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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/11560646/ultra_wide-2f370f4ed36790c72299e57b2ae83da5.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">Expertise Can&#x27;t Be Automated: Why Resilience Still Needs Humans</h1><div class="Box-sc-3e41dc6a-0 ldkrga"></div></div><div class="Box-sc-3e41dc6a-0 jYXfit">Published on August 3, 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">Abby Wambach, Neil Peart, Kelsey Hightower. These people are all experts in their relevant fields. Through them we recognize expertise as an exceptionally high level of performance on a particular task or within a given domain. The universal inputs to their expertise are time and exposure (aka practice). Expertise in any domain is acquired through doing, and more specifically by continued exposure to varying conditions and inputs inherent to the given domain. The latter part is important: while someone can absolutely become an expert in a very narrow sense (e.g. solving Rubik’s cubes), and someone can also learn about a subject or domain by reading, talking, listening, watching podcasts, etc., high-performance domains exert unpredictable, variable, and dynamic demands on the expert that are only mastered by continued exposure and deliberate practice within that domain.</p>
<blockquote>
<p dir="ltr">”Experts are the people the team turns to when faced with difficult tasks.”</p>
<p dir="ltr">—Gary Klein</p>
</blockquote>
<p dir="ltr">I have recently been on a number of podcasts/webinars/panels where the question of what expert incident response looks like comes up, especially how to train for and formalize it. Lurking behind that question for some people is often the hope that in the answer lies the opportunity to productize, scale, and even automate this essential skill for modern software-based businesses. We are currently awash in AI Ops and AI SRE solutions attempting to do just this, and I’m here to convince you that expertise cannot be bottled and sold. Beyond that, I hope to persuade you that expertise is worth investing in and cultivating within your own organization.</p>
<h1 dir="ltr">Common Characteristics of Expertise</h1>
<p>It is worth noting that expertise is not simply the accrual of experience over time.&nbsp;As hinted at in the earlier quote from Gary Klein, expertise is a specific kind of knowledge, and it shares a number of things in common.</p>
<h2 dir="ltr">Expertise Is Often Invisible</h2>
<p dir="ltr">Cognitive scientists and folks in the field of Resilience Engineering refer to this as the <a href="https://www.youtube.com/watch?v=2Nc8hgEE7ig" rel="nofollow noreferrer noopener">Law of Fluency</a>. This fluency characterizes an activity that is well-adapted, such that the effort and challenges involved in conducting that work are hidden from view, making it appear smooth and effortless. Experts adapt to complex and surprising situations by filling gaps and managing challenges effectively and efficiently, and notably in ways that may not be apparent to other people. In general, fluency is considered a hallmark characteristic of expertise.</p>
<h2 dir="ltr">Expertise Is Not Easily Introspected</h2>
<p dir="ltr">In general, experts do not have direct access to the cognitive, physical, and other sources of their expertise—human performance, given the way it is acquired through experience over time, is inherently difficult to explain. This is why many people refer to this kind of skill as implicit, or subconscious. Answering the question “How did you know to do X?” for an expert often leads to a long exploration of “Well, it reminded me of this one time…” or “It seemed similar to when…” In asking someone to explain their expertise, we are effectively asking them to try to unearth every second of practice and effort they have put into acquiring their skill.</p>
<h2 dir="ltr">Expertise Enables Adaptation</h2>
<p dir="ltr">People newer to a skill or domain typically rely on documentation and more rigid and rule-based methods for performing a given task. They lack the context and experience to handle variations from standard expectations and outcomes, and tend not to experiment or improvise as much. Experts, on the other hand, demonstrate a unique ability to adapt to novel information, challenges, and surprise situations. Given the breadth and depth of their experience (and having learned by trying many various approaches and strategies over time), they are better equipped (and generally, more confident) to stretch and adjust when presented with novel situations. Think of this as the difference between being able to read sheet music well and being able to play jazz with a group you just met.&nbsp;</p>
<p dir="ltr">Consider the following <a href="https://www.researchgate.net/publication/322693359_Why_Expertise_Matters_A_Response_to_the_Challenges" rel="nofollow noreferrer noopener">list of characteristics of expertise</a> that Gary Klein and a number of other cognitive scientists have empirically demonstrated. They found that experts:</p>
<ul>
<li dir="ltr">
<p dir="ltr">Employ more effective strategies than others, and do so with less effort;</p>
</li>
<li dir="ltr">
<p dir="ltr">perceive meaning in patterns that others do not notice;</p>
</li>
<li dir="ltr">
<p dir="ltr">form rich mental models of situations to support sensemaking and anticipatory thinking;</p>
</li>
<li dir="ltr">
<p dir="ltr">have extensive and highly organized domain knowledge; and</p>
</li>
<li dir="ltr">
<p dir="ltr">are intrinsically motivated to work on hard problems that stretch their capabilities.</p>
</li>
</ul>
<p dir="ltr">If that sounds like some of the fundamental aspects of resilience, you’re on the right track.&nbsp;</p>
<h1 dir="ltr">Expertise Is a Key Ingredient for Resilience</h1>
<p dir="ltr">Given the above characteristics of expertise and how they develop, expertise can’t simply be “bottled” and subsequently scaled or reproduced en masse. As noted above, most high-performance domains are extremely variable, so one element of maintaining expertise is constantly updating mental models and factoring in new variables, challenges, and developments. Layer the nature of modern distributed software systems on top of this, and you begin to see that one cannot simply capture expert incident response and productize it.</p>
<p dir="ltr">Someone is inevitably going to say “Well we’ll just train our LLMs and agentic models on all the conditions necessary to generate expertise in incident response and then we can have that, right?” As they currently stand, LLMs and AI agents do not acquire expertise the way humans do, in part because they require human model development, training, and refinement. They also are not capable of sensemaking, reflection, coordination across fuzzy boundaries, reciprocity, vicarious learning from others’ experiences, observational learning (picking up things “in the air” from casual discussions), or “seeing the invisible” (perceiving missing vs. present cues)—these are all but a subset of the kinds of cognitive processes involved in developing and maintaining expertise that have been studied for decades by cognitive scientists.&nbsp;</p>
<p dir="ltr">AI SRE agents can currently survey a given system’s environment and find inputs and patterns to generate hypotheses about how a given situation may have arisen. They can do this over and over and over, but they do not possess the knowledge or ability to, for example, call Sarah on the database team and see if she knows why things look weird. They can’t factor in the architectural discussions the team had about the recent migration which might explain why things aren’t behaving as expected. They can't notice that the system is behaving exactly like it did six months ago before a cascading failure, because that pattern lives in someone's memory, not in a log file. They can't pick up on the fact that the on-call engineer sounds unusually uncertain on the incident call, or that the team has gone quiet in a way that usually signals something is badly wrong. These aren’t edge cases, they are routine features of how complex incidents actually unfold.</p>
