLikewise, people can and will compensate \u003cem\u003efor each other\u003c/em\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli dir=\"ltr\"\u003e\n\u003cp dir=\"ltr\"\u003eAn on-call worker has been on an incident at night, and as they are recovering or exhausted, someone else who is not currently on-call intercepts and fields alerts instead, just to make sure their colleague is not woken up.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli dir=\"ltr\"\u003e\n\u003cp dir=\"ltr\"\u003eDesigners are unavailable, but a graphical bug has been ticketed. You decide to fix it to the best of your ability by looking at similar elements in your product despite not having design as a requirement or expectation of your role.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli dir=\"ltr\"\u003e\n\u003cp dir=\"ltr\"\u003eThe platform team is swamped with work to support other internal customers. But deadline pressure in the product team cannot wait for the platform team to become available. So the product team spins up cloud services on their own to work around the perceived bottleneck of the platform team.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"ltr\"\u003eYou’ll note that all these examples of compensation require doing things such as a unit bending and unofficially extending their definition of work, using their own spare capacity to help or cover for another part of the system that is under stress, or doing a mix of both to provide capabilities that are desirable but currently not part of the nominal function. \u003cstrong\u003eCompensation can be temporary, or, over time, become a permanent fixture of how the system works.\u003c/strong\u003e\u003c/p\u003e\n\u003ch2 dir=\"ltr\"\u003eDecompensation\u003c/h2\u003e\n\u003cp dir=\"ltr\"\u003eCompensatory mechanisms are often called on so gradually that your average observer wouldn't even know it's taking place. Systems (or organisms) that appear absolutely healthy one day collapse, and that is how we discover they were overextended for a long while.\u0026nbsp;\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eTo understand decompensation, let’s go back to biological systems, with an example of congestive heart failure provided by\u0026nbsp; Dr. Richard I. Cook in a group discussion a few years ago.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eEffects of heart damage accumulate gradually over the years—partly just by aging—and can be offset by compensatory mechanisms in the human body. As the heart becomes weaker and pumps less blood with each beat, adjustments manage to keep the overall flow constant over time. This can be done by increasing the heart rate using complex neural and hormonal signaling.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eOther processes can be added to this: kidneys faced with lower blood pressure and flow can reduce how much urine they create to keep more fluid in the circulatory system, which increases cardiac filling pressure, which stretches the heart further before each beat, which adds to the stroke volume. Multiple pathways of this kind exist through the body, and they can maintain or optimize cardiac performance.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eHowever, each of these compensatory mechanisms has other, less desirable consequences. The heart remains damaged and they offset it, but the organism remains unable to generate greater cardiac output, such as would be required during exercise. You would therefore see \"normal\" cardiac performance at rest, with little ability to deal with increased demand. If the damage is gradual enough, the organism will adjust its behavior to maintain compensation: you will walk slower, take breaks while climbing stairs, and will just generally avoid situations that strain your body. This may be done without awareness of the decreased capacity of the system, and you may even resist acknowledging that you ever slowed down.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cstrong\u003eDecompensation happens when all the compensatory mechanisms no longer prevent a downward spiral\u003c/strong\u003e. If the heart can't maintain its output anymore, other organs (most often the kidneys) start failing. A failing organ can't overextend itself to help the heart; what was a stable negative feedback loop (slowing down some adverse effect) becomes a positive feedback loop (accelerating and amplifying things), which quickly leads to collapse and death.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eSomeone with a compensated congestive heart failure appears externally well and stable. They have gradually adjusted their habits to cope with their limited capacity as their heart weakened through life. However, looking well and healthy can hide how precarious of a position the person is in. Someone in their late sixties skipping their heart medication for a few days or ignoring their low-salt diet could be enough to tip the scales into decompensation.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cstrong\u003eDecompensation usually doesn’t happen because compensation mechanisms fail, but because their range is exhausted.\u003c/strong\u003e A system that is compensating looks fine until it doesn’t. That's when failures may cascade and major breakdowns occur.\u003c/p\u003e\n\u003ch2 dir=\"ltr\"\u003eDecompensation and Resilience in Software\u003c/h2\u003e\n\u003cp dir=\"ltr\"\u003eA classic case of decompensation in software systems is introducing queues to handle temporary overload in front of services with fixed capacity. However, as the lulls that are used to catch up become rarer with increased usage, the software system reaches \u003ca href=\"https://surfingcomplexity.blog/2025/12/23/saturation-waymo-edition/\" rel=\"nofollow noreferrer noopener\"\u003esaturation\u003c/a\u003e. People might try to compensate by increasing the queue size or increasing job timeouts, but as delays get worse, tasks get re-enqueued by clients, which further saturates the queue; at this point the ability to delay jobs is no longer effective at controlling load, and the accelerating feedback loop tells us the system is on its way to collapse.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eFrom that point on, we may encounter a cascading failure. Some \u003cem\u003etipping point\u003c/em\u003e is met, and one failure mode grows and propagates throughout the rest of the system. While cascading failures are fascinating and worth studying on their own, they can be caused by a variety of mechanisms. Decompensation is one of them, but so are \u003ca href=\"https://www.sciencedirect.com/topics/engineering/common-mode-failure\" rel=\"nofollow noreferrer noopener\"\u003ecommon mode failures\u003c/a\u003e—a simultaneous failure of several components due to a single shared dependency\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eDecompensation will tend to be more subtle because compensating behavior hides problems. We could think of auto-scaling reaching its limits and then killing a cluster because it falls behind on its ability to do work as a common mode failure. This could become decompensation if you discovered that whenever your system neared its scaling limits, engineers went in and made optimizations that kept squeezing the lemon further and further, trying to find ways to reduce demand.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eCommon mode failures are worth looking into because they too can cause cascades, and might reveal gaps in how we analyzed or planned our systems. While compensation and decompensation can reveal similar issues, they are of particular interest to Resilience Engineering because they are also signs of \u003cem\u003eadaptive behavior\u003c/em\u003e.\u003c/p\u003e\n\u003ch2 dir=\"ltr\"\u003eAdaptive Behavior, Adaptation, and Resilience Engineering\u003c/h2\u003e\n\u003cp dir=\"ltr\"\u003eIt is worthwhile to make a distinction between compensation, which is a type of adaptive process, and adaptation writ large. See compensation as coping with ongoing pressures or degradations (even if they never end!), whereas successful adaptation could be thought of as reaching new, more optimal operating points. Think of how someone driving a manual car with worn out brakes (or who fears their brakes might overheat) might use \u003ca href=\"https://en.wikipedia.org/wiki/Engine_braking\" rel=\"nofollow noreferrer noopener\"\u003eengine braking\u003c/a\u003e to slow the car down when going downhill; this is very different from \u003ca href=\"https://en.wikipedia.org/wiki/Regenerative_braking\" rel=\"nofollow noreferrer noopener\"\u003eregenerative braking\u003c/a\u003e in electric or hybrid vehicles, which is a designed feature.