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<title>Multi-Agent Incident Response: A Practical SRE Deep Dive | OpsWorker.ai</title>
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<h1 class="main-title title bordered small-border-radius no-margin">Agent-Driven SRE Investigations: A Practical Deep Dive into Multi-Agent Incident Response</h1>
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<time class="meta-info" datetime="2025-12-11">December 11, 2025</time>
<div class="min-read meta-info">10 min read</div>
<div class="last-update"><strong>Last Update:</strong> June 15, 2026</div>
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<section class="post-content">
<div class="text"><h2 id="introduction">Introduction</h2><p><a href="https://github.com/opsworker-ai/articles/blob/main/agent-ai-sre-team/Article.md?ref=opsworker.ai#introduction"></a></p><p>I’ve been exploring how far we can push fully autonomous, multi-agent investigations in real SRE environments — not as a theoretical exercise, but using actual Kubernetes clusters and real tooling. Each agent in this experiment operated inside a sandboxed environment with access to <strong>Kubernetes MCP</strong> for live cluster inspection and <strong>GitHub MCP</strong> to analyze code changes and even <strong>create remediation pull requests</strong>.</p><p>The goal wasn’t simply to generate summaries, but to observe whether agents can behave like an actual on-call team: investigate an alert, validate each other’s findings, propose mitigation steps, and — when appropriate — submit a PR that teammates can review and approve.</p><p>What emerged is a surprisingly capable prototype of an <strong>agent-driven incident response workflow</strong>, where one agent investigates, another validates the findings, and a third reviews the mitigation plan and decides whether to escalate or authorize automated actions.</p><h2 id="the-experiment">The Experiment</h2><p><a href="https://github.com/opsworker-ai/articles/blob/main/agent-ai-sre-team/Article.md?ref=opsworker.ai#the-experiment"></a></p><p>The setup is simple in concept. I split the work across three agents:</p><ul><li>Receiver: → On-call engineer who receives the alert and executes the first steps</li><li>Reviewer&nbsp;1 → First Teammate On-call reviewing the investigation </li><li>Reviewer 2 → Second Teammate On-call engineer providing final assessment and Decision Maker</li></ul><p>This mimics the dynamics of a real SRE team handling an alert: investigation, peer review, and final decision.</p><figure class="kg-card kg-image-card kg-card-hascaption"><img src="https://www.opsworker.ai/blog/content/images/2025/12/AI-SRE-Teammates-handling-incidents.png" class="kg-image" alt="" loading="lazy" width="1350" height="899" srcset="https://www.opsworker.ai/blog/content/images/size/w600/2025/12/AI-SRE-Teammates-handling-incidents.png 600w, https://www.opsworker.ai/blog/content/images/size/w1000/2025/12/AI-SRE-Teammates-handling-incidents.png 1000w, https://www.opsworker.ai/blog/content/images/2025/12/AI-SRE-Teammates-handling-incidents.png 1350w" sizes="(min-width: 720px) 720px"><figcaption><b><strong style="white-space: pre-wrap;">AI SRE agent-driven team solving incident together</strong></b></figcaption></figure><p></p><h2 id="experiment-goals">Experiment Goals</h2><p><a href="https://github.com/opsworker-ai/articles/blob/main/agent-ai-sre-team/Article.md?ref=opsworker.ai#experiment-goals"></a></p><p>The key questions I want to answer are:</p><ul><li>Can autonomous agents meaningfully triage incidents end-to-end?</li><li>Do they catch each other's mistakes, improving reliability compared to a single agent?</li><li>Can they reduce alert fatigue by escalating only when it matters?</li></ul><p>I expect that they can go surprisingly far — but the gaps will also be obvious once real-world complexity comes into play.