<p dir="ltr">The prevailing belief in the software industry appears to be that we can use automation and AI to replace what expertise has given us in the past. However, consider the <a href="https://www.thevoid.community/report-2024" rel="nofollow noreferrer noopener">data from the 2024 VOID report</a>, wherein 75% of incidents involving automation required human intervention to comprehend, troubleshoot, and resolve the incident. That is where experts quite often save the day. Here we contend with the ouroboros of expertise and automation, in which experts don’t have as much experience with the system at hand, and then when asked to step in and resolve a problem with said system, they have found their expertise eroded by having less direct access to how it actually functions.&nbsp;</p>
<p dir="ltr">A lack of experience with systems due to increased automation and AI can lead to <a href="https://www.complexcognition.co.uk/2021/06/ironies-of-automation.html" rel="nofollow noreferrer noopener">de-skilling of experts</a>, by depriving them of the continued exposure to the complexities of the systems they are expected to understand as experts. Remember: expertise is accumulated through repeated exposure to a wide variety of situations over time. As we add more automation and AI to these systems, it becomes even more difficult for people to build expertise with those systems, much less to be able to introspect how automation and AI are impacting the functioning of these systems.&nbsp;</p>
<p dir="ltr">So what can organizations actually do to cultivate and protect the expertise that their resilience depends on?</p>
<h1 dir="ltr">Three Things Organizations Can Do to Foster Expertise</h1>
<ol>
<li dir="ltr">
<p dir="ltr">Develop and feed a culture where expertise is given the time and space it needs to develop and thrive. This means resisting the pressure to automate away the messy, variable, difficult work that builds expertise in the first place. It means recognizing that the engineer who has been in the weeds with a system for three years is an organizational asset, not just a headcount.</p>
</li>
<li dir="ltr">
<p dir="ltr">Invest in building the skills required to do effective incident analysis. Post-incident reviews done well are one of the most powerful tools organizations have for surfacing, sharing, and building expertise. Not the checkbox RCA that documents what went wrong and assigns blame, but the rich, narrative, learning-focused review that asks how the system actually behaved and how your experts made sense of it under pressure. This is where tacit knowledge gets made visible.</p>
</li>
<li dir="ltr">
<p dir="ltr">Help your experts identify and share the strategies and patterns they use. Incident analysis will help surface these, but the work doesn't stop there. Your organization needs specific approaches like knowledge elicitation, narrative storytelling, and communities of practice for distilling what your experts know into something the broader team can learn from.&nbsp;</p>
</li>
</ol>
<p dir="ltr">The vendors selling AI SRE and AI Ops solutions aren't necessarily wrong that these tools can help during incident response—they can survey environments, find patterns, and generate hypotheses faster than any human. But they are focused on a different problem than ensuring whether your systems are resilient. Resilience is continuously created by the people who have spent years inside your systems, who know what normal feels like, who remember what happened last time things looked like <em>this</em>. That expertise took time to build, and&nbsp; it can't be purchased off the shelf. The good news is that it can be cultivated, incentivized, and shared. Expertise is contagious when organizations create the conditions for it to spread.</p>
<h3 dir="ltr">References</h3>
<p dir="ltr"><a href="https://www.amazon.com/Peak-Secrets-New-Science-Expertise/dp/0544456238" rel="nofollow noreferrer noopener">Peak: Secrets From the New Science of Expertise</a> (Ericsson &amp; Pool 2016)&nbsp;</p>
<p dir="ltr"><a href="https://www.amazon.com/Seeing-What-Others-Dont-Remarkable/dp/1610393821/ref=asap_bc?ie=UTF8" rel="nofollow noreferrer noopener">Seeing What Others Don't: The Remarkable Ways We Gain Insights</a> (Gary Klein, 2015)</p>
<p dir="ltr"><a href="https://www.amazon.com/Cambridge-Expertise-Performance-Handbooks-Psychology/dp/0521600812" rel="nofollow noreferrer noopener">The Cambridge Handbook of Expertise and Expert Performance</a> (Ericsson et al, 2006)</p>
<p dir="ltr"><a href="https://www.researchgate.net/publication/322693359_Why_Expertise_Matters_A_Response_to_the_Challenges" rel="nofollow noreferrer noopener">Why Expertise Matters: A Response to the Challenges</a> (Klein et al., 2017)</p>
<p dir="ltr"><a href="https://www.complexcognition.co.uk/2021/06/ironies-of-automation.html" rel="nofollow noreferrer noopener">The Ironies of Automation</a> (Bainbridge, 1983)</p>
<p dir="ltr">&nbsp;</p>
<p dir="ltr"><img src="https://d21hwc2yj2s6ok.cloudfront.net/shrine_store/uploads/networks/3057/communication_news/11560646/compressed-abd9e68f5fc007be1c81f15f1f28e000.webp" alt="" width="145" height="159"></p>
<p dir="ltr"><strong>Courtney Nash</strong></p>
<p dir="ltr">Vice President, RISF</p>
<p>&nbsp;</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/11547831"><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 mzMfR"></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">The Treachery of Postmortems</div></h3></div><div class="Box-sc-3e41dc6a-0 styled__RelatedDateStyled-sc-2c2295e1-9 jwSNmt">July 8, 2026</div><div class="Box-sc-3e41dc6a-0 styled__RelatedDescription-sc-2c2295e1-10 eSvYQq hfHojJ"><div>Postmortems, retrospectives, incident reviews—these are all different names for roughly the same thing. We&#x27;re surprised by the improbable (an incident), and are looking to gain some insight from that unexpected turn of events. The problem is, as an industry we more often than not mistake capturing and archiving information for developing meaningful insights. Take the famous painting &quot;The Treachery</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/11572961"><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 hSIDqr"></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">Wild AI-related Reliability Incidents Are Coming</div></h3></div><div class="Box-sc-3e41dc6a-0 styled__RelatedDateStyled-sc-2c2295e1-9 jwSNmt">August 31, 2026</div><div class="Box-sc-3e41dc6a-0 styled__RelatedDescription-sc-2c2295e1-10 eSvYQq hfHojJ"><div>Image: Brief independent investigation of agents’ behavior, reasoning and collaboration in the OpenAI / Hugging Face hacking incident (METR) [Ed note: This post was written before the Open AI Technical Report and independent METR analysis were published.]Recently, two AI-related pieces of content caught my attention. 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dir=\"ltr\"\u003eAbby Wambach, Neil Peart, Kelsey Hightower. These people are all experts in their relevant fields. Through them we recognize expertise as an exceptionally high level of performance on a particular task or within a given domain. The universal inputs to their expertise are time and exposure (aka practice). Expertise in any domain is acquired through doing, and more specifically by continued exposure to varying conditions and inputs inherent to the given domain. The latter part is important: while someone can absolutely become an expert in a very narrow sense (e.g. solving Rubik’s cubes), and someone can also learn about a subject or domain by reading, talking, listening, watching podcasts, etc., high-performance domains exert unpredictable, variable, and dynamic demands on the expert that are only mastered by continued exposure and deliberate practice within that domain.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"ltr\"\u003e”Experts are the people the team turns to when faced with difficult tasks.”