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eThe reason this distinction is useful is that while compensation has people work outside the design envelope, finding this behavior is often key to \u003cem\u003eredefining\u003c/em\u003e the design envelope. Successful adaptation requires harnessing adaptive behaviors \u003cem\u003eand\u003c/em\u003e reworking the system.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eThis fundamentally requires you to study \u003ca href=\"https://humanisticsystems.com/2016/12/05/the-varieties-of-human-work/\" rel=\"nofollow noreferrer noopener\"\u003ework-as-done\u003c/a\u003e (a topic for another blog post to come) to understand where people act with reciprocity, sacrificing bits of their performance to help the overall system, where they bend rules and definitions to provide success. In places where this is a habit, you will find both strength in how they adapt, and brittleness in the risk of becoming dependent on this hidden work without knowing about it, and exhausting all such capacity in the future.\u0026nbsp;\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003ePart of Resilience Engineering in practice is knowing this happens, that it is necessary to deal with the unexpected, and knowing how to both protect, foster, and build on this type of capacity to keep systems successful.\u003c/p\u003e\n\u003ch3 dir=\"ltr\"\u003eFurther Reading\u003c/h3\u003e\n\u003cp dir=\"ltr\"\u003eIf you’d like to learn more about decompensation, along with two other contributors to complex system failures, I recommend starting with \u003ca href=\"https://www.researchgate.net/publication/284324002_Basic_patterns_in_how_adaptive_systems_fail\" rel=\"nofollow noreferrer noopener\"\u003eBasic Patterns in How Adaptive Systems Fail\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"https://d21hwc2yj2s6ok.cloudfront.net/shrine_store/uploads/networks/3057/communication_news/11454232/compressed-cbe8d7ef02a736d3dce5cb949c6453fc.webp\" alt=\"\" width=\"134\" height=\"134\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFred Hebert\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStaff SRE, Honeycomb\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3b:T35fc,"])</script><script>self.__next_f.push([1,"\u003cp dir=\"ltr\"\u003eConsider the following scenario: A set of automated tasks has somehow failed to run to completion. Because a thorough fix will take some time and the tasks you need run are time-sensitive, you complete the work manually to keep things moving forward. If this sounds familiar, then you may have helped provide \u003cem\u003ecompensation\u003c/em\u003e for that system. Compensation is a very interesting mechanism in software systems because it can keep complex systems alive, but also because it can be a factor in how they quickly and unexpectedly collapse.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eThere’s a tangling of closely related concepts in describing how complex systems collapse. Many of these lead to cascading failures, where a failure in one part of the system propagates to another one or to many others, which further causes other subsystems to fail.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003ePast figuring out how individual failures happen, there are ways in which the system fragilizes itself, such as increasing coupling between parts by \u003ca href=\"https://ferd.ca/notes/paper-going-solid.html\" rel=\"nofollow noreferrer noopener\" target=\"_blank\"\u003egoing solid\u003c/a\u003e. A more subtle way coupling can happen is through compensation, which hides that anything might even be going wrong.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eIn this post, we’ll take a look at what compensation means, and then how decompensation can rapidly happen and lead to a cascade of failures, and why this is relevant to Resilience Engineering.\u003c/p\u003e\n\u003ch3\u003eCompensation\u003c/h3\u003e\n\u003cp dir=\"ltr\"\u003eA basic rule of complex systems is that \u003ca href=\"https://how.complexsystems.fail/#5\" rel=\"nofollow noreferrer noopener\" target=\"_blank\"\u003ethey always run in a degraded mode\u003c/a\u003e, but they continue to function because the system has ways to deal with some of these situations. Degradation can take multiple forms. A partial loss of capacity or temporary overload can be handled by tradeoffs between efficiency and thoroughness (see the \u003ca href=\"https://erikhollnagel.com/ideas/etto-principle/\" rel=\"nofollow noreferrer noopener\" target=\"_blank\"\u003eETTO principle\u003c/a\u003e), or through structural elements like redundancy and building spare capacity, for example. A more subtle coping mechanism relies on adaptive patterns of compensation, a term borrowed from biology that indicates the mechanism through which an organism finds new ways to perform a capacity that has been lost.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eCompensation is rarely seen in software as we often think of it, e.g. code running without humans, because software tends to be designed such that everything does the job it has been designed to do, and little else. Instead, compensation in this context, as an adaptive mechanism, is most likely to be observed in the human part of a sociotechnical system.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eExamples of humans compensating for software faults or flaws could include:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp dir=\"ltr\"\u003eIn provisioning new enterprise accounts, the system will automatically allocate sufficient resources for the contract. However, because new users tend to do large migration jobs when setting up their environment, the customer success team makes a habit of manually overriding parameters to grant more capacity for the first weeks of use, before returning it to normal once usage is steady.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"ltr\"\u003eDuring an outage where hardware capacity is limited, a team will decide to run their own services hotter by tweaking their config to do more work on fewer devices or on older hardware (possibly slightly hurting their own tail latencies in the process) in order to leave more room for other teams’ more critical services whose lack of resources would have a worse impact on users.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"ltr\"\u003eA cache is added in front of a database to speed up responses, and limit the impact of slow queries. As the database becomes reliant on the cache to protect it, teams gradually build utilities and ad-hoc processes to backfill and prewarm the cache in case of issues, rather than spending time on the more challenging task of optimizing queries or re-thinking their product design.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"ltr\"\u003eLikewise, people can and will compensate \u003cem\u003efor each other\u003c/em\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp dir=\"ltr\"\u003eAn on-call worker has been on an incident at night, and as they are recovering or exhausted, someone else who is not currently on-call intercepts and fields alerts instead, just to make sure their colleague is not woken up.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"ltr\"\u003eDesigners are unavailable, but a graphical bug has been ticketed. You decide to fix it to the best of your ability by looking at similar elements in your product despite not having design as a requirement or expectation of your role.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"ltr\"\u003eThe platform team is swamped with work to support other internal customers. But deadline pressure in the product team cannot wait for the platform team to become available. So the product team spins up cloud services on their own to work around the perceived bottleneck of the platform team.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"ltr\"\u003eYou’ll note that all these examples of compensation require doing things such as a unit bending and unofficially extending their definition of work, using their own spare capacity to help or cover for another part of the system that is under stress, or doing a mix of both to provide capabilities that are desirable but currently not part of the nominal function. \u003cstrong\u003eCompensation can be temporary, or, over time, become a permanent fixture of how the system works.\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003eDecompensation\u003c/h2\u003e\n\u003cp dir=\"ltr\"\u003eCompensatory mechanisms are often called on so gradually that your average observer wouldn't even know it's taking place. Systems (or organisms) that appear absolutely healthy one day collapse, and that is how we discover they were overextended for a long while.