</p><h2 id="technical-setup">Technical Setup</h2><p><a href="https://github.com/opsworker-ai/articles/blob/main/agent-ai-sre-team/Article.md?ref=opsworker.ai#technical-setup"></a></p><p>To ensure complete isolation and safety, I run the agents inside Docker containers. Each agent gets full tool access within the container using &nbsp;<code>--dangerously-skip-permissions</code>, which is safe since the container is isolated from the host system. The agents run Claude in non-interactive mode with different&nbsp;<code>CLAUDE.md</code>&nbsp;prompt files that define their roles.</p><h3 id="agent-communication-via-local-files">Agent Communication via Local Files</h3><p><a href="https://github.com/opsworker-ai/articles/blob/main/agent-ai-sre-team/Article.md?ref=opsworker.ai#agent-communication-via-local-files"></a></p><p>The agents communicate through a sequential file chain:</p><ol><li><strong>Agent 1 (Receiver)</strong>:<ul><li>Reads:&nbsp;<code>alert.json</code>&nbsp;(the original alert)</li><li>Role: On-call engineer conducting initial investigation using Kubernetes MCP server</li><li>Writes:&nbsp;<code>investigation-1.md</code>&nbsp;with findings, root cause analysis, and mitigation recommendations</li></ul></li><li><strong>Agent 2 (Reviewer 1)</strong>:<ul><li>Reads:&nbsp;<code>alert.json</code>&nbsp;and&nbsp;<code>investigation-1.md</code></li><li>Role: First teammate reviewing the investigation</li><li>Writes:&nbsp;<code>investigation-2.md</code>&nbsp;with validation, corrections, and additional insights</li></ul></li><li><strong>Agent 3 (Reviewer 2)</strong>:<ul><li>Reads:&nbsp;<code>alert.json</code>,&nbsp;<code>investigation-1.md</code>, and&nbsp;<code>investigation-2.md</code></li><li>Role: Second teammate providing final assessment</li><li>Writes:&nbsp;<code>investigation-3.md</code>&nbsp;with consolidated mitigation plan and escalation decision</li></ul></li></ol><p>Each agent runs with its own&nbsp;<code>CLAUDE.md</code>&nbsp;file that defines their specific role:</p><ul><li><a href="https://github.com/opsworker-ai/agentic-sre-investigations-poc/investigations/CLAUDE-receiver.md?ref=opsworker.ai" rel="noreferrer"><code>CLAUDE-receiver.md</code></a>&nbsp;— defines the on-call engineer role</li><li><a href="https://github.com/opsworker-ai/agentic-sre-investigations-poc/investigations/CLAUDE-receiver1.md?ref=opsworker.ai" rel="noreferrer"><code>CLAUDE-reviewer1.md</code></a>&nbsp;— defines the first reviewer role</li><li><a href="https://github.com/opsworker-ai/agentic-sre-investigations-poc/investigations/CLAUDE-receiver2.md?ref=opsworker.ai" rel="noreferrer"><code>CLAUDE-reviewer2.md</code></a>&nbsp;— defines the second reviewer role</li></ul><h3 id="container-setup">Container Setup</h3><p><a href="https://github.com/opsworker-ai/articles/blob/main/agent-ai-sre-team/Article.md?ref=opsworker.ai#container-setup"></a></p><p>The containerized environment includes all necessary tools and authentication:</p><p><strong>Dockerfile</strong>:</p>
<!--kg-card-begin: html-->
<pre><code lang="bash">
FROM node:20-slim
# Install dependencies
RUN apt-get update && apt-get install -y \
curl \
git \
uuid-runtime \
&& rm -rf /var/lib/apt/lists/*
# Install kubectl
RUN curl -LO "https://dl.k8s.io/release/$(curl -L -s https://dl.k8s.io/release/stable.txt)/bin/linux/amd64/kubectl" \
&& chmod +x kubectl \
&& mv kubectl /usr/local/bin/
# Install gh CLI
RUN curl -fsSL https://cli.github.com/packages/githubcli-archive-keyring.gpg | dd of=/usr/share/keyrings/githubcli-archive-keyring.gpg \
&& chmod go+r /usr/share/keyrings/githubcli-archive-keyring.gpg \
&& echo "deb [arch=$(dpkg --print-architecture) signed-by=/usr/share/keyrings/githubcli-archive-keyring.gpg] https://cli.github.com/packages stable main" | tee /etc/apt/sources.list.d/github-cli.list > /dev/null \
&& apt-get update \
&& apt-get install -y gh
# Install Claude Code
RUN npm install -g @anthropic-ai/claude-code
WORKDIR /workspace
CMD ["/bin/bash"]
</code></pre>
<!--kg-card-end: html-->
<p> <strong>docker-compose.yml</strong>:</p>
<!--kg-card-begin: html-->
<pre><code lang="yaml">
version: '3.8'
services:
sre-agent:
build: .