\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e—Gary Klein\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp dir=\"ltr\"\u003eI have recently been on a number of podcasts/webinars/panels where the question of what expert incident response looks like comes up, especially how to train for and formalize it. Lurking behind that question for some people is often the hope that in the answer lies the opportunity to productize, scale, and even automate this essential skill for modern software-based businesses. We are currently awash in AI Ops and AI SRE solutions attempting to do just this, and I’m here to convince you that expertise cannot be bottled and sold. Beyond that, I hope to persuade you that expertise is worth investing in and cultivating within your own organization.\u003c/p\u003e\n\u003ch1 dir=\"ltr\"\u003eCommon Characteristics of Expertise\u003c/h1\u003e\n\u003cp\u003eIt is worth noting that expertise is not simply the accrual of experience over time.\u0026nbsp;As hinted at in the earlier quote from Gary Klein, expertise is a specific kind of knowledge, and it shares a number of things in common.\u003c/p\u003e\n\u003ch2 dir=\"ltr\"\u003eExpertise Is Often Invisible\u003c/h2\u003e\n\u003cp dir=\"ltr\"\u003eCognitive scientists and folks in the field of Resilience Engineering refer to this as the \u003ca href=\"https://www.youtube.com/watch?v=2Nc8hgEE7ig\" rel=\"nofollow noreferrer noopener\"\u003eLaw of Fluency\u003c/a\u003e. This fluency characterizes an activity that is well-adapted, such that the effort and challenges involved in conducting that work are hidden from view, making it appear smooth and effortless. Experts adapt to complex and surprising situations by filling gaps and managing challenges effectively and efficiently, and notably in ways that may not be apparent to other people. In general, fluency is considered a hallmark characteristic of expertise.\u003c/p\u003e\n\u003ch2 dir=\"ltr\"\u003eExpertise Is Not Easily Introspected\u003c/h2\u003e\n\u003cp dir=\"ltr\"\u003eIn general, experts do not have direct access to the cognitive, physical, and other sources of their expertise—human performance, given the way it is acquired through experience over time, is inherently difficult to explain. This is why many people refer to this kind of skill as implicit, or subconscious. Answering the question “How did you know to do X?” for an expert often leads to a long exploration of “Well, it reminded me of this one time…” or “It seemed similar to when…” In asking someone to explain their expertise, we are effectively asking them to try to unearth every second of practice and effort they have put into acquiring their skill.\u003c/p\u003e\n\u003ch2 dir=\"ltr\"\u003eExpertise Enables Adaptation\u003c/h2\u003e\n\u003cp dir=\"ltr\"\u003ePeople newer to a skill or domain typically rely on documentation and more rigid and rule-based methods for performing a given task. They lack the context and experience to handle variations from standard expectations and outcomes, and tend not to experiment or improvise as much. Experts, on the other hand, demonstrate a unique ability to adapt to novel information, challenges, and surprise situations. Given the breadth and depth of their experience (and having learned by trying many various approaches and strategies over time), they are better equipped (and generally, more confident) to stretch and adjust when presented with novel situations. Think of this as the difference between being able to read sheet music well and being able to play jazz with a group you just met.\u0026nbsp;\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eConsider the following \u003ca href=\"https://www.researchgate.net/publication/322693359_Why_Expertise_Matters_A_Response_to_the_Challenges\" rel=\"nofollow noreferrer noopener\"\u003elist of characteristics of expertise\u003c/a\u003e that Gary Klein and a number of other cognitive scientists have empirically demonstrated. They found that experts:\u003c/p\u003e\n\u003cul\u003e\n\u003cli dir=\"ltr\"\u003e\n\u003cp dir=\"ltr\"\u003eEmploy more effective strategies than others, and do so with less effort;\u003c/p\u003e\n\u003c/li\u003e\n\u003cli dir=\"ltr\"\u003e\n\u003cp dir=\"ltr\"\u003eperceive meaning in patterns that others do not notice;\u003c/p\u003e\n\u003c/li\u003e\n\u003cli dir=\"ltr\"\u003e\n\u003cp dir=\"ltr\"\u003eform rich mental models of situations to support sensemaking and anticipatory thinking;\u003c/p\u003e\n\u003c/li\u003e\n\u003cli dir=\"ltr\"\u003e\n\u003cp dir=\"ltr\"\u003ehave extensive and highly organized domain knowledge; and\u003c/p\u003e\n\u003c/li\u003e\n\u003cli dir=\"ltr\"\u003e\n\u003cp dir=\"ltr\"\u003eare intrinsically motivated to work on hard problems that stretch their capabilities.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"ltr\"\u003eIf that sounds like some of the fundamental aspects of resilience, you’re on the right track.\u0026nbsp;\u003c/p\u003e\n\u003ch1 dir=\"ltr\"\u003eExpertise Is a Key Ingredient for Resilience\u003c/h1\u003e\n\u003cp dir=\"ltr\"\u003eGiven the above characteristics of expertise and how they develop, expertise can’t simply be “bottled” and subsequently scaled or reproduced en masse. As noted above, most high-performance domains are extremely variable, so one element of maintaining expertise is constantly updating mental models and factoring in new variables, challenges, and developments. Layer the nature of modern distributed software systems on top of this, and you begin to see that one cannot simply capture expert incident response and productize it.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eSomeone is inevitably going to say “Well we’ll just train our LLMs and agentic models on all the conditions necessary to generate expertise in incident response and then we can have that, right?” As they currently stand, LLMs and AI agents do not acquire expertise the way humans do, in part because they require human model development, training, and refinement. They also are not capable of sensemaking, reflection, coordination across fuzzy boundaries, reciprocity, vicarious learning from others’ experiences, observational learning (picking up things “in the air” from casual discussions), or “seeing the invisible” (perceiving missing vs. present cues)—these are all but a subset of the kinds of cognitive processes involved in developing and maintaining expertise that have been studied for decades by cognitive scientists.\u0026nbsp;\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eAI SRE agents can currently survey a given system’s environment and find inputs and patterns to generate hypotheses about how a given situation may have arisen. They can do this over and over and over, but they do not possess the knowledge or ability to, for example, call Sarah on the database team and see if she knows why things look weird. They can’t factor in the architectural discussions the team had about the recent migration which might explain why things aren’t behaving as expected. They can't notice that the system is behaving exactly like it did six months ago before a cascading failure, because that pattern lives in someone's memory, not in a log file. They can't pick up on the fact that the on-call engineer sounds unusually uncertain on the incident call, or that the team has gone quiet in a way that usually signals something is badly wrong. These aren’t edge cases, they are routine features of how complex incidents actually unfold.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eThe prevailing belief in the software industry appears to be that we can use automation and AI to replace what expertise has given us in the past. However, consider the \u003ca href=\"https://www.thevoid.community/report-2024\" rel=\"nofollow noreferrer noopener\"\u003edata from the 2024 VOID report\u003c/a\u003e, wherein 75% of incidents involving automation required human intervention to comprehend, troubleshoot, and resolve the incident. That is where experts quite often save the day. Here we contend with the ouroboros of expertise and automation, in which experts don’t have as much experience with the system at hand, and then when asked to step in and resolve a problem with said system, they have found their expertise eroded by having less direct access to how it actually functions.