\u0026nbsp;\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eTo understand decompensation, let’s go back to biological systems, with an example of congestive heart failure provided by\u0026nbsp; Dr. Richard I. Cook in a group discussion a few years ago.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eEffects of heart damage accumulate gradually over the years—partly just by aging—and can be offset by compensatory mechanisms in the human body. As the heart becomes weaker and pumps less blood with each beat, adjustments manage to keep the overall flow constant over time. This can be done by increasing the heart rate using complex neural and hormonal signaling.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eOther processes can be added to this: kidneys faced with lower blood pressure and flow can reduce how much urine they create to keep more fluid in the circulatory system, which increases cardiac filling pressure, which stretches the heart further before each beat, which adds to the stroke volume. Multiple pathways of this kind exist through the body, and they can maintain or optimize cardiac performance.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eHowever, each of these compensatory mechanisms has other, less desirable consequences. The heart remains damaged and they offset it, but the organism remains unable to generate greater cardiac output, such as would be required during exercise. You would therefore see \"normal\" cardiac performance at rest, with little ability to deal with increased demand. If the damage is gradual enough, the organism will adjust its behavior to maintain compensation: you will walk slower, take breaks while climbing stairs, and will just generally avoid situations that strain your body. This may be done without awareness of the decreased capacity of the system, and you may even resist acknowledging that you ever slowed down.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cstrong\u003eDecompensation happens when all the compensatory mechanisms no longer prevent a downward spiral\u003c/strong\u003e. If the heart can't maintain its output anymore, other organs (most often the kidneys) start failing. A failing organ can't overextend itself to help the heart; what was a stable negative feedback loop (slowing down some adverse effect) becomes a positive feedback loop (accelerating and amplifying things), which quickly leads to collapse and death.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eSomeone with a compensated congestive heart failure appears externally well and stable. They have gradually adjusted their habits to cope with their limited capacity as their heart weakened through life. However, looking well and healthy can hide how precarious of a position the person is in. Someone in their late sixties skipping their heart medication for a few days or ignoring their low-salt diet could be enough to tip the scales into decompensation.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cstrong\u003eDecompensation usually doesn’t happen because compensation mechanisms fail, but because their range is exhausted.\u003c/strong\u003e A system that is compensating looks fine until it doesn’t. That's when failures may cascade and major breakdowns occur.\u003c/p\u003e\n\u003ch2\u003eDecompensation and Resilience in Software\u003c/h2\u003e\n\u003cp dir=\"ltr\"\u003eA classic case of decompensation in software systems is introducing queues to handle temporary overload in front of services with fixed capacity. However, as the lulls that are used to catch up become rarer with increased usage, the software system reaches \u003ca href=\"https://surfingcomplexity.blog/2025/12/23/saturation-waymo-edition/\" rel=\"nofollow noreferrer noopener\" target=\"_blank\"\u003esaturation\u003c/a\u003e. People might try to compensate by increasing the queue size or increasing job timeouts, but as delays get worse, tasks get re-enqueued by clients, which further saturates the queue; at this point the ability to delay jobs is no longer effective at controlling load, and the accelerating feedback loop tells us the system is on its way to collapse.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eFrom that point on, we may encounter a cascading failure. Some \u003cem\u003etipping point\u003c/em\u003e is met, and one failure mode grows and propagates throughout the rest of the system. While cascading failures are fascinating and worth studying on their own, they can be caused by a variety of mechanisms. Decompensation is one of them, but so are \u003ca href=\"https://www.sciencedirect.com/topics/engineering/common-mode-failure\" rel=\"nofollow noreferrer noopener\" target=\"_blank\"\u003ecommon mode failures\u003c/a\u003e—a simultaneous failure of several components due to a single shared dependency\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eDecompensation will tend to be more subtle because compensating behavior hides problems. We could think of auto-scaling reaching its limits and then killing a cluster because it falls behind on its ability to do work as a common mode failure. This could become decompensation if you discovered that whenever your system neared its scaling limits, engineers went in and made optimizations that kept squeezing the lemon further and further, trying to find ways to reduce demand.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eCommon mode failures are worth looking into because they too can cause cascades, and might reveal gaps in how we analyzed or planned our systems. While compensation and decompensation can reveal similar issues, they are of particular interest to Resilience Engineering because they are also signs of \u003cem\u003eadaptive behavior\u003c/em\u003e.\u003c/p\u003e\n\u003ch2\u003eAdaptive Behavior, Adaptation, and Resilience Engineering\u003c/h2\u003e\n\u003cp dir=\"ltr\"\u003eIt is worthwhile to make a distinction between compensation, which is a type of adaptive process, and adaptation writ large. See compensation as coping with ongoing pressures or degradations (even if they never end!), whereas successful adaptation could be thought of as reaching new, more optimal operating points. Think of how someone driving a manual car with worn out brakes (or who fears their brakes might overheat) might use \u003ca href=\"https://en.wikipedia.org/wiki/Engine_braking\" rel=\"nofollow noreferrer noopener\" target=\"_blank\"\u003eengine braking\u003c/a\u003e to slow the car down when going downhill; this is very different from \u003ca href=\"https://en.wikipedia.org/wiki/Regenerative_braking\" rel=\"nofollow noreferrer noopener\" target=\"_blank\"\u003eregenerative braking\u003c/a\u003e in electric or hybrid vehicles, which is a designed feature.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eThe reason this distinction is useful is that while compensation has people work outside the design envelope, finding this behavior is often key to \u003cem\u003eredefining\u003c/em\u003e the design envelope. Successful adaptation requires harnessing adaptive behaviors \u003cem\u003eand\u003c/em\u003e reworking the system.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eThis fundamentally requires you to study \u003ca href=\"https://humanisticsystems.com/2016/12/05/the-varieties-of-human-work/\" rel=\"nofollow noreferrer noopener\" target=\"_blank\"\u003ework-as-done\u003c/a\u003e (a topic for another blog post to come) to understand where people act with reciprocity, sacrificing bits of their performance to help the overall system, where they bend rules and definitions to provide success. In places where this is a habit, you will find both strength in how they adapt, and brittleness in the risk of becoming dependent on this hidden work without knowing about it, and exhausting all such capacity in the future.\u0026nbsp;\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003ePart of Resilience Engineering in practice is knowing this happens, that it is necessary to deal with the unexpected, and knowing how to both protect, foster, and build on this type of capacity to keep systems successful.