The latest articles on DEV Community by milan (@milancharan). https://dev.to/milancharan https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F3999056%2F9281888d-fa47-4c25-9d36-61a979d9be7b.png https://dev.to/milancharan en milan Tue, 23 Jun 2026 16:16:47 +0000 https://dev.to/milancharan/what-happens-when-your-ai-agent-gets-stuck-in-production-3327 https://dev.to/milancharan/what-happens-when-your-ai-agent-gets-stuck-in-production-3327 <p>The most expensive AI agent failures I've seen weren't model failures.</p> <p>They were silent failures.</p> <p>The agent looked healthy. The workflow was still running. Tokens were still being consumed.</p> <p>But the agent had already stopped making meaningful progress.</p> <p>Over time I ran into the same production issues repeatedly:</p> <ul> <li>Infinite loops</li> <li>Retry storms</li> <li>Silent stalls</li> <li>Tool failures hidden behind successful responses</li> <li>Agents drifting away from the original goal</li> <li>No visibility into what the agent was actually doing</li> </ul> <p>A better prompt never fixed these problems.</p> <p>The solution ended up being a runtime supervision layer around the agents rather than more workflow logic.</p> <h2> The Problem </h2> <p>Most agent frameworks focus on getting agents to run.</p> <p>Production teams care about different questions:</p> <ul> <li>Why is this execution stuck?</li> <li>Is it still making progress?</li> <li>Can I safely pause it?</li> <li>Can I resume it later?</li> <li>Should I terminate it entirely?</li> </ul> <p>Those questions become difficult when the runtime only exposes logs.</p> <h2> Runtime Supervision </h2> <p>One design decision that worked well was separating supervision from agent logic.</p> <p>Instead of embedding every guardrail directly inside the workflow graph, a dedicated runtime layer observes execution and enforces operational rules.</p> <p>This keeps agent workflows simple while allowing supervision logic to evolve independently.</p> <p>The runtime is responsible for:</p> <ul> <li>Loop detection</li> <li>Retry management</li> <li>Budget enforcement</li> <li>Pause and resume operations</li> <li>Execution checkpoints</li> <li>Stop reason classification</li> <li>Live telemetry</li> </ul> <p>The result is a system where operational concerns can change without requiring modifications to agent behavior.</p> <h2> Explicit Stop Reasons </h2> <p>One lesson I learned quickly:</p> <p>"Failed" is not a useful status.</p> <p>Execution stops should explain themselves.</p> <p>Examples:</p> <ul> <li>LOOP_DETECTED</li> <li>BUDGET_EXCEEDED</li> <li>RETRY_LIMIT_REACHED</li> <li>TOOL_FAILURE</li> <li>TIMEOUT</li> <li>USER_PAUSED</li> <li>USER_KILLED</li> </ul> <p>The recovery path depends on why the execution stopped.</p> <p>Without that information operators are forced to guess.</p> <h2> Semantic Loop Detection </h2> <p>Most loop detection implementations use step counts.</p> <p>The problem is that agents can make progress on the wrong objective without technically looping.</p> <p>An execution might spend twenty steps confidently pursuing a plan that diverged from the original goal.</p> <p>What worked better was periodically asking:</p> <p>"Are we meaningfully closer to the goal than we were several steps ago?"</p> <p>This catches drift before it becomes expensive.</p> <h2> Pause vs Kill </h2> <p>These are not the same operation.</p> <h3> Pause </h3> <p>Pause preserves execution state.</p> <p>Execution stops, but the runtime keeps the latest checkpoint.</p> <p>Resume simply loads the last committed state and continues.</p> <h3> Kill </h3> <p>Kill terminates execution completely.</p> <p>Active state is removed and the execution cannot continue.</p> <p>The distinction becomes important when debugging long-running workflows.</p> <h2> Checkpoint Before Action </h2> <p>Before every external action:</p> <ul> <li>API calls</li> <li>Browser interactions</li> <li>Email delivery</li> <li>Database writes</li> </ul> <p>the runtime creates a checkpoint.</p> <p>Successful execution clears the checkpoint.</p> <p>If the process crashes, the next execution immediately knows what was in flight.</p> <p>This turned silent failures into recoverable failures.</p> <h2> Retry Storm Protection </h2> <p>One failed dependency can create thousands of wasted requests.</p> <p>The pattern that worked best was:</p> <ul> <li>Exponential backoff</li> <li>Retry budgets</li> <li>Circuit breakers</li> </ul> <p>Without all three, agents tend to fail repeatedly and burn tokens while making no progress.</p> <h2> Live Telemetry </h2> <p>Logs tell you what happened.</p> <p>Operators usually need to know what is happening right now.</p> <p>The runtime continuously tracks:</p> <ul> <li>Current task</li> <li>Current step</li> <li>Active tool</li> <li>Execution status</li> <li>Recent transitions</li> </ul> <p>The goal is to make agent execution observable while it is running, not after the incident has already happened.</p> <h2> Final Thoughts </h2> <p>Building AI agents is becoming easier every month.</p> <p>Building agents that can survive production failures is still difficult.</p> <p>The most important lesson I learned is that reliability problems usually appear outside the model.</p> <p>They appear in retries, checkpoints, tool failures, execution control, and supervision.</p> <p>What has been the hardest production failure you've encountered while running AI agents?</p> ai devops opensource machinelearning