volumes:
- ./:/workspace
- ~/.kube:/root/.kube:ro # Kubernetes config
- ~/.config/gh:/root/.config/gh:ro # GitHub CLI auth
- ~/.config/claude:/root/.config/claude:ro # Claude auth
network_mode: host # Access to local k8s cluster
</code></pre>
<!--kg-card-end: html-->
<p></p><p><strong>run-investigation.sh</strong>:</p>
<!--kg-card-begin: html-->
<pre><code lang="bash">
#!/bin/bash
# Run agent 1 with isolated session
cat CLAUDE-receiver.md | claude -p --session-id $(uuidgen) --dangerously-skip-permissions > investigation-1.md
# Run agent 2 with isolated session
cat CLAUDE-reviewer1.md | claude -p --session-id $(uuidgen) --dangerously-skip-permissions > investigation-2.md
# Run agent 3 with isolated session
cat CLAUDE-reviewer2.md | claude -p --session-id $(uuidgen) --dangerously-skip-permissions > investigation-3.md
</code></pre>
<!--kg-card-end: html-->
<p> <strong>Usage</strong>:</p>
<!--kg-card-begin: html-->
<pre><code lang="bash">
docker-compose build
docker-compose run sre-agent bash run-investigation.sh
</code></pre>
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<p> <br>This pattern ensures each agent:</p><ul><li>Has the full context of previous work through the sequential file chain</li><li>Runs in complete isolation with unique session IDs</li><li>Has safe, unrestricted tool access within the container</li><li>Can access Kubernetes and GitHub with your local credentials</li><li>Maintains a clear audit trail of the investigation process</li></ul><p>The containerized approach provides flexibility to:</p><ul><li>Control information flow between agents through file-based communication</li><li>Expand easily with new tools or checks without affecting the host system</li><li>Reuse the structure for other workflows like CI/CD validation or automated rollbacks</li><li>Run experiments safely without risking production systems</li></ul><h2 id="experimental-results">Experimental Results</h2><p><a href="https://github.com/opsworker-ai/articles/blob/main/agent-ai-sre-team/Article.md?ref=opsworker.ai#experimental-results"></a></p><p><strong>⚠️&nbsp;CRITICAL DISCLAIMER: This is a completely sandboxed experiment. All incidents, investigations, and remediation actions occurred in an isolated test environment with no production impact.</strong></p><p>I conducted 7 documented experiments across 3 distinct incident types. All runs were successful in identifying root causes and proposing remediation actions.</p><h3 id="experiment-1-init-container-crashloopbackoff">Experiment 1: Init Container CrashLoopBackOff</h3><p><a href="https://github.com/opsworker-ai/articles/blob/main/agent-ai-sre-team/Article.md?ref=opsworker.ai#experiment-1-init-container-crashloopbackoff"></a></p><p><strong>Incident:</strong>&nbsp;Pod stuck in perpetual crash state for 8 days&nbsp;</p><p><strong>Alert:</strong>&nbsp;<code>KubePodNotReady</code>&nbsp;for&nbsp;<code>failing-init-demo-5999d7545c-sh4zk</code></p><p><strong>Investigation:</strong></p><ul><li>Agents identified hardcoded&nbsp;<code>exit 1</code>&nbsp;In the init container</li><li>Verified 4,512 restarts over 16 days</li><li>Proposed deletion as primary mitigation</li></ul><p><strong>Unexpected Discovery:</strong>&nbsp;The final reviewer discovered this was an&nbsp;<strong>intentional test deployment</strong>&nbsp;- the very test case for the investigation system itself! The agents were investigating their own test data without realizing it.</p><p><strong>Key Learning:</strong>&nbsp;Agents lack meta-awareness. They didn't question why a deployment named "failing-init-demo" existed or check project documentation. This revealed a critical gap: investigators need an explicit "context gathering" phase before diving into technical diagnosis.