\u003c/p\u003e\n\u003ch3\u003eFurther Reading\u003c/h3\u003e\n\u003cp dir=\"ltr\"\u003eIf you’d like to learn more about decompensation, along with two other contributors to complex system failures, I recommend starting with \u003ca href=\"https://www.researchgate.net/publication/284324002_Basic_patterns_in_how_adaptive_systems_fail\" rel=\"nofollow noreferrer noopener\" target=\"_blank\"\u003eBasic Patterns in How Adaptive Systems Fail\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"https://d21hwc2yj2s6ok.cloudfront.net/shrine_store/uploads/networks/3057/communication_news/11454232/compressed-cbe8d7ef02a736d3dce5cb949c6453fc.webp\" style=\"max-width:100%\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFred Hebert\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStaff SRE, Honeycomb\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3c:T33a8,"])</script><script>self.__next_f.push([1,"\u003cp dir=\"ltr\"\u003e\u003cem\u003eAuthor’s note: I need to credit my friend and former colleague Tim Nicholas for his review and contribution to this article and the insights within it. Tim has been pivotal in shaping how I understand failures in complex systems and Resilience Engineering, and how I incorporate this into my overall approach as an engineering leader. (Also, the spelling is New Zealand English.)\u003c/em\u003e \u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eSo you’re an Engineering Manager or a Director, or perhaps a Staff Engineer. You’ve discovered the world of Resilience Engineering and had some pretty significant \"a ha\" moments. Now you want to bring Resilience Engineering into your organisation, and set the world to rights. \u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eI’ve been exactly where you are right now, and I’m going to suggest you hold your horses, and let me share my insights and recommendations from my experience as an Engineering Leader working to make Resilience Engineering a reality. There’s pitfalls on this journey that I can help you avoid.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eAs an SRE (or adjacent) leader, you will find yourself in a challenging position when it comes to Resilience Engineering. You’re the layer in the middle. You're stuck between the practitioners, who are passionate but may not have good insight into the realities and challenges of leaders, and the executive layers, who have different goals and concerns and don’t have the motivation to deviate from things they think are working. \u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eMost people working in tech and IT, no matter the type of company or organisation, will be familiar with various practices around incidents. A lot of these are summed up in regular incident reporting. Often produced monthly or existing in a dashboard, this reporting includes data and metrics such as MTTR, incident counts by severity and root cause type, and compliance with completing post incident review (PIR) action items. \u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eThese are often non-negotiable—the default standards and expectations that have permeated deeply and widely throughout our industry. In many cases, Boards and Executives expect to see these reports as indicators of risk and operational effectiveness, to ensure they fulfill their corporate governance obligations. Engineering Leaders and Managers simultaneously use them as a purported lens to measure team and engineering system performance. Producing a Root Cause Analysis (RCA) is a deeply embedded expectation of both leadership and customers following a high severity incident, as is tracking completion of PIR action items. You can’t explore a monitoring or incident response tool without being sold the ability to drive down your MTTR. \u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eHowever, these are all things that those of us in the Resilience in Software community know and accept to be not just misleading and outdated, but also counterproductive and potentially harmful to the actual improvement of safety in an organisation.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eThat leaves those of us in Resilience Engineering with quite the dilemma. Do we speak out against these deeply embedded practices and expectations and seek to explicitly educate our colleagues and leaders, or is it all a necessary evil in making space for resilience work? \u003c/p\u003e\n\u003ch2 dir=\"ltr\"\u003eThe Paradox\u003c/h2\u003e\n\u003cp dir=\"ltr\"\u003eThe fact is, we are faced with a paradox. A contradiction that reveals deeper truths and a reality that is difficult to reconcile. \u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eFor an engineering leader to build trust and credibility—to be perceived as competent—they need to meet or exceed the expectations of leaders and executives by carrying out these default standards and practices. You believe this to be contrary to your goals, however my experience tells me you should do these things anyway in pursuit of bigger picture resilience and adaptive capacity. \u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eBefore you get upset and stop reading, hear me out. I’ve been the leadership layer in the middle, sandwiched between the practitioners who really understand Resilience Engineering and see the need for it every day, and the executives with different concerns, goals and motivations. \u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eMy goal when it comes to Resilience Engineering is to legitimise the work, the practices and the concepts. I want to make space for engineers to conduct incident analysis to better understand our systems and the conditions that enable incidents to occur. I also want to build understanding and acknowledgement of complexity and Resilience Engineering in leadership to enable better awareness and decision making. \u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eVery specifically, my goals no longer include the death of root cause analysis, incident metrics, MTTR and PIR action tracking. \u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eTelling executive leadership that incident counts, MTTR, and root cause analysis are wrong/incorrect/misleading gets you nowhere. I can confidently say that when an executive leader wants to be talking about quality of service for your customers, the last thing they want to hear about is academic papers and Monte Carlo simulations. This narrative is harming our ability to do resilience work, and undermines the credibility of our ideas, our practices and our movement. It is very difficult to educate people against their will, especially if their view of competence on a subject is different to your own. We must make Resilience Engineering appealing and useful to executives if we are to get their support and buy in.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eBoard and Executive pressure around incidents and the need to present something that is viewed as tangible and sound should never be underestimated. Once you are in the pressure cooker of “too many incidents” or “poor reliability,” incident data and reports can give leaders the tools to \u003cstrong\u003efeel\u003c/strong\u003e in control but also demonstrate that they are in control. Many of the traditional incident practices and metrics help leaders communicate that they are controlling things, and also creates opportunities for them to exert control on what people do—even when they don't have control over the actual system or system outcomes. \u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eThe closer you are to the sharp end—the point where the most direct and often challenging work of a particular activity takes place—the more apparent it is that uncertainty exists. Many of the above-mentioned default standards and expectations around incidents are designed to help manage and control uncertainty. However, Resilience Engineering is essentially about embracing uncertainty as an unavoidable reality of complex systems. As a result we tend to want everyone to accept uncertainty as unavoidable. In the Boardroom, far removed from the sharp end, the perspective is very different. Uncertainty is something that should be managed and reduced. It can become a risk with acceptable probability, but it can't be unmanaged. From this different perspective, the system and its outcomes must be controlled, or have the illusion of being controlled, even if we know this to be at odds with the system’s reality. \u003c/p\u003e\n\u003ch2 dir=\"ltr\"\u003eMy Recommendations\u003c/h2\u003e\n\u003cp dir=\"ltr\"\u003eYou need to play the game that others think they are playing. As an engineering leader, you only have so much social capital and so many cards to play in what you can change and influence. You want these cards to be used for your agenda, and you want to make meaningful progress. This might be influencing for additional headcount or prioritising a particular piece of system improvement work. I don’t think those