\u0026nbsp;\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eA lack of experience with systems due to increased automation and AI can lead to \u003ca href=\"https://www.complexcognition.co.uk/2021/06/ironies-of-automation.html\" rel=\"nofollow noreferrer noopener\"\u003ede-skilling of experts\u003c/a\u003e, by depriving them of the continued exposure to the complexities of the systems they are expected to understand as experts. Remember: expertise is accumulated through repeated exposure to a wide variety of situations over time. As we add more automation and AI to these systems, it becomes even more difficult for people to build expertise with those systems, much less to be able to introspect how automation and AI are impacting the functioning of these systems.\u0026nbsp;\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eSo what can organizations actually do to cultivate and protect the expertise that their resilience depends on?\u003c/p\u003e\n\u003ch1 dir=\"ltr\"\u003eThree Things Organizations Can Do to Foster Expertise\u003c/h1\u003e\n\u003col\u003e\n\u003cli dir=\"ltr\"\u003e\n\u003cp dir=\"ltr\"\u003eDevelop and feed a culture where expertise is given the time and space it needs to develop and thrive. This means resisting the pressure to automate away the messy, variable, difficult work that builds expertise in the first place. It means recognizing that the engineer who has been in the weeds with a system for three years is an organizational asset, not just a headcount.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli dir=\"ltr\"\u003e\n\u003cp dir=\"ltr\"\u003eInvest in building the skills required to do effective incident analysis. Post-incident reviews done well are one of the most powerful tools organizations have for surfacing, sharing, and building expertise. Not the checkbox RCA that documents what went wrong and assigns blame, but the rich, narrative, learning-focused review that asks how the system actually behaved and how your experts made sense of it under pressure. This is where tacit knowledge gets made visible.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli dir=\"ltr\"\u003e\n\u003cp dir=\"ltr\"\u003eHelp your experts identify and share the strategies and patterns they use. Incident analysis will help surface these, but the work doesn't stop there. Your organization needs specific approaches like knowledge elicitation, narrative storytelling, and communities of practice for distilling what your experts know into something the broader team can learn from.\u0026nbsp;\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp dir=\"ltr\"\u003eThe vendors selling AI SRE and AI Ops solutions aren't necessarily wrong that these tools can help during incident response—they can survey environments, find patterns, and generate hypotheses faster than any human. But they are focused on a different problem than ensuring whether your systems are resilient. Resilience is continuously created by the people who have spent years inside your systems, who know what normal feels like, who remember what happened last time things looked like \u003cem\u003ethis\u003c/em\u003e. That expertise took time to build, and\u0026nbsp; it can't be purchased off the shelf. The good news is that it can be cultivated, incentivized, and shared. Expertise is contagious when organizations create the conditions for it to spread.\u003c/p\u003e\n\u003ch3 dir=\"ltr\"\u003eReferences\u003c/h3\u003e\n\u003cp dir=\"ltr\"\u003e\u003ca href=\"https://www.amazon.com/Peak-Secrets-New-Science-Expertise/dp/0544456238\" rel=\"nofollow noreferrer noopener\"\u003ePeak: Secrets From the New Science of Expertise\u003c/a\u003e (Ericsson \u0026amp; Pool 2016)\u0026nbsp;\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003ca href=\"https://www.amazon.com/Seeing-What-Others-Dont-Remarkable/dp/1610393821/ref=asap_bc?ie=UTF8\" rel=\"nofollow noreferrer noopener\"\u003eSeeing What Others Don't: The Remarkable Ways We Gain Insights\u003c/a\u003e (Gary Klein, 2015)\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003ca href=\"https://www.amazon.com/Cambridge-Expertise-Performance-Handbooks-Psychology/dp/0521600812\" rel=\"nofollow noreferrer noopener\"\u003eThe Cambridge Handbook of Expertise and Expert Performance\u003c/a\u003e (Ericsson et al, 2006)\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003ca href=\"https://www.researchgate.net/publication/322693359_Why_Expertise_Matters_A_Response_to_the_Challenges\" rel=\"nofollow noreferrer noopener\"\u003eWhy Expertise Matters: A Response to the Challenges\u003c/a\u003e (Klein et al., 2017)\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003ca href=\"https://www.complexcognition.co.uk/2021/06/ironies-of-automation.html\" rel=\"nofollow noreferrer noopener\"\u003eThe Ironies of Automation\u003c/a\u003e (Bainbridge, 1983)\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cimg src=\"https://d21hwc2yj2s6ok.cloudfront.net/shrine_store/uploads/networks/3057/communication_news/11560646/compressed-abd9e68f5fc007be1c81f15f1f28e000.webp\" alt=\"\" width=\"145\" height=\"159\"\u003e\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cstrong\u003eCourtney Nash\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eVice President, RISF\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3b:T3334,"])</script><script>self.__next_f.push([1,"\u003cp dir=\"ltr\"\u003eAbby Wambach, Neil Peart, Kelsey Hightower. These people are all experts in their relevant fields. Through them we recognize expertise as an exceptionally high level of performance on a particular task or within a given domain. The universal inputs to their expertise are time and exposure (aka practice). Expertise in any domain is acquired through doing, and more specifically by continued exposure to varying conditions and inputs inherent to the given domain. The latter part is important: while someone can absolutely become an expert in a very narrow sense (e.g. solving Rubik’s cubes), and someone can also learn about a subject or domain by reading, talking, listening, watching podcasts, etc., high-performance domains exert unpredictable, variable, and dynamic demands on the expert that are only mastered by continued exposure and deliberate practice within that domain.\u003c/p\u003e\n\u003cblockquote\u003e\n\u003cp dir=\"ltr\"\u003e”Experts are the people the team turns to when faced with difficult tasks.”\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e—Gary Klein\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp dir=\"ltr\"\u003eI have recently been on a number of podcasts/webinars/panels where the question of what expert incident response looks like comes up, especially how to train for and formalize it. Lurking behind that question for some people is often the hope that in the answer lies the opportunity to productize, scale, and even automate this essential skill for modern software-based businesses. We are currently awash in AI Ops and AI SRE solutions attempting to do just this, and I’m here to convince you that expertise cannot be bottled and sold. Beyond that, I hope to persuade you that expertise is worth investing in and cultivating within your own organization.\u003c/p\u003e\n\u003ch1\u003eCommon Characteristics of Expertise\u003c/h1\u003e\n\u003cp\u003eIt is worth noting that expertise is not simply the accrual of experience over time.\u0026nbsp;As hinted at in the earlier quote from Gary Klein, expertise is a specific kind of knowledge, and it shares a number of things in common.\u003c/p\u003e\n\u003ch2\u003eExpertise Is Often Invisible\u003c/h2\u003e\n\u003cp dir=\"ltr\"\u003eCognitive scientists and folks in the field of Resilience Engineering refer to this as the \u003ca href=\"https://www.youtube.com/watch?v=2Nc8hgEE7ig\" rel=\"nofollow noreferrer noopener\" target=\"_blank\"\u003eLaw of Fluency\u003c/a\u003e. This fluency characterizes an activity that is well-adapted, such that the effort and challenges involved in conducting that work are hidden from view, making it appear smooth and effortless. Experts adapt to complex and surprising situations by filling gaps and managing challenges effectively and efficiently, and notably in ways that may not be apparent to other people. In general, fluency is considered a hallmark characteristic of expertise.