</p><h3 id="experiment-2-ingress-misconfiguration-http-404">Experiment 2: Ingress Misconfiguration (HTTP 404)</h3><p><a href="https://github.com/opsworker-ai/articles/blob/main/agent-ai-sre-team/Article.md?ref=opsworker.ai#experiment-2-ingress-misconfiguration-http-404"></a></p><p><strong>Incident:</strong>&nbsp;Application returning 404 errors for 11 hours&nbsp;<br>the <strong>Root Cause:</strong>&nbsp;AWS ALB configured with HTTPS on port 444 instead of the standard port 443</p><blockquote># Incorrect<br>alb.ingress.kubernetes.io/listen-ports: '[{"HTTP": 80}, {"HTTPS": 444}]'</blockquote><p><strong>Investigation Quality:</strong></p><ul><li><strong>Agent 1:</strong>&nbsp;Correctly identified the <strong>,</strong>root cause with definitive evidence</li><li><strong>Agent 2:</strong>&nbsp;Enhanced safety analysis, identified shared ALB risk affecting 5 other services</li><li><strong>Agent 3:</strong>&nbsp;Authorized auto-remediation with comprehensive monitoring requirements</li></ul><p><strong>Output:</strong>&nbsp;Final investigation (Agent 3) was 871 lines of comprehensive incident analysis,<strong>a </strong> including mitigation plan, rollback procedures, monitoring requirements, and long-term prevention strategies (Kyverno policy recommendations).</p><h3 id="experiments-3-7-database-configuration-error-series">Experiments 3-7: Database Configuration Error Series</h3><p><a href="https://github.com/opsworker-ai/articles/blob/main/agent-ai-sre-team/Article.md?ref=opsworker.ai#experiments-3-7-database-configuration-error-series"></a></p><p><strong>Incident Type:</strong>&nbsp;Application crash due to a non-existent database&nbsp;<br><strong>Root Cause:</strong>&nbsp;Database name changed to&nbsp;<code>evershop-update-2025</code>&nbsp;which doesn't exist</p><p>These experiments tested different system capabilities:</p><p><strong>Experiment 3:</strong>&nbsp;Investigation without GitHub MCP access</p><ul><li>Agents identified the crash but couldn't trace it to the source code changes</li><li>Demonstrated the critical importance of repository access</li></ul><p><strong>Experiment 4:</strong>&nbsp;JSONL output format testing</p><ul><li>Revealed excessive verbosity (large, hard-to-parse files)</li><li>Led to format simplification</li></ul><p><strong>Experiment 5:</strong>&nbsp;Full investigation with all access</p><ul><li>Successfully traced the issue to the commit&nbsp;<code>39920dac</code>&nbsp;made 11 minutes before the alert</li><li>Created remediation PR</li><li>99% confidence in root cause</li></ul><p>Timeline correlation:</p><pre><code>09:14:34 UTC - Commit merged
09:24:26 UTC - Deployment with bad config
09:25:25 UTC - Alert fires
6+ hours - Service down, 75 pod restarts
</code></pre><p><strong>Experiment 6:</strong>&nbsp;Action logging improvement</p><ul><li>The investigation was correct, but lacked documentation of HOW agents investigated</li><li>Led to prompt changes requiring explicit "Investigation Actions" sections</li><li>Now, agents document: "Ran PostgreSQL pod via Kubernetes MCP to verify databases" instead of just "Database verified."</li></ul><p><strong>Experiment 7:</strong>&nbsp;Silent failure case</p><ul><li><strong>Critical reliability issue:</strong>&nbsp;Agent 2 (Reviewer 1) produced only a newline, no investigation output</li><li>Agent 1 successfully created PR #6 and wrote a complete investigation (23 lines)</li><li>Agent 2 failed silently - output file contained only 1 byte (newline character)</li><li>Agent 3 ran automatically, but only had Agent 1's output available (Agent 2's was empty)</li><li>Agent 3 still correctly reviewed PR #6 and provided a valid assessment</li><li>A 4th agent was run manually to verify the investigation chain</li><li><strong>Root cause:</strong>&nbsp;Agent 2 executed but produced no text output (silent failure)</li><li><strong>Impact:</strong>&nbsp;Subsequent agents in the pipeline lacked intermediate review, though the final result was still valid</li><li><strong>Lessons:</strong>&nbsp;Identified need for output validation, real-time visibility, and fail-fast behavior</li></ul><h2 id="system-evolution">System Evolution</h2><p><a href="https://github.com/opsworker-ai/articles/blob/main/agent-ai-sre-team/Article.md?ref=opsworker.ai#system-evolution"></a></p><p>Through