limited cards should be used for debating concepts like MTTR and incident counts. \u003c/p\u003e\n\u003ch3 dir=\"ltr\"\u003eGive Them What They’re Asking For\u003c/h3\u003e\n\u003cp dir=\"ltr\"\u003eIf leaders and executives are asking, then continue to produce that incident data and show those PIR actions. If you’re not doing it then someone else will, and it’s much better to be in control of the data and the narrative. Position yourself and your SRE leaders as credible, in control and taking action, even if it’s not by your definition. If your leaders aren’t currently asking for typical incident reports and metrics, I strongly suggest you have the data on hand to be in a position to provide it should you be asked. You need your perceived competence and resilience work to ride the wave of leadership change and organisational evolution. Even if you succeed with one set of leaders in building their understanding that these are not the metrics or approaches they need, leaders come and go. If you are perceived as too far from industry norms you will have a hard time establishing trust and credibility with a new set of leaders who haven't been on the same journey. \u003c/p\u003e\n\u003ch3 dir=\"ltr\"\u003eProceed with Caution When Educating and Influencing\u003c/h3\u003e\n\u003cp dir=\"ltr\"\u003eRather than seeking to educate directly in a way that can be perceived as contrary, model the Resilience Engineering concepts, language and practices you want to see others adopt. Once your stakeholders become familiar with the concepts through a more implicit approach, you’ll find them more receptive to being introduced to new ways of understanding reliability and resilience, and different types of recommendations from their teams as a result. \u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eEducating and influencing leadership on Resilience Engineering is not without risks, so I recommend that you proceed with caution. This approach assumes leaders have the willingness to learn and the conceptual grounding to make informed decisions. In reality, this is rarely the case. When it comes to incidents, leaders tend to believe they know how things should be done and are resistant to alternative approaches and perspectives. Even if you do manage to succeed in educating leadership, the outcome is uncertain. At best they gain only a surface-level grasp of new concepts, at worst they misinterpret and misapply what they’ve learned, causing further harm. \u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eA more effective strategy is to empower people closer to the work—those who have done the analysis and understand the tradeoffs—to provide recommendations for executive action. These recommendations should narrow the range of options and enable leadership to make decisions confidently. These recommendations are more likely to be accepted if they are backed by the credibility and trust gained through giving leaders what they’re asking for. \u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eAnecdotally, attempting to influence leadership towards more of our Resilience Engineering methods and practices has a notable rate of burnout for SRE leaders and senior individual contributors. I suspect the phenomenon we are currently experiencing in this regard is part of some broader industry cycle due to factors outside of our control. Either way, we need our perceived competence and resilience work to ride out the wave of this industry cycle, just as we need to ride out the wave of leadership change and organisational evolution. And we need to be able to do that with our mental and emotional wellbeing intact. \u003c/p\u003e\n\u003ch3 dir=\"ltr\"\u003eAcknowledge and Negotiate the Paradox\u003c/h3\u003e\n\u003cp dir=\"ltr\"\u003eSo what does this look like in practice? Think of it like putting spinach in a kid’s smoothie—give them what they think they want, and include the good, healthy stuff they also need in the process. If someone states human error as a cause for an incident, you can mention that this tends to be an indicator of underlying systemic challenges worth taking a closer look at. If you can, offer to provide SRE resources to conduct a more useful PIR. If leaders are getting caught up on incident counts and MTTR, you can use SLOs to provide a more meaningful representation of the quality of the customer experience, while providing context in the form of themes and patterns relevant to your audience. \u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eIt’s here in these conversations, with the background of outputs and metrics you’re not enthusiastic about, that you’re best placed to use those valuable cards to make recommendations based on insights from incident learning and resilience work. Whether it be highlighting systematic challenges and areas of underinvestment or making recommendations to support better prioritisation and improving the engineered system.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eIn all of this, you are constantly negotiating the paradox we face in Resilience Engineering. \u003cstrong\u003eThese opposing perspectives and methods will always coexist\u003c/strong\u003e. One might be more dominant than the other at certain points in time, but there will always be that push and pull. You need to be ready for it. By building the perception of competence and playing along with short term optics, you can pursue the bigger picture of Resilience Engineering. \u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eI still hope that one day we will build broad industry recognition for Resilience Engineering concepts and practices, while outdated methods and metrics fade off into the sunset. Until that day comes, we play the game while working to change the rules.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e \u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cstrong\u003e\u003cimg src=\"/storage/3784353/download\" alt=\"\" width=\"148\" height=\"111\"\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cstrong\u003eMichelle Casey\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eSRE/Engineering Leader \u003c/p\u003e\n\u003cp\u003e \u003c/p\u003e"])</script><script>self.__next_f.push([1,"3d:T5eb2,"])</script><script>self.__next_f.push([1,"\u003cp dir=\"ltr\"\u003eResilience engineering views incidents through a different frame than the conventional approach in the software industry, which tends to treat incidents as an irritating interruption to our work. The unrelenting pressure of the roadmap hangs over retrospectives. The clock is always ticking. We focus on the quickest fixes that might prevent the failure in the future so we can “get back to work.” To apply resilience engineering in the software business we must recognize that \u003cem\u003eincidents are part of everyday work in complex systems\u003c/em\u003e, not merely interruptions. In fact, incidents are a recurring opportunity for learning and discovery.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eThere are two features of (almost) all incidents that usually go unnoticed or ignored: expertise and overload. Both of these are pervasive in incidents, yet also elusive.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eExpertise by its very nature conceals the difficulty of incident response work. It is deep, well-practiced know-how, judgement, and experience for effective problem-solving and effective coordination under pressure. Similarly, overload is so pervasive that people adapt to it without even noticing. The cognitive capacity of incident responders can be exhausted by the demands on attention and communication, contributing to distraction, impaired decision-making, delayed actions, and increased errors or stress.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eIn order to draw these elusive and important things into the open, I’m going to dig into a short example from a particularly difficult moment in an incident drill in which two experienced incident commanders deftly managed the overload. The high pressure of this moment is a perfect laboratory to examine these subtle and elusive features of both expertise and overload.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eConsider this section of the video transcript from the incident drill. Let's get a first impression without commentary. (I’m deliberately not expanding the acronyms yet, bear with me.) Sarah is leading the response and Alex is her deputy.