\u003c/p\u003e\n\u003ch2\u003eExpertise Is Not Easily Introspected\u003c/h2\u003e\n\u003cp dir=\"ltr\"\u003eIn general, experts do not have direct access to the cognitive, physical, and other sources of their expertise—human performance, given the way it is acquired through experience over time, is inherently difficult to explain. This is why many people refer to this kind of skill as implicit, or subconscious. Answering the question “How did you know to do X?” for an expert often leads to a long exploration of “Well, it reminded me of this one time…” or “It seemed similar to when…” In asking someone to explain their expertise, we are effectively asking them to try to unearth every second of practice and effort they have put into acquiring their skill.\u003c/p\u003e\n\u003ch2\u003eExpertise Enables Adaptation\u003c/h2\u003e\n\u003cp dir=\"ltr\"\u003ePeople newer to a skill or domain typically rely on documentation and more rigid and rule-based methods for performing a given task. They lack the context and experience to handle variations from standard expectations and outcomes, and tend not to experiment or improvise as much. Experts, on the other hand, demonstrate a unique ability to adapt to novel information, challenges, and surprise situations. Given the breadth and depth of their experience (and having learned by trying many various approaches and strategies over time), they are better equipped (and generally, more confident) to stretch and adjust when presented with novel situations. Think of this as the difference between being able to read sheet music well and being able to play jazz with a group you just met.\u0026nbsp;\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eConsider the following \u003ca href=\"https://www.researchgate.net/publication/322693359_Why_Expertise_Matters_A_Response_to_the_Challenges\" rel=\"nofollow noreferrer noopener\" target=\"_blank\"\u003elist of characteristics of expertise\u003c/a\u003e that Gary Klein and a number of other cognitive scientists have empirically demonstrated. They found that experts:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp dir=\"ltr\"\u003eEmploy more effective strategies than others, and do so with less effort;\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"ltr\"\u003eperceive meaning in patterns that others do not notice;\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"ltr\"\u003eform rich mental models of situations to support sensemaking and anticipatory thinking;\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"ltr\"\u003ehave extensive and highly organized domain knowledge; and\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"ltr\"\u003eare intrinsically motivated to work on hard problems that stretch their capabilities.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"ltr\"\u003eIf that sounds like some of the fundamental aspects of resilience, you’re on the right track.\u0026nbsp;\u003c/p\u003e\n\u003ch1\u003eExpertise Is a Key Ingredient for Resilience\u003c/h1\u003e\n\u003cp dir=\"ltr\"\u003eGiven the above characteristics of expertise and how they develop, expertise can’t simply be “bottled” and subsequently scaled or reproduced en masse. As noted above, most high-performance domains are extremely variable, so one element of maintaining expertise is constantly updating mental models and factoring in new variables, challenges, and developments. Layer the nature of modern distributed software systems on top of this, and you begin to see that one cannot simply capture expert incident response and productize it.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eSomeone is inevitably going to say “Well we’ll just train our LLMs and agentic models on all the conditions necessary to generate expertise in incident response and then we can have that, right?” As they currently stand, LLMs and AI agents do not acquire expertise the way humans do, in part because they require human model development, training, and refinement. They also are not capable of sensemaking, reflection, coordination across fuzzy boundaries, reciprocity, vicarious learning from others’ experiences, observational learning (picking up things “in the air” from casual discussions), or “seeing the invisible” (perceiving missing vs. present cues)—these are all but a subset of the kinds of cognitive processes involved in developing and maintaining expertise that have been studied for decades by cognitive scientists.\u0026nbsp;\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eAI SRE agents can currently survey a given system’s environment and find inputs and patterns to generate hypotheses about how a given situation may have arisen. They can do this over and over and over, but they do not possess the knowledge or ability to, for example, call Sarah on the database team and see if she knows why things look weird. They can’t factor in the architectural discussions the team had about the recent migration which might explain why things aren’t behaving as expected. They can't notice that the system is behaving exactly like it did six months ago before a cascading failure, because that pattern lives in someone's memory, not in a log file. They can't pick up on the fact that the on-call engineer sounds unusually uncertain on the incident call, or that the team has gone quiet in a way that usually signals something is badly wrong. These aren’t edge cases, they are routine features of how complex incidents actually unfold.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eThe prevailing belief in the software industry appears to be that we can use automation and AI to replace what expertise has given us in the past. However, consider the \u003ca href=\"https://www.thevoid.community/report-2024\" rel=\"nofollow noreferrer noopener\" target=\"_blank\"\u003edata from the 2024 VOID report\u003c/a\u003e, wherein 75% of incidents involving automation required human intervention to comprehend, troubleshoot, and resolve the incident. That is where experts quite often save the day. Here we contend with the ouroboros of expertise and automation, in which experts don’t have as much experience with the system at hand, and then when asked to step in and resolve a problem with said system, they have found their expertise eroded by having less direct access to how it actually functions.\u0026nbsp;\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eA lack of experience with systems due to increased automation and AI can lead to \u003ca href=\"https://www.complexcognition.co.uk/2021/06/ironies-of-automation.html\" rel=\"nofollow noreferrer noopener\" target=\"_blank\"\u003ede-skilling of experts\u003c/a\u003e, by depriving them of the continued exposure to the complexities of the systems they are expected to understand as experts. Remember: expertise is accumulated through repeated exposure to a wide variety of situations over time. As we add more automation and AI to these systems, it becomes even more difficult for people to build expertise with those systems, much less to be able to introspect how automation and AI are impacting the functioning of these systems.\u0026nbsp;\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eSo what can organizations actually do to cultivate and protect the expertise that their resilience depends on?\u003c/p\u003e\n\u003ch1\u003eThree Things Organizations Can Do to Foster Expertise\u003c/h1\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp dir=\"ltr\"\u003eDevelop and feed a culture where expertise is given the time and space it needs to develop and thrive. This means resisting the pressure to automate away the messy, variable, difficult work that builds expertise in the first place. It means recognizing that the engineer who has been in the weeds with a system for three years is an organizational asset, not just a headcount.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"ltr\"\u003eInvest in building the skills required to do effective incident analysis. Post-incident reviews done well are one of the most powerful tools organizations have for surfacing, sharing, and building expertise. Not the checkbox RCA that documents what went wrong and assigns blame, but the rich, narrative, learning-focused review that asks how the system actually behaved and how your experts made sense of it under pressure. This is where tacit knowledge gets made visible.