these experiments, the system evolved significantly:</p><h3 id="output-format-evolution">Output Format Evolution</h3><p><a href="https://github.com/opsworker-ai/articles/blob/main/agent-ai-sre-team/Article.md?ref=opsworker.ai#output-format-evolution"></a></p><p><strong>Initial (Experiments 1-4):</strong>&nbsp;7-section verbose format</p><blockquote>1. Incident Summary<br>2. Investigation Findings<br>3. Root Cause<br>4. Immediate Mitigation<br>5. Long-term Prevention<br>6. Monitoring Plan<br>7. Pull Requests Links</blockquote><p><strong>Current (Experiments 6-7):</strong>&nbsp;Concise, action-focused format</p><blockquote>1. Investigation Actions (tools/methods used)<br>2. Root Cause (with file:line references)<br>3. Immediate Actions (critical steps only)<br>4. PR Links</blockquote><p><strong>Current State:</strong>&nbsp;The sandbox script remains simple for experimentation. Production implementation would include these safety features.</p><h2 id="key-findings">Key Findings</h2><p><a href="https://github.com/opsworker-ai/articles/blob/main/agent-ai-sre-team/Article.md?ref=opsworker.ai#key-findings"></a></p><h3 id="investigations">investigation's</h3><p><a href="https://github.com/opsworker-ai/articles/blob/main/agent-ai-sre-team/Article.md?ref=opsworker.ai#what-worked-"></a></p><ol><li><strong>Root Cause Identification:</strong>&nbsp;100% success rate across all experiments</li><li><strong>Tool Proficiency:</strong>&nbsp;Excellent use of Kubernetes and GitHub APIs</li><li><strong>Multi-Agent Value:</strong>&nbsp;Peer review consistently improved quality and safety</li><li><strong>Autonomous Remediation:</strong>&nbsp;Successfully created appropriate PRs with fixes</li><li><strong>Evidence-Based Analysis:</strong>&nbsp;Strong technical reasoning with specific code references</li></ol><h3 id="critical-issues-%E2%9A%A0%EF%B8%8F">Critical Issues&nbsp;⚠️</h3><p><a href="https://github.com/opsworker-ai/articles/blob/main/agent-ai-sre-team/Article.md?ref=opsworker.ai#critical-issues-%EF%B8%8F"></a></p><ol><li><strong>Silent Failures:</strong>&nbsp;Agents can complete work without producing the required outputs</li><li><strong>Meta-Context Blindness:</strong>&nbsp;Don't question the investigation's purpose or check documentation</li><li><strong>Verbosity Control:</strong>&nbsp;Initial formats consumed excessive tokens</li><li><strong>Process Transparency:</strong>&nbsp;Agents didn't document their investigation methods</li></ol><h2 id="production-readiness-gap">Production Readiness Gap</h2><p><a href="https://github.com/opsworker-ai/articles/blob/main/agent-ai-sre-team/Article.md?ref=opsworker.ai#production-readiness-gap"></a></p><p><strong>This is a sandbox experiment.</strong>&nbsp;Significant engineering work remains for production deployment:</p><h3 id="missing-components">Missing Components</h3><p><a href="https://github.com/opsworker-ai/articles/blob/main/agent-ai-sre-team/Article.md?ref=opsworker.ai#missing-components"></a></p><p><strong>Security:</strong></p><ul><li>No authentication/authorization layer</li><li>No audit logging</li><li>Kubernetes access is cluster-admin (overly permissive)</li></ul><p><strong>Safety:</strong></p><ul><li>No dry-run mode</li><li>No blast radius analysis</li><li>No automated rollback</li><li>No rate limiting or circuit breakers</li></ul><p><strong>Integration:</strong></p><ul><li>No PagerDuty/incident management integration</li><li>No Slack notifications</li><li>No metrics/observability for agent performance</li><li>No SLA tracking</li></ul><p><strong>Reliability:</strong></p><ul><li>No retry logic for transient failures</li><li>No concurrency control (parallel alerts)</li><li>No queue management</li><li>No cost controls (unlimited API calls)</li><li>No timeout handling</li></ul><p><strong>Operations:</strong></p><ul><li>No runbooks for agent failures</li><li>No monitoring of agent health</li><li>No disaster recovery plan</li><li>No multi-tenancy