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003ciframe class=\"embedly-embed\" style=\"aspect-ratio: 640 / 480; max-width: 100%;\" title=\"YouTube embed\" src=\"https://cdn.embedly.com/widgets/media.html?src=https%3A%2F%2Fwww.youtube.com%2Fembed%2FgCL_Hc0GCFU%3Ffeature%3Doembed\u0026amp;display_name=YouTube\u0026amp;url=https%3A%2F%2Fwww.youtube.com%2Fwatch%3Fv%3DgCL_Hc0GCFU\u0026amp;image=https%3A%2F%2Fi.ytimg.com%2Fvi%2FgCL_Hc0GCFU%2Fhqdefault.jpg\u0026amp;type=text%2Fhtml\u0026amp;schema=youtube-nocookie\" width=\"640\" height=\"480\" frameborder=\"0\" scrolling=\"no\" allowfullscreen=\"allowfullscreen\"\u003e\u003c/iframe\u003e\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eA few minutes later, Sarah is managing a few other threads of investigation.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\u0026nbsp;\u003ciframe class=\"embedly-embed\" style=\"aspect-ratio: 640 / 480; max-width: 100%;\" title=\"YouTube embed\" src=\"https://cdn.embedly.com/widgets/media.html?src=https%3A%2F%2Fwww.youtube.com%2Fembed%2FjX8maUosprc%3Ffeature%3Doembed%26modestbranding%3D1%26rel%3D0\u0026amp;display_name=YouTube\u0026amp;url=https%3A%2F%2Fwww.youtube.com%2Fwatch%3Fv%3DjX8maUosprc\u0026amp;image=https%3A%2F%2Fi.ytimg.com%2Fvi%2FjX8maUosprc%2Fhqdefault.jpg\u0026amp;type=text%2Fhtml\u0026amp;schema=youtube\" width=\"640\" height=\"480\" frameborder=\"0\" scrolling=\"no\" allowfullscreen=\"allowfullscreen\"\u003e\u003c/iframe\u003e\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eThese exchanges happen quickly and casually. Why bother pointing to this at all? There is a lot to learn about both expertise and overload hiding between the lines of this dialog. Let's dig in.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eFirst off, Sarah's claim during that exchange turns out to be a misunderstanding of Hamed's role. In my interviews with her she reported feeling embarrassed to have gotten this wrong during the drill. It is frequently the case that the expert must make effective decisions even with incomplete and inaccurate understanding. It is also notable that despite her misunderstanding of Hamed’s specific role, she correctly assessed that Tanya has deeper technical understanding than Hamed. This is a signal to me that Sarah also has expertise in quickly recognizing and contrasting the expertise of others.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eNext up, the acronyms BCP and CAN. They are useful glimpses of how expertise conceals the difficulty of work.\u0026nbsp;Do you know these terms? I didn’t know either of them. Sarah and Alex are fluent in a shared vocabulary. For them, BCP is a \u003cem\u003ebusiness continuity plan\u003c/em\u003e and CAN is a \u003cem\u003econditions-actions-needs\u003c/em\u003e report. Does the expansion of the acronyms expand your understanding of the underlying complexity? This is the very point of acronyms. They encapsulate complex ideas. Experts use them to communicate efficiently, especially under pressure.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eMany readers will have at least heard of \u003ca href=\"https://en.wikipedia.org/wiki/Business_continuity_planning\" rel=\"nofollow noreferrer noopener\"\u003ebusiness continuity planning\u003c/a\u003e and related ideas of disaster recovery. Fewer readers will know that conditions-actions-needs reports are a communication framework for reporting progress during an incident. Both of these are deep rabbit holes that don’t add enough to this discussion, so I will avoid them for brevity.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eAs an observer who is unfamiliar with the jargon, these terms are opaque. Sarah and Alex know what they’re talking about, but the underlying complexity of this situation is concealed for the uninformed observer. Their fluency with shared jargon conceals the complexity they are tackling. There’s another subtlety on this point. Imagine someone who fluently understands BCP but has never heard of CAN. They would feel the elevated sense of urgency knowing that whatever else is going on, Sarah thinks they may need to engage the business continuity plan. They might view the delay in Alex’s response as lacking an appropriate level of urgency.\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eAt this point in the incident, Sarah has previously asked four different times in Slack to learn more about the BCP. The only answer has been a link to the document. These are subtle clues of her expertise in recognizing and managing overload.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eTake note of what she is doing and what she is \u003cem\u003enot\u003c/em\u003e doing. She isn’t reading the document herself. This hints that she is adapting to overload. A less experienced person might adapt differently, trying to still do all the things in front of them but \u003cem\u003ereducing thoroughness\u003c/em\u003e on each. Another option would be to just drop it—she could shed load by ignoring the BCP. I can tell that she hasn’t just dropped the work because she keeps asking for more details. And each request for information is itself an attempt to recruit resources to help cope with the overload. Let’s look at how she recruits Alex to do this work.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eSarah's expertise with overload goes beyond understanding her own limits. Even while she delegates the reading to Alex, she demonstrates awareness that he may also be overloaded.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eKnowing the BCP will demand careful attention, she asks about his capacity (“\u003cem\u003edo you have bandwidth\u003c/em\u003e…”) even before she makes the specific request for help (“...\u003cem\u003eto read it\u003c/em\u003e?”).\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eWhat's more, we see a moment of investing in \u003cem\u003ecommon ground \u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e with Alex, and reducing his cognitive work for this task. She shares her screen to show exactly which document. This gesture is loaded with insight about how expertise tackles overload:\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eI now know that she’s opened the doc even if she hasn’t read it. Moreover, she’s keeping track of it to be able to quickly pull it into view. This is an example of \u003cem\u003etime-shifting\u003c/em\u003e work. She’s got the document close to hand when delegating to Alex. Showing the doc reduces the scope of work for Alex by priming his perception to recognize the document. Alex won't have to spend any extra energy wondering if he is looking at the correct document. It also subtly signals the importance of the request.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eSarah's demonstration of her expertise at managing overload and maintaining common ground happens in the space of a single breath.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cstrong\u003eExpertise hides what makes work difficult.\u003c/strong\u003e\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eBecause overload is pervasive in incidents, experienced responders must develop tacit expertise in managing overload. But because that expertise is tacit, they won't even notice the subtle ways they help everyone around them cope with overload.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eIf you have ever had the experience of relief when one of your best responders shows up, you have felt the tacit knowledge. You don't know why, but somehow incidents just go better when they are in the room. And maybe the main thing you have learned is to call for their help if things get bad.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eBy contrast, this example features two responders who know overload and are explicitly familiar with a pattern of four responses to overload (for more on this, see the companion article on the \u003ca href=\"https://resilienceinsoftware.org/news/11469953\" rel=\"nofollow noreferrer noopener\"\u003eFour Responses to Overload\u003c/a\u003e). When I interviewed them individually about their experience in the drill, each brought up this pattern on their own without any prompting from me. Given that the pattern was that front-of-mind for them, when they participate in incident retros, they almost certainly bring these topics up for discussion and deliberately spread the explicit knowledge in their respective organizations.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eWhat’s more, we can see Sarah repeatedly choose the two more strategic adaptations to her own overload: recruiting resources and time-shifting work. Unless the observer knows to look for it, and knows how to recognize it, this expertise in managing overload is invisible.