The Converra blog
How to evaluate, observe, and actually fix production AI agents — with the honest tool rankings, not vendor spin.
New here? Start with what AI agent optimization is, from failure to verified fix.
Agent optimization
Turning production failures into shipped fixes, and keeping the loop running.
How to Fix an AI Agent's Tool Descriptions Without Guessing
Find wrong-tool calls, change one tool description, compare the candidate on the same tasks, then verify the behavior after an approved release.
Read itEvery Self-Improving Agent Loop in 2026 Stops at the Pull Request
LangSmith Engine, Arize Signal, Braintrust Loop, and Galileo Autotune all stop at a proposal a human must validate. What a loop needs to close, with sources.
Read itHow to Find the AI Agent Failures Your Evals Miss and Prove the Fix
Evals test what you anticipated; production finds the rest. A six-step method to find the failures your suite misses, fix them, and prove it on live traffic.
Read itHow to Turn Production Agent Traces Into Improvements
A practical workflow for turning AI agent traces into shipped improvements: diagnose at the step, count patterns, prioritize, make one bounded change, verify.
Read itContext Engineering After Launch: An Operations Guide
Manage production AI agent context with traceable layers, isolated changes, separate retrieval tests, controlled rollout, and fresh production evidence.
Read itWithout Production Verification, an AI Agent Improvement Loop Is Still a Hypothesis
AI agent improvement is easy to demo and hard to verify. The missing contracts between a proposed fix, deployment, production evidence, and the next iteration.
Read itWhy AI Companies Keep Humans in the Loop
Human oversight is not the opposite of AI autonomy. OpenAI, Anthropic, Microsoft, and AWS show how risk-tiered approval lets agents safely do more work.
Read itVerification and reliability
Proving a fix worked, measuring reliability, and handling regressions after changes.
When an AI Agent Fix's Expected Impact Becomes a Measured Result
Turn an estimated agent improvement into a tested fix and a production verdict. Separate forecasts, simulation results, release evidence and live outcomes.
Read itPrompt CI/CD That Ends in a Production Verdict, Not a Green Check
A prompt CI/CD pipeline for AI agents: version, regression-gate, compare head-to-head, deploy with a marker, then measure the merged change on live traffic.
Read itAI Agent Incident Response Runbook: From Alert to Verified Fix
A nine-step AI agent incident runbook: triage, freeze, diagnose at the step, contain, fix one layer, test, ship, verify on live traffic, close on a verdict.
Read itHow to Migrate a Production Agent from Claude Sonnet 4.5
Claude Sonnet 4.5 retires November 30, 2026. The API changes that break agents on Sonnet 5.5, the behavior to re-test, and a step-by-step migration plan.
Read itHow to Verify an AI Agent Fix After Deployment
Verify an AI agent fix on live traffic: fix the baseline, mark the release, compare like with like, check confounders and regressions, and read the verdict.
Read itAI Agent Incident Postmortem Template: What to Record After a Failure
Use this AI agent incident postmortem template to preserve impact, execution lineage, causal evidence, corrective action, and the production verdict.
Read itAI Agent Reliability Is Not One Score: A Practical Scorecard
Measure AI agent reliability across outcomes, behavior, safety, latency, recovery, and evaluator quality with explicit formulas and gates.
Read itHow to Migrate a Production AI Agent to GPT-5.6
A production-focused GPT-5.6 migration guide covering model tiers, reasoning, tools, caching, paired tests, rollback, and production verification.
Read itWhy AI Agents Regress After Prompt or Model Changes
Why AI agents regress after a prompt edit or model upgrade, the failures to recognize, and a practical process to find and fix the cause before users do.
Read itEvaluation and testing
Evaluation harnesses, regression suites, simulation, and LLM judges.
Jev as a Judge: What a Decision Model Changes for Production Verdicts
Jev is a typed decision model, not an LLM. Here is what it changes for judging agent failures at production scale, and why a judge label is still not a verdict.
Read itHow to Build an AI Agent Evaluation Harness That Survives Production
Build a reproducible AI agent evaluation harness around real failures, complete trajectories, calibrated graders, matched comparisons, and production feedback.
Read itHow to Build an AI Agent Regression Suite From Production Traces
Turn production AI agent failures into reviewed, minimized regression cases with clean controls, versioned evidence, and matched replay gates.
Read itHow Many Simulated Conversations Do You Need? Stop Looking for a Magic Number.
There is no universal simulation count. Size AI agent tests around the decision, effect, variance, rare failures, coverage, and acceptable risk.
Read itYour LLM Evals Passed. Why Did the Agent Still Fail in Production?
A green LLM eval is bounded test evidence, not production proof. Six reasons agents still fail after passing—and the evidence needed to close the loop.
Read itYour LLM Judge Is Not Ground Truth
An LLM judge is a measurement instrument, not ground truth. How to measure precision and recall, expose bias, calibrate with humans, and monitor drift.
Read itHow Converra Evaluates AI Agent Performance
Converra does not trust one universal agent score. It combines customer outcomes, paired simulation, regression gates, and production verdicts.
Read itTool rankings
Honest rankings of the tools for evaluating, observing, and fixing agents.
Arize Signal vs LangSmith Engine vs Braintrust Loop vs Converra (2026)
A side-by-side of four agent improvement loops: what each reads, produces, tests before handoff, deploys, and measures after the ship. Reviewed October 2026.
Read itLangSmith Alternatives (2026): 7 Tools Compared
Seven LangSmith alternatives compared from vendor docs: Langfuse, Braintrust, Phoenix, Opik, Weave, Datadog, and Converra. What each does and who it fits.
Read itLangfuse Alternatives (2026): 7 Tools Compared
Seven Langfuse alternatives compared from vendor docs: Opik, Phoenix, LangSmith, Braintrust, Weave, Datadog, and Converra. What each does and who it fits.
Read itBest Tools to Fix Production AI Agents (2026)
Most agent tools diagnose; few fix. The 2026 ranking of tools that actually change production agent behavior — autonomous loops, optimizers, coding agents.
Read itBest AI Agent Evaluation Tools (2026)
The honest 2026 ranking of AI agent evaluation tools — LangSmith, Braintrust, Galileo, Arize, Patronus, Opik — plus the layer that acts on what they measure.
Read itBest AI Agent Observability Tools (2026)
The 2026 ranking of AI agent observability tools — LangSmith, Langfuse, Arize, Opik, Datadog — plus the layer that turns what you see into a shipped fix.
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