support</li></ul><h3 id="cost-analysis-estimated">Cost Analysis (Estimated)</h3><p><a href="https://github.com/opsworker-ai/articles/blob/main/agent-ai-sre-team/Article.md?ref=opsworker.ai#cost-analysis-estimated"></a></p><p><strong>Per-Investigation Estimate</strong>&nbsp;(using Claude Sonnet-class pricing):</p><ul><li>Simple incidents: ~50-80K tokens total across 3 agents</li><li>Complex incidents: ~100-150K tokens total across 3 agents</li><li><strong>Actual cost varies by model</strong>&nbsp;(Haiku: $0.10-0.30, Sonnet: $0.50-2.00, Opus: $5-15 per investigation)</li></ul><p><strong>Sandbox Experience:</strong></p><ul><li>This experiment used Claude Sonnet (mid-tier pricing)</li><li>Token usage averaged 40-100K per full investigation cycle</li><li>Cost per investigation: Estimated $1-5 (sandbox scale)</li></ul><p><strong>At Scale Estimate:</strong></p><ul><li>100 high-priority alerts/day with automated investigation</li><li>Using hybrid model (Haiku for Agent 1, Sonnet for Agent 3)</li><li>Estimated:&nbsp;<strong>$10-50K/month</strong>&nbsp;depending on alert volume and model selection</li><li><strong>Critical:</strong>&nbsp;Aggressive filtering needed to investigate only high-value alerts</li></ul><h3 id="timeline-to-production">Timeline to Production</h3><p><a href="https://github.com/opsworker-ai/articles/blob/main/agent-ai-sre-team/Article.md?ref=opsworker.ai#timeline-to-production"></a></p><p><strong>Estimated effort:</strong>&nbsp;6-12 months with a dedicated team</p><ol><li><strong>Months 1-2:</strong>&nbsp;Security hardening, authentication, authorization</li><li><strong>Months 3-4:</strong>&nbsp;Production integrations (PagerDuty, Slack, metrics)</li><li><strong>Months 5-6:</strong>&nbsp;Reliability engineering (retries, timeouts, queuing)</li><li><strong>Months 7-8:</strong>&nbsp;Cost controls and optimization</li><li><strong>Months 9-10:</strong>&nbsp;Multi-tenancy and scale testing</li><li><strong>Months 11-12:</strong>&nbsp;Beta program with read-only investigations</li></ol><h2 id="statistical-summary">Statistical Summary</h2><p><a href="https://github.com/opsworker-ai/articles/blob/main/agent-ai-sre-team/Article.md?ref=opsworker.ai#statistical-summary"></a></p>
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<table style="box-sizing: border-box; border-spacing: 0px; border-collapse: collapse; margin-top: 0px; margin-bottom: 16px; display: block; width: max-content; max-width: 100%; overflow: auto; font-variant: tabular-nums;"><thead style="box-sizing: border-box;"><tr style="box-sizing: border-box; background-color: rgb(255, 255, 255); border-top: 1px solid rgba(209, 217, 224, 0.7);"><th style="box-sizing: border-box; padding: 6px 13px; font-weight: 600; border-color: rgb(209, 217, 224); border-style: solid; border-width: 1px; border-image: none 100% / 1 / 0 stretch;">Metric</th><th style="box-sizing: border-box; padding: 6px 13px; font-weight: 600; border-color: rgb(209, 217, 224); border-style: solid; border-width: 1px; border-image: none 100% / 1 / 0 stretch;">Value</th></tr></thead><tbody style="box-sizing: border-box;"><tr style="box-sizing: border-box; background-color: rgb(255, 255, 255); border-top: 1px solid rgba(209, 217, 224, 0.7);"><td style="box-sizing: border-box; padding: 6px 13px; border-color: rgb(209, 217, 224); border-style: solid; border-width: 1px; border-image: none 100% / 1 / 0 stretch;">Total Experiments</td><td style="box-sizing: border-box; padding: 6px 13px; border-color: rgb(209, 217, 224); border-style: solid; border-width: 1px; border-image: none 100% / 1 / 0 stretch;">7</td></tr><tr style="box-sizing: border-box; background-color: rgb(246, 248, 250); border-top: 1px solid rgba(209, 217, 224, 0.7);"><td style="box-sizing: border-box; padding: 6px 13px; border-color: rgb(209, 217, 224); border-style: solid; border-width: 1px; border-image: none 100% / 1 / 0 stretch;">Unique Incident Types</td><td style="box-sizing: border-box; padding: 6px 13px; border-color: rgb(209, 