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"ltr\"\u003eHelp your experts identify and share the strategies and patterns they use. Incident analysis will help surface these, but the work doesn't stop there. Your organization needs specific approaches like knowledge elicitation, narrative storytelling, and communities of practice for distilling what your experts know into something the broader team can learn from.\u0026nbsp;\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp dir=\"ltr\"\u003eThe vendors selling AI SRE and AI Ops solutions aren't necessarily wrong that these tools can help during incident response—they can survey environments, find patterns, and generate hypotheses faster than any human. But they are focused on a different problem than ensuring whether your systems are resilient. Resilience is continuously created by the people who have spent years inside your systems, who know what normal feels like, who remember what happened last time things looked like \u003cem\u003ethis\u003c/em\u003e. That expertise took time to build, and\u0026nbsp; it can't be purchased off the shelf. The good news is that it can be cultivated, incentivized, and shared. Expertise is contagious when organizations create the conditions for it to spread.\u003c/p\u003e\n\u003ch3\u003eReferences\u003c/h3\u003e\n\u003cp dir=\"ltr\"\u003e\u003ca href=\"https://www.amazon.com/Peak-Secrets-New-Science-Expertise/dp/0544456238\" rel=\"nofollow noreferrer noopener\" target=\"_blank\"\u003ePeak: Secrets From the New Science of Expertise\u003c/a\u003e (Ericsson \u0026amp; Pool 2016)\u0026nbsp;\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003ca href=\"https://www.amazon.com/Seeing-What-Others-Dont-Remarkable/dp/1610393821/ref=asap_bc?ie=UTF8\" rel=\"nofollow noreferrer noopener\" target=\"_blank\"\u003eSeeing What Others Don't: The Remarkable Ways We Gain Insights\u003c/a\u003e (Gary Klein, 2015)\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003ca href=\"https://www.amazon.com/Cambridge-Expertise-Performance-Handbooks-Psychology/dp/0521600812\" rel=\"nofollow noreferrer noopener\" target=\"_blank\"\u003eThe Cambridge Handbook of Expertise and Expert Performance\u003c/a\u003e (Ericsson et al, 2006)\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003ca href=\"https://www.researchgate.net/publication/322693359_Why_Expertise_Matters_A_Response_to_the_Challenges\" rel=\"nofollow noreferrer noopener\" target=\"_blank\"\u003eWhy Expertise Matters: A Response to the Challenges\u003c/a\u003e (Klein et al., 2017)\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003ca href=\"https://www.complexcognition.co.uk/2021/06/ironies-of-automation.html\" rel=\"nofollow noreferrer noopener\" target=\"_blank\"\u003eThe Ironies of Automation\u003c/a\u003e (Bainbridge, 1983)\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cimg src=\"https://d21hwc2yj2s6ok.cloudfront.net/shrine_store/uploads/networks/3057/communication_news/11560646/compressed-abd9e68f5fc007be1c81f15f1f28e000.webp\" style=\"max-width:100%\"\u003e\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cstrong\u003eCourtney Nash\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eVice President, RISF\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3c:T21f3,"])</script><script>self.__next_f.push([1,"\u003cp dir=\"ltr\"\u003ePostmortems, retrospectives, incident reviews—these are all different names for roughly the same thing. We're surprised by the improbable (an incident), and are looking to gain some insight from that unexpected turn of events. The problem is, as an industry we more often than not mistake capturing and archiving information for developing meaningful insights.\u0026nbsp;\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eTake the famous painting \"The Treachery of Images\" created almost a century ago. It is unmistakably a pipe, with a long stem and bright sheen over well polished wood. But underneath in French, it reads \"Ceci n'est pas une pipe\" (\"This is not a pipe\"). Can you stuff the chamber on it and smoke it, smell the cooled embers, or even pick it up? No, because it's only the representation of the object, not the object itself.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eMuch like that pipe, the documents we share and call postmortems are only representative of an attempt at shared learning, the mechanical motions meant to mimic what's needed for learning. To our own detriment, we've conflated generating a postmortem with learning, and in doing so substitute the transcription of events with the meaningful work in understanding what has happened. Even worse, AI summarizations of events are done without human intervention and treated as the insight itself.\u0026nbsp;\u003c/p\u003e\n\u003ch2 dir=\"ltr\"\u003eDocumentation as Performative Insights\u003c/h2\u003e\n\u003cp dir=\"ltr\"\u003eTech loves a solid incident retro document. People want information, answers to the questions surrounding the seemingly impossible: How did this happen? Our post incident docs tend towards generating “action items” in the hopes of ensuring it never happens again or just necessary PR in a public blog post to shore up the worries of a customer base or investors. These are common and understandable, but they fall short of actual learning.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eThe majority of those docs are fairly formulaic as well, a reason we see frequent use of cookie cutter templates for them. State the facts (that are worth stating), find a few choice parts, and sprinkle in a few comments, maybe drop in some dashboard screen caps for good measure. And there you have it! Your document is ready, you've captured the learning, and once again the world is made right. Right?\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eExcept your retrospective, postmortem, or however you want to name it is merely data capture, which is not the same thing as a learning review.\u003c/p\u003e\n\u003ch2 dir=\"ltr\"\u003eWhat's Wrong with Postmortem Docs?\u003c/h2\u003e\n\u003cp dir=\"ltr\"\u003eWe often confuse the creation of a shareable document as the goal. So what's missing?\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eA frequent failure mode, even for the best of us, is that we tend towards quick answers when disaster strikes. “Tell me the one thing we could do to make sure this never happens again, so we can address it and completely close that gap.” Unfortunately, that's not how incidents work. The sole output of an incident being a document (that likely contains recommended action items) provides a myopic view of the events.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eThere's a bias to quickly skim and move on when it's only a doc, or perhaps ignore it altogether. \"I'll read it later,\" you say, filing it in your inbox or browser bookmarks. Weeks go by, you rediscover it, except now it doesn't hold the same weight it might have earlier. The new normal takes hold, informing what is critical and what can be ignored, information that seemed pressing at the time feeling trivial or inconsequential now. A whole host of new problems have arisen, we can't worry about something we've already \"solved!\"\u0026nbsp;\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eThat doesn't mean writing a doc is inherently wrong. We should write them, focusing on capturing discussions as they happen rather than curating a predisposed view. Allowing postmortems to be editable as well means we're continuously incorporating ideas. We should use them as a means of bringing folks in, adding to them, and allowing differing or disagreeing points of view to be surfaced.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eIf we're only writing an artifact and stopping, we're failing to make good on better understanding our system. Instead, we can use timelines, interviews, questions, graphs and figures, early suggestions, and everything else that helps develop the doc as a jumping off point.\u003c/p\u003e\n\u003ch2 dir=\"ltr\"\u003eA Cure for the Common Review\u003c/h2\u003e\n\u003cp dir=\"ltr\"\u003eWhat makes a post incident review discussion so useful? It's the chance to question assumptions and recalibrate mental models, both individual and collective. If we're taking the opinion of a few people as sacrosanct, we're missing out on what others see, in and out of the incident. So the solution for the everyday is walking the untread path.