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eWith similar expertise, Alex responds to Sarah’s request by time-shifting work:\u0026nbsp;“\u003cem\u003eYeah, once I get this CAN out then I'll read it. I'm almost done\u003c/em\u003e.”\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eHe responds, signalling he understands the importance of the request. He adapts to his own overload by momentarily deferring this request. He also explicitly states what he has prioritized instead—this is extra energy Alex spends to maintain common ground with Sarah. This provides the briefest update on her previous request for the CAN, and also offers her a chance to override that prioritization decision.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eOnce again, this display of expertise happens in the space of a single breath. Sarah’s short reply (“\u003cem\u003eAwesome. Thank you\u003c/em\u003e.”) serves to close this short conversation—implicitly validating Alex’s decision to finish the CAN first.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eThere are more examples of overload and expertise in the next exchange.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e...\u003cem\u003ebusily typing and voicing a complicated group of questions\u003c/em\u003e...\u003cbr\u003e\u003cstrong\u003eSarah\u003c/strong\u003e: Oh man it takes me so long to type.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cstrong\u003eAlex\u003c/strong\u003e: There are some execution steps for you in the BCP for failing over.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cstrong\u003eSarah\u003c/strong\u003e: Hold just a second while I get these... uh...\u003cbr\u003e...\u003cem\u003ethis train of thought trails off as she switches to voice what she's typing\u003c/em\u003e...\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eIn the video recording we can see and hear many ways Sarah tries to phrase the questions. Alex could also see and hear her working. He nevertheless chooses to interrupt, increasing Sarah's cognitive load. But he also gives just enough indication to justify the interruption: this is about the BCP.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eSarah, explicitly time-shifts work again (“\u003cem\u003eHold just a second\u003c/em\u003e.”). She also tacitly sheds load (...\u003cem\u003ethis train of thought trails off\u003c/em\u003e...). This moment is the first we’ve seen of Sarah shedding load.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eThis example of shedding load is particularly instructive. We frequently adapt to overload without any conscious decision. Under these overloading demands, Sarah focuses attention on the most important or most urgent work—in this case, typing the questions, voicing them, and explicitly targeting them at specific responders.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eNotice again how expertise hides the difficulty of the work. Some of the work disappears completely. Sarah normally does extra work to provide context of her thinking. That extra work just drops as her voice tracks exactly what she's typing. The absence of that work is only apparent by contrasting what \"normal\" sounds like for her under less intense circumstances.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eLet’s look at one more observation of expertise with managing overload in the complicated group of questions Sarah typed into Slack:\u0026nbsp;\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cem\u003eCan we move part of the traffic? Or is it all or nothing\u003c/em\u003e?\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003eAre there servers in the area of the DC we can shut down\u003c/em\u003e? \u003cem\u003eIs our UAT there and able to be shut down for instance?\u003c/em\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cem\u003eCan the DC vendor give us an ETA? Is there anything else they can do to cool the room they are not already doing\u003c/em\u003e? \u003cem\u003eAre we the only tenant there\u003c/em\u003e?\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp dir=\"ltr\"\u003eWhat we learn here is that the model of four responses to overload applies to machines just as it applies to people. Her first question (“\u003cem\u003eCan we move part of the traffic\u003c/em\u003e?”) is recruiting resources in the form of an alternate data center.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eHer second question (“\u003cem\u003eAre there servers ... we can shut down\u003c/em\u003e?”) is working the same problem from the opposite direction. It would shed load on the network and also remove heat-generating computation from the data center.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eHer third question (“\u003cem\u003eAnything else they can do to cool the room\u003c/em\u003e?”) is also recruiting resources.\u0026nbsp;She also explicitly directs those questions to specific responders. Each question is demanding on its own and Sarah is spreading that load across the team.\u0026nbsp;Notice how she is managing overload at many levels simultaneously: her own, the incident response team, and the machines and resources related to the data center.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eFinally, she is able to turn her attention back to Alex (“\u003cem\u003eAlex, sorry, I put you in a buffer. What was that about the BCP\u003c/em\u003e?”). There is a subtle signalling that Alex had interrupted skillfully. Sarah shows her understanding that he has the details about the BCP and that there is work in that plan that she must execute.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eIt is again notable what we do not see here. Whatever else Alex has been doing, he’s managed his own attention and overload to be able to respond to Sarah at this point. Unlike the earlier exchange when he was working on the CAN and deferred her request, here he immediately engages by reading the six steps aloud.\u003c/p\u003e\n\u003ch1 dir=\"ltr\"\u003ePutting Overload \u0026amp; Expertise Insights Into Action\u003c/h1\u003e\n\u003cp dir=\"ltr\"\u003eThis part of the incident drill illuminates three things that are difficult to even perceive: expertise, overload, and explicit expertise about overload. Instead of observing overload and expertise directly (which is often difficult or impossible), we can infer their presence by looking at what is observable: the adaptations themselves.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eOverload is pervasive. Knowing this model of four responses to overload can help us to recognize its presence, to anticipate ways overload can cascade from one part of the system to another, and to choose better adaptations when we know there’s overload.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eIf you suspect you have hidden overload, look for any of the four adaptations. If you see time-shifting work or recruiting resources, you can infer that responders are anticipating the risk of overload and may be adequately managing it. Shedding load and reducing thoroughness suggest acute overload that deserves more aggressive intervention. For example, resources that have been recruited to support local overload do not come for free. Ensure whatever tradeoffs that are paying for the additional resources are also under consideration. If there are many tasks that are getting time-shifted, there may be hidden costs of context-switching to mitigate those.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eIf you already know overload is present, then review the four adaptations to weigh your options for managing it. If the overload is an immediate threat, focus on ways to shed load or reduce thoroughness. If you feel some breathing room, look around for resources you could recruit or find ways to time-shift the work.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eExpertise can be even more elusive than overload. It is certainly much more difficult to categorize. So much expertise develops tacitly that experts themselves have a difficult time explaining how they do what they do. Although I have shown many examples of how subtle and invisible expertise is, I have not offered any specific advice about how to recognize expert performance in the wild. There are learnable skills for discovering this expertise but we will tackle those in a different article.\u003c/p\u003e\n\u003ch1 dir=\"ltr\"\u003eLearning in Addition to Fixing\u003c/h1\u003e\n\u003cp dir=\"ltr\"\u003eI’ve made another subtle move in this article by featuring an incident drill. Drills by their nature focus attention on how the incident responders do their work, instead of focusing on what needs to be fixed in the code or documentation, or monitoring. Humans develop skills and expertise through practice and experience. Conventional retrospectives focused only on the fixing miss out on all the places we could be sharing expertise across the organization or improving the work of running incidents.