217, 224); border-style: solid; border-width: 1px; border-image: none 100% / 1 / 0 stretch;">3</td></tr><tr style="box-sizing: border-box; background-color: rgb(255, 255, 255); border-top: 1px solid rgba(209, 217, 224, 0.7);"><td style="box-sizing: border-box; padding: 6px 13px; border-color: rgb(209, 217, 224); border-style: solid; border-width: 1px; border-image: none 100% / 1 / 0 stretch;">Root Cause Success Rate</td><td style="box-sizing: border-box; padding: 6px 13px; border-color: rgb(209, 217, 224); border-style: solid; border-width: 1px; border-image: none 100% / 1 / 0 stretch;">100%</td></tr><tr style="box-sizing: border-box; background-color: rgb(246, 248, 250); border-top: 1px solid rgba(209, 217, 224, 0.7);"><td style="box-sizing: border-box; padding: 6px 13px; border-color: rgb(209, 217, 224); border-style: solid; border-width: 1px; border-image: none 100% / 1 / 0 stretch;">PRs Created by Agents</td><td style="box-sizing: border-box; padding: 6px 13px; border-color: rgb(209, 217, 224); border-style: solid; border-width: 1px; border-image: none 100% / 1 / 0 stretch;">3+ (verified: #2, #5, #6)</td></tr><tr style="box-sizing: border-box; background-color: rgb(255, 255, 255); border-top: 1px solid rgba(209, 217, 224, 0.7);"><td style="box-sizing: border-box; padding: 6px 13px; border-color: rgb(209, 217, 224); border-style: solid; border-width: 1px; border-image: none 100% / 1 / 0 stretch;">Total Investigation Output</td><td style="box-sizing: border-box; padding: 6px 13px; border-color: rgb(209, 217, 224); border-style: solid; border-width: 1px; border-image: none 100% / 1 / 0 stretch;">4,333 lines</td></tr><tr style="box-sizing: border-box; background-color: rgb(246, 248, 250); border-top: 1px solid rgba(209, 217, 224, 0.7);"><td style="box-sizing: border-box; padding: 6px 13px; border-color: rgb(209, 217, 224); border-style: solid; border-width: 1px; border-image: none 100% / 1 / 0 stretch;">Avg Investigation Time</td><td style="box-sizing: border-box; padding: 6px 13px; border-color: rgb(209, 217, 224); border-style: solid; border-width: 1px; border-image: none 100% / 1 / 0 stretch;">2-6 hours</td></tr><tr style="box-sizing: border-box; background-color: rgb(255, 255, 255); border-top: 1px solid rgba(209, 217, 224, 0.7);"><td style="box-sizing: border-box; padding: 6px 13px; border-color: rgb(209, 217, 224); border-style: solid; border-width: 1px; border-image: none 100% / 1 / 0 stretch;">Silent Failures Discovered</td><td style="box-sizing: border-box; padding: 6px 13px; border-color: rgb(209, 217, 224); border-style: solid; border-width: 1px; border-image: none 100% / 1 / 0 stretch;">1</td></tr><tr style="box-sizing: border-box; background-color: rgb(246, 248, 250); border-top: 1px solid rgba(209, 217, 224, 0.7);"><td style="box-sizing: border-box; padding: 6px 13px; border-color: rgb(209, 217, 224); border-style: solid; border-width: 1px; border-image: none 100% / 1 / 0 stretch;">Format Iterations</td><td style="box-sizing: border-box; padding: 6px 13px; border-color: rgb(209, 217, 224); border-style: solid; border-width: 1px; border-image: none 100% / 1 / 0 stretch;">3</td></tr></tbody></table>
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<h3 id="conclusion">Conclusion</h3><p>This experiment set out to answer a simple question: can a multi-agent AI system actually investigate a Kubernetes incident the way an experienced on-call engineer would - end to end, with real cluster access, under realistic conditions? The results were more encouraging than we expected, and also more revealing about where the hard limits currently sit.</p><p>Across the scenarios we ran, the multi-agent system demonstrated genuine autonomous capability in five distinct areas:</p><p>✅ <strong>Investigating Kubernetes incidents with real cluster access</strong> - the agents did not work from static logs or pre-packaged data. They queried the live cluster, navigated topology, and gathered evidence the way an engineer would during an active incident.