If an AI agent starts performing worse after an update, compare the new release with a known-good version, roll back through your deployment platform if the regression is confirmed, and rerun the same evaluations to verify recovery. A rollback restores software or configuration; it does not automatically undo emails, database changes, or other actions the agent already completed.
What to do first
If the regression could cause material harm, limit further exposure using the traffic controls, disablement options, or incident procedure available in your platform. There is no universal control or threshold: choose the response based on the agent’s impact and your system’s capabilities.
Before changing anything else, identify which release is running and what changed. Record the versions of the prompt, model, tools, routing, guardrails, and relevant configuration. Then inspect traces around the point where behavior changed. A trace can capture model calls, tool calls, guardrails, and handoffs, helping you locate the failing step rather than treating every bad outcome as a model problem. OpenAI’s agent evaluation guide describes using traces to inspect workflows and investigate failures.
Confirm that the update caused a regression
Compare the candidate release with the last validated baseline using representative tasks and repeatable evaluations. Judge the result by whether the agent achieved the intended outcome—not merely whether it returned without an error.
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- Task outcome: Did the agent meet the user’s goal according to a reference answer or explicit acceptance criteria?
- Workflow behavior: Did it choose appropriate tools, make the right handoffs, and follow required guardrails? OpenAI’s trace-grading guide describes evaluating these kinds of workflow decisions.
- Consistency: Did the new version fail cases the baseline handled? LangSmith’s evaluation documentation describes regression testing as a way to check consistency across application versions and compare improvements with regressions.
- Operational impact: Where relevant, compare latency, cost, and failure severity. Set thresholds for your workload; the available guidance does not establish universal limits.
Use a representative evaluation dataset, not just the single example that triggered concern. Repeating the same cases against both versions makes the comparison more useful and gives you a test suite for verifying a rollback.
Roll back to the last validated version
Once you have confirmed the regression, use your deployment system’s documented release mechanism to redeploy or switch traffic to the previously validated version. Keep that known-good release available so you can restore it without reconstructing its settings from memory.
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The exact procedure depends on the platform. For a model change in Copilot Studio, Microsoft identifies redeploying the previously validated solution version as the rollback path; that guidance applies to its model-migration context, not every AI-agent platform. Microsoft’s Copilot Studio model migration guidance also notes that this path is slower than a configuration toggle. LangChain describes deployment versioning and rollback as LangSmith platform capabilities, but exact availability and steps depend on the product and account configuration. See LangSmith’s platform information.
There is no universal rollback command or standard recovery time. Follow your platform’s current deployment instructions, and confirm that the version you are restoring includes all the settings needed for the intended baseline.
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Verify that performance recovered
After the rollback, rerun the same evaluation cases against the restored version. Compare outcomes with the known-good baseline, then inspect fresh traces and relevant production quality signals. A deployment that starts successfully is not, by itself, proof that task quality has recovered. OpenAI describes trace grading as a way to benchmark changes, identify regressions, and validate improvements; LangSmith documents both offline evaluation and online production evaluation.
Once the incident is contained, add representative failures to the regression dataset. That lets you check future model, prompt, routing, or tool changes against behavior that has already caused trouble.
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Handle actions already taken as a separate incident
Restoring an older release changes the running software or configuration; it does not establish that external side effects have been reversed. Review the degraded release’s action logs and the affected systems for messages sent, records changed, or other completed actions. Decide whether each requires a correction, compensation, or human review using the appropriate procedure for that system. There is no universal method for undoing these actions.
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Before releasing another change, preserve a known-good version and evaluate the candidate against the same representative dataset. Include expected tool use, handoffs, guardrails, and end results, then monitor production behavior after deployment. Define workload-specific thresholds for quality and operational impact rather than relying on a generic rollback rule.
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