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eRemember our resilience engineering frame: incidents are first-class work. We can gain a competitive advantage by rewarding ongoing professional development in incident response. Through this know-how we can continually earn the trust of our customers.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eOne cool trick you can now apply to all of your future retrospectives is to ask how people adapted to overload in this incident. These patterns of adaptations to overload happen at a wide range of tempos and scales. Your teams and your services have unique expressions of overload and associated adaptations. Incidents provide an opportunity to look closely at those specific adaptations to specific overload and where specific circumstances exceeded the capacity of the system to adapt. The general pattern is useful, but your people need the grungy local details about how overload shows up in your world. When reflecting on incidents after the fact, these observations can reveal improvements to the operational practices of your teams or the operational tooling of the services in question.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eSharing the model of four responses to overload across your teams, and regularly discovering how people apply that model in real incidents is a powerful driver to turn incident retros into a community of practice with ongoing transfer of expertise and know-how. In this way, you can create leverage from the pervasive combination of expertise, and overload in incidents and spread the more specific expertise about overload.\u003c/p\u003e\n\u003ch1 dir=\"ltr\"\u003eFurther Reading\u003c/h1\u003e\n\u003cp dir=\"ltr\"\u003eInterested readers can learn a lot more in \u003ca href=\"https://www.researchgate.net/publication/333091997_Approaching_Overload_Diagnosis_and_Response_to_Anomalies_in_Complex_and_Automated_Production_Software_Systems\" rel=\"nofollow noreferrer noopener\"\u003eApproaching Overload: Diagnosis and Response to Anomalies in Complex and Automated Production Software Systems\u003c/a\u003e by Marisa Bigelow. There are four case studies of different software and network incidents with thoughtful attention to overload. You may be surprised to notice that the responses to overload are present equally in the software services and in the people running those services. What’s more, Bigelow traces connections between the overload of the software and the overload among the responders—an example of how overload in one part of a system can cascade to other parts.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eSee also:\u0026nbsp;\u003ca href=\"https://www.researchgate.net/publication/351579618_Patterns_in_How_People_Think_and_Work_The_Importance_of_Pattern_Discovery_for_Understanding_Complex_Adaptive_Systems\" rel=\"nofollow noreferrer noopener\"\u003ePatterns in How People Think and Work: The Importance of Pattern Discovery for Understanding Complex Adaptive Systems\u003c/a\u003e. Woods, David \u0026amp; Licu, Tony \u0026amp; Leonhardt, Jörg \u0026amp; Rayo, Michael \u0026amp; Balkin, E. \u0026amp; Cioponea, Radu. (2021).\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cem\u003eThanks to \u003cstrong\u003eFred Hebert\u003c/strong\u003e, \u003cstrong\u003eGreg Holecek\u003c/strong\u003e, and \u003cstrong\u003eJames Boyd \u003c/strong\u003efor reviewing early drafts. \u003c/em\u003e\u003cstrong id=\"docs-internal-guid-0de1ecf1-7fff-3a46-2162-5cbbbdbf999c\"\u003e\u003c/strong\u003e\u003cem\u003eSpecial thanks to \u003cstrong\u003eCourtney Nash\u003c/strong\u003e, \u003cstrong\u003eSarah Butt\u003c/strong\u003e, \u003cstrong\u003eAlex Elman\u003c/strong\u003e, \u003cstrong\u003eHamed Silatani\u003c/strong\u003e and Uptime Labs. This article has been a long time coming. It started with a thematic analysis of many production incidents when I was a member of Alex’s team at a previous employer. That became a foundation for conference talks on the multi-party dilemma given by Sarah and Alex in 2023. The three of us shared the insights from the multi-party dilemma with Uptime Labs which informed the structure of the drill. Then Courtney had the brilliant idea that I should do an analysis of Sarah and Alex practicing that drill. Beth \u003cstrong\u003eAdele Long\u003c/strong\u003e’s workshops on regenerative productivity helped me strongly narrow the focus of this article to something manageable. \u003cstrong\u003eJohn Allspaw\u003c/strong\u003e clarified an important distinction between tacit expertise and the Law of Fluency—tacit expertise is why we need the many tools from Naturalistic Decision Making in order to discover expertise, whereas the Law of Fluency concerns the way experts actively adapting conceal the difficulty of their work to all the people around them. Those are tightly related, but also not the same thing. And I am particularly grateful to \u003cstrong\u003eBrian Marick\u003c/strong\u003e for probably the best demonstration of constructive feedback I’ve ever seen—especially the examples of putting specific examples first, then explaining the theory from the concrete.\u003c/em\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cp dir=\"ltr\"\u003e\u003csup\u003e1\u0026nbsp;\u003c/sup\u003e\u003cspan style=\"font-size: 12px;\"\u003eInvestigating the jargon in use between experts under pressure helps to reveal the complexity hidden in plain sight in day-to-day work. See \u003ca href=\"https://www.researchgate.net/publication/335606945_Chapter_3_Being_Bumpable_Consequences_of_Resource_Saturation_and_Near-Saturation_for_Cognitive_Demands_on_ICU_Practitioners\" rel=\"nofollow noreferrer noopener\"\u003eChapter 3 Being Bumpable: Consequences of Resource Saturation and Near-Saturation for Cognitive Demands on ICU Practitioners\u003c/a\u003e.\u003c/span\u003e\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003csup\u003e2 \u003c/sup\u003e\u003cspan style=\"font-size: 12px;\"\u003eA colloquial understanding of common ground will be good enough to let us focus on the subtleties of expertise and overload. That said, there is a deeper understanding of common ground and common grounding that I have not covered in this article but nevertheless fits perfectly in this context. Klein, Gary \u0026amp; Feltovich, Paul J. \u0026amp; Bradshaw, Jeffrey \u0026amp; Woods, David. (2005). \u003ca href=\"https://www.researchgate.net/publication/227992178_Common_Ground_and_Coordination_in_Joint_Activity\" rel=\"nofollow noreferrer noopener\"\u003eCommon Ground and Coordination in Joint Activity\u003c/a\u003e. 10.1002/0471739448, ch6.\u003c/span\u003e\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003csup\u003e3 \u003c/sup\u003e\u003cspan style=\"font-size: 12px;\"\u003eThis section heading is how Dr. Richard Cook paraphrased The Law of Fluency from Dr. David Woods: “Well”-adapted work occurs with a facility that belies the difficulty of the demands resolved and the dilemmas balanced. Woods, David \u0026amp; Hollnagel, Erik. (2006). \u003ca href=\"https://www.researchgate.net/publication/329025433_Joint_Cognitive_Systems_Patterns_in_Cognitive_Systems_Engineering\" rel=\"nofollow noreferrer noopener\"\u003eJoint Cognitive Systems: Patterns in Cognitive Systems Engineering\u003c/a\u003e. 10.1201/9781420005684. p.20.\u003c/span\u003e\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"font-size: 12px;\"\u003e\u003cimg src=\"https://resilienceinsoftware.org/storage/4400602/download\" alt=\"\" width=\"134\" height=\"134\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan style=\"font-size: 12px;\"\u003e\u003cstrong\u003eEric Dobbs\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e"])</script><script>self.__next_f.push([1,"5:[\"$\",\"$L39\",null,{\"news\":{\"id\":11454232,\"og_url\":null,\"pinned_to_feed\":false,\"pinned_to_index_page\":false,\"public\":true,\"publication_timezone\":\"America/Los_Angeles\",\"published_at\":\"2026-01-22T05:22:00Z\",\"rss_url\":null,\"seo_canonical_url\":null,\"seo_description\":null,\"seo_title\":null,\"social_media_list\":[\"linkedin\"],\"visible_on_feed\":false,\"visible_on_index_page\":true,\"type\":\"news\",\"subject\":\"Decompensation and Cascading Failures\",\"content\":\"$3a\",\"sanitized_content\":\"Consider the following scenario: A set of automated tasks has somehow failed to run to completion. 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