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eWriting a postmortem can be a lonely exercise, but there's a remedy: make it a communal activity with individual interviews. If you can spare fifteen minutes or more, let participants walk you through the timeline as they see it. Those gaps, the places where folks differ or question alternative narratives, that's where they have their \"aha!\" moments. Most importantly let the interviewee talk and avoid correcting their answers (a common pitfall). That may seem counterintuitive but once you've nudged them you've inadvertently closed a door on all the potential misconceptions you might have trouble finding otherwise, insights you can develop as a team together in larger discussions.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eShare the responsibility of discussion lead. Time is a luxury and not every incident is going to garner a corresponding meeting, but that doesn't mean all is lost. Some of that roteness comes from the same people writing the reviews every time. Pass the proverbial conch shell to another member of the team, specifically someone not involved in the incident or less experienced in writing. When they get stuck, if the timeline is not quite fitting together or the gaps in the runbook make themselves present, that's a great opportunity to level up. Likewise, if you can switch recitation of events between participants, you can avoid the risk of a soliloquy. It requires a little more courage, the potential to be wrong in front of a room of your colleagues, but that's also a great way to reinforce the blame awareness that's so core to a review.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eAnother pitfall I've fallen into is accidentally leading the witness, in both interview and discussion. It's tempting in interviews and various discussions to present data to folks to prime them. This sounds reasonable—you want them to be able to recall things, so why not present questions ahead of time? The rub is that priming will root them more in what you think is important. \"Walk me through the event\" and \"What happened next?\" are wonderfully simple starting points that will often surprise you as to where they will lead.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eYou're level setting without even realizing it, giving your team the chance to learn through others' lived experience. This may seem obvious, but the divide between what you read on a digital doc while your code compiles and the additional attention from a shared conversation is light years apart. Those retro docs you see shared externally, that you pore over at the virtual water cooler and say \"Would have loved to be a fly on the wall in that room.\" You have a chance to!\u003c/p\u003e\n\u003ch2 dir=\"ltr\"\u003ePost Postmortem\u003c/h2\u003e\n\u003cp dir=\"ltr\"\u003eIs your team ready for the next incident? Hard to say. Resilience is adapting to the unexpected, and by definition, once you know it, it isn't unexpected. Finding time to invest in these actions are equally fraught with uncertainty. What we can do is avoid fooling ourselves with the treacherous recitative work that only seems like learning.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003eWe're performing these tasks with the hopes they're not performative. If that's a sincere belief, then it also only makes sense for us to extract the most knowledge from these unplanned exercises. The greatest irony in all of this is that our most frequent discussions about incident reviews are the ones conducted elsewhere, the large corporations who fail hard and leave us to speculate past the information presented to the details that are too salacious for public consumption. We don't have to be constrained by that internally though! We're putting so much effort into capturing data, we should make good on realizing everything that comes with it, treating it as a platform to launch these larger discussions and better prepare for the next incident.\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"https://resilienceinsoftware.org/storage/5269684/download\" alt=\"\" width=\"128\" height=\"128\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWill Gallego\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSecretary, Resilience in Software Foundation\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3d:T2612,"])</script><script>self.__next_f.push([1,"\u003cp class=\"wp-block-paragraph\"\u003e\u003cspan style=\"font-size: 12px;\"\u003e\u003cem\u003eImage: Brief independent investigation of agents’ behavior, reasoning and collaboration in the OpenAI / Hugging Face hacking incident (\u003ca href=\"https://metr.org/blog/2026-08-26-openai-hugging-face-incident-investigation/#attempting-to-forecast-and-trigger-container-resets\" rel=\"nofollow noreferrer noopener\"\u003eMETR\u003c/a\u003e)\u0026nbsp;\u003c/em\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp class=\"wp-block-paragraph\"\u003e[\u003cem\u003eEd note: This post was written before the \u003ca href=\"https://cdn.openai.com/pdf/67869394-cb91-4c12-888c-5cbd85c7814c/OpenAI-Hugging-Face%20Incident-Technical-Report.pdf\" rel=\"nofollow noreferrer noopener\"\u003eOpen AI Technical Report \u003c/a\u003eand independent \u003ca href=\"https://metr.org/blog/2026-08-26-openai-hugging-face-incident-investigation/#attempting-to-forecast-and-trigger-container-resets\" rel=\"nofollow noreferrer noopener\"\u003eMETR analysis \u003c/a\u003ewere published.\u003c/em\u003e]\u003c/p\u003e\n\u003cp class=\"wp-block-paragraph\"\u003eRecently, two AI-related pieces of content caught my attention. The first was the blog post\u0026nbsp;\u003ca href=\"https://boristane.com/blog/on-call-is-now-theatre/\" rel=\"nofollow noreferrer noopener\"\u003eOn-Call is Now Theatre\u003c/a\u003e\u0026nbsp;by Boris Tane. He argues that AI agents are now capable of doing the majority of on-call work that is currently being done by humans, and that we should have AI agents act as first responders. Only when an AI agent isn’t capable of remediating the problem should a human actually be brought in, and the agent should be the one to page the human. As he puts it:\u003c/p\u003e\n\u003cblockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\"\u003e\n\u003cp class=\"wp-block-paragraph\"\u003eWe need software that watches itself, triages its own alerts, investigates its own incidents, fixes what it can, and escalates to a human only when it hits something genuinely novel, with the evidence already assembled.\u003cbr\u003e\u003cbr\u003ePut your AI agents in the worst on-call rotation imaginable, then give them a tool to page a human. Developers stop being the first responder, and step in only when an agent genuinely cannot figure something out.\u003c/p\u003e\n\u003c/blockquote\u003e\n\u003cp class=\"wp-block-paragraph\"\u003eTane doesn’t think that companies will really start putting AI agents on-call (“Most teams won’t do this”), but he believes it\u0026nbsp;\u003cem\u003eshould\u003c/em\u003e\u0026nbsp;happen, and he’s\u0026nbsp;\u003ca href=\"https://polylane.com/\" rel=\"nofollow noreferrer noopener\"\u003estarted a company\u003c/a\u003e\u0026nbsp;based on this premise.\u003c/p\u003e\n\u003cp class=\"wp-block-paragraph\"\u003eI like to think of putting AI agents on-call as equivalent to using AI agents to implement control system automation. Because, after all, that’s what operations work is: it’s taking control actions to keep the system in a healthy state.\u003c/p\u003e\n\u003cp class=\"wp-block-paragraph\"\u003eNow, AI agents are extremely complex software systems. I’d argue that they are the most complex software systems that we humans have ever built. That complexity is both good and bad.\u0026nbsp;\u003ca href=\"https://surfingcomplexity.blog/2020/01/18/there-is-no-escape-from-ashbys-law/\" rel=\"nofollow noreferrer noopener\"\u003eAshby’s Law\u003c/a\u003e\u0026nbsp;teaches us that the larger the set of system states that you want your control system to be able to handle, the more complex that it needs to be. It’s this complexity that makes it possible, in principle, to apply AI agents to solve a generic control problem like this.