</p><p>✅ <strong>Identifying root causes with 99-100% confidence</strong> - when the agents converged on a root cause, the confidence scores were not inflated or hand-wavy. The reasoning chains were traceable, the evidence was specific, and in our test scenarios the conclusions matched what a human investigator would have found.</p><p>✅ <strong>Creating pull requests with appropriate fixes</strong> - beyond diagnosis, the system produced concrete, scoped remediation - not generic suggestions, but PRs that addressed the specific configuration or code issue identified during investigation.</p><p>✅ <strong>Providing valuable peer review and safety validation</strong> - one of the more interesting findings was that a second agent reviewing the first agent's conclusions caught issues and added genuine value, similar to how a second engineer reviewing a postmortem catches things the original investigator missed.</p><p>✅ <strong>Making escalation decisions</strong> - the system did not treat every incident as solvable by itself. In several cases it correctly identified that a finding required human judgment and escalated rather than pushing forward with a low-confidence action.</p><p>Taken together, these results show that the building blocks of agent-driven SRE investigation are real and working today, not theoretical.</p><p><strong>However, this is nowhere near production-ready.</strong></p><p>The gap between "the agents did the right thing in this test" and "we trust this to run unsupervised against our production clusters" is significant - and it is not a gap that closes with better prompts or a few more iterations. It closes through the kind of infrastructure, governance, and evaluation work that does not show up in a demo: permission boundaries, audit trails, reproducibility guarantees, handling of edge cases that did not appear in our test scenarios, and - critically - enough production track record to know how the system behaves when something genuinely novel happens.</p><p>The honest takeaway is not "this works" or "this doesn't work." It is that the core reasoning capability is further along than most skeptics assume, and the path to production is further than most vendors are willing to admit. Both of those things are true at the same time, and that is exactly the gap we are building OpsWorker to close.</p><hr><h2 id="appendix-experiment-files">Appendix: Experiment Files</h2><p><a href="https://github.com/opsworker-ai/articles/blob/main/agent-ai-sre-team/Article.md?ref=opsworker.ai#appendix-experiment-files"></a></p><p>All 7 experiments are documented in&nbsp;<a href="https://github.com/opsworker-ai/agentic-sre-investigations-poc/snapshots?ref=opsworker.ai" rel="noreferrer"><code>snapshots/</code></a>:</p><pre><code>snapshots/
├── 1-init-container-crashloop/ # Intentional test case discovery
├── 2-wrong-port-ingress-alb/ # HTTP 404 due to port 444
├── 3-wrong-db-missing-mcp/ # Investigation without GitHub access
├── 4-wrong-db-jsonl-missing-mcp/ # Format testing
├── 5-wrong-db-JSONL/ # Full successful investigation
├── 6-good-but-not-enough-actions-logs/ # Action logging improvement
└── 7-missing-review1-text-outputs/ # Silent failure case
</code></pre><p>Each directory contains:</p><ul><li><code>alert.json</code>&nbsp;- Original Prometheus alert</li><li><code>investigation-{1,2,3}.md</code>&nbsp;- Agent outputs</li><li>Supporting files (backups, PR references)</li></ul><p><strong>Github Repository URL: </strong> <a href="https://github.com/opsworker-ai/agentic-sre-investigations-poc?ref=opsworker.ai">github.com/opsworker-ai/agentic-sre-investigations-poc</a></p></div>
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<p>🚀 Founder @ OpsWorker.ai | 25+ Years in Platform Engineering, Cloud, and DevOps | Building the AI SRE teammate that fixes incidents before engineers get paged. Today, I’m channeling that experience into OpsWorker.ai</p>
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