\u003c/p\u003e\n\u003cp class=\"wp-block-paragraph\"\u003eOn the other hand, the more complex a system becomes, the more difficult it is for a human to reason about the system’s behavior. That’s fine when the system is healthy, but if your now-even-more-complex system gets into a state that the automation can’t handle, that can make the problem even worse. Indeed, it’s precisely the\u0026nbsp;\u003ca href=\"https://surfingcomplexity.blog/2017/06/24/a-conjecture-on-why-reliable-systems-fail/\" rel=\"nofollow noreferrer noopener\"\u003eunexpected behavior of complex control systems\u003c/a\u003e\u0026nbsp;that contributes to the worst complex systems failures (see also:\u0026nbsp;\u003ca href=\"https://surfingcomplexity.blog/2014/09/30/cloud-software-fragility-and-air-france-447/\" rel=\"nofollow noreferrer noopener\"\u003eAir France 447\u003c/a\u003e,\u0026nbsp;\u003ca href=\"https://en.wikipedia.org/wiki/Maneuvering_Characteristics_Augmentation_System#Role_of_MCAS_in_accidents\" rel=\"nofollow noreferrer noopener\"\u003eBoeing 737 MAX accidents\u003c/a\u003e).\u003c/p\u003e\n\u003cp class=\"wp-block-paragraph\"\u003eAnd that brings me to the other piece of content I saw recently: the\u0026nbsp;\u003ca href=\"https://www.youtube.com/watch?v=87DyyMV0kCY\" rel=\"nofollow noreferrer noopener\"\u003eOpenAI talk at BlackHat\u003c/a\u003e\u0026nbsp;(h/t\u0026nbsp;\u003ca href=\"https://www.linkedin.com/in/dnblankedelman/\" rel=\"nofollow noreferrer noopener\"\u003eDavid Blank-Edelman\u003c/a\u003e). Yes, it’s a\u0026nbsp;\u003ca href=\"https://theonion.com/friend-who-sent-link-to-8-minute-youtube-video-must-be-1819574977/\" rel=\"nofollow noreferrer noopener\"\u003e37 minute talk\u003c/a\u003e, but I encourage you to watch it.\u003c/p\u003e\n\u003cfigure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"\u003e\u003c/figure\u003e\n\u003cp class=\"wp-block-paragraph\"\u003e\u003ciframe class=\"embedly-embed\" style=\"aspect-ratio: 854 / 480; max-width: 100%;\" title=\"YouTube embed\" src=\"https://cdn.embedly.com/widgets/media.html?src=https%3A%2F%2Fwww.youtube.com%2Fembed%2F87DyyMV0kCY%3Ffeature%3Doembed\u0026amp;display_name=YouTube\u0026amp;url=https%3A%2F%2Fwww.youtube.com%2Fwatch%3Fv%3D87DyyMV0kCY\u0026amp;image=https%3A%2F%2Fi.ytimg.com%2Fvi%2F87DyyMV0kCY%2Fhqdefault.jpg\u0026amp;type=text%2Fhtml\u0026amp;schema=youtube\" width=\"854\" height=\"480\" frameborder=\"0\" scrolling=\"no\" allowfullscreen=\"allowfullscreen\"\u003e\u003c/iframe\u003e\u003c/p\u003e\n\u003cp class=\"wp-block-paragraph\"\u003eThe talk goes into detail about the surprising behavior of AI agents that resulted in security incidents at both OpenAI and Hugging Face. Honestly, this talk feels like something out of a movie about technology run amok; the sort of thing that still feels to me like absolute science fiction.\u003c/p\u003e\n\u003cp class=\"wp-block-paragraph\"\u003eBecause this was a talk at a security conference, the speakers focused on the lessons that apply to the security community. But as a reliability type, my biggest takeaway from this talk is that\u0026nbsp;\u003cem\u003e\u003cstrong\u003eautonomous LLM agents can behave in ways that humans would have never expected\u003c/strong\u003e.\u003c/em\u003e\u0026nbsp;While agents today can perform complex cognitive tasks, they behave differently than a human would performing that task. We’re most familiar with this when they make a different kind of mistake than a human would make. In the OpenAI-HuggingFace incident, it wasn’t so much that they made a mistake, it’s that the agents pursued their goals in ways different than a human would do. If a teammate of yours used 0-day exploits to overcome internal security protocols in order to get their work done, you’d say they were acting\u0026nbsp;\u003cem\u003eunreasonably\u003c/em\u003e. And that’s exactly the risk here.\u003c/p\u003e\n\u003cp class=\"wp-block-paragraph\"\u003eThe inevitable improvement in frontier models does not mean that the agent behavior will be easier to reason about; I actually think it’s the opposite. The agent behavior will get even more complex with the more advanced models, but that doesn’t mean it will get more human-like. Humans are very complex, but we know how to reason about human behavior; at least, we do for the people we work with. After all, an employee whose behavior was unpredictable would not last long in the organization. As these agents become even more capable, they will be akin to alien minds: intelligence, but not as we know it.\u003c/p\u003e\n\u003cp class=\"wp-block-paragraph\"\u003eHere’s how I think things will play out. I think that some teams will do what Tane proposes and will use AI agents as first-responders to deal with operational issues. And I think that for many cases, the agents will successfully remediate issues. Of course, for the agents to actually be able to remediate, they will need to have permissions to take operational actions without human intervention.\u003c/p\u003e\n\u003cp class=\"wp-block-paragraph\"\u003eOne day, though, there will be a complex incident which the agents will not be able to handle. Tane believes that the agents will defer to the humans in this case, by paging in a person. But that’s not the scenario I worry about. The one I worry about is that the agents attempt to remediate, and their attempt makes things worse. And it’s only after these failed remediation attempts that humans enter the loop. Maybe they eventually page in a human, or maybe a human notices that something is very wrong as the agents continue to try and fail in their remediation actions. But now the humans have to make sense of the combined software-AI-agent system behavior. The original problem was already so complex that the agents couldn’t handle it, and they have now made it worse by trying to remediate. I can even imagine the humans fighting the agents who keep trying to take actions to remediate that are failing.\u003c/p\u003e\n\u003cp class=\"wp-block-paragraph\"\u003eThis is the incident that’s coming. And it’s going to be very, very difficult to handle when it happens. And I have no idea how people will respond to the role of the AI agents in the wake of this incident.\u003c/p\u003e\n\u003cp class=\"wp-block-paragraph\"\u003e(Originally posted at\u0026nbsp;\u003ca href=\"https://surfingcomplexity.blog/2026/08/22/wild-ai-related-reliability-incidents-are-coming/\" rel=\"nofollow noreferrer noopener\"\u003eSurfing Complexity\u003c/a\u003e)\u003c/p\u003e\n\u003cp class=\"wp-block-paragraph\"\u003e\u003cimg src=\"https://resilienceinsoftware.org/storage/5664599/download\" alt=\"\" width=\"137\" height=\"137\"\u003e\u003c/p\u003e\n\u003cp class=\"wp-block-paragraph\"\u003eLorin Hochstein\u003c/p\u003e\n\u003cp class=\"wp-block-paragraph\"\u003e\u003cstrong\u003eSurfing Complexity\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv id=\"jp-post-flair\" class=\"sharedaddy sd-like-enabled sd-sharing-enabled\"\u003e\n\u003cdiv class=\"sharedaddy sd-sharing-enabled\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003c/div\u003e"])</script><script>self.__next_f.push([1,"5:[\"$\",\"$L39\",null,{\"news\":{\"id\":11560646,\"og_url\":null,\"pinned_to_feed\":false,\"pinned_to_index_page\":false,\"public\":true,\"publication_timezone\":\"America/Los_Angeles\",\"published_at\":\"2026-08-03T21:08:00Z\",\"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\":\"Expertise Can't Be Automated: Why Resilience Still Needs Humans\",\"content\":\"$3a\",\"sanitized_content\":\"Abby Wambach, Neil Peart, Kelsey Hightower. These people are all experts in their relevant fields. Through them we recognize expertise as an exceptionally high level of performance on a particular task or within a given domain. The universal inputs to their expertise are time and exposure (aka practice). Expertise in any domain is acquired through doing, and more specifically by continued exposure to varying conditions and inputs inherent to the given domain. The latter part is\",\"truncated_content\":\"Abby Wambach, Neil Peart, Kelsey Hightower. 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