Evaluation tools score your agent. They grade outputs, track quality over time, and flag regressions — so you know, with numbers, when something broke. Every team running agents in production needs one.
The 2026 boundary is no longer ‘tools score; humans do everything else.’ Braintrust Loop can analyze traces and suggest prompt improvements inside playgrounds. Amazon Bedrock AgentCore can recommend prompt and tool changes, batch-evaluate them, and split live traffic for A/B tests. The practical distinction is how much of diagnosis, rollout, rollback, and post-change judgment the platform owns.
The short version
Pick based on how far you want the platform to go: scoring only, assisted optimization, controlled production testing, or an autonomous loop that deploys a winner and returns a durable verdict.
What evaluation tools do — and how far they now go
An AI agent evaluation tool runs your agent's outputs through scorers — LLM-as-judge, heuristics, or human review — and turns behavior into metrics: faithfulness, relevance, task completion, tone, safety. The good ones version your datasets, catch regressions before deploy, and let you trace a bad score back to the turn that caused it.
Capabilities now overlap. Braintrust Loop can inspect logs, generate scorers, and suggest prompt improvements from playground annotations. Amazon Bedrock AgentCore can turn production traces and evaluator output into recommendations, batch-test proposed changes, and run controlled comparisons on live traffic. Neither should be described as scoring only. The useful comparison is ownership: who proposes the change, tests it, governs deployment and rollback, and decides whether production behavior actually improved.
The autonomous improvement loop — diagnoses the failing step, generates a fix, simulation-tests it, deploys it, and verifies it worked on real production traffic.
Best for: teams who want failing scores turned into shipped, verified fixes — not another dashboard to read.
Closes the loop evaluation leaves open: diagnose → fix → test → deploy → verdict
Every shipped fix gets a production verdict: verified, not fixed, or confounded
Head-to-head simulation against synthetic personas before anything reaches production
No eval dataset required to start — it learns failure patterns from real traffic
Not a pure scoring/monitoring tool — if you only want metrics and dashboards, an eval tool is the lighter fit
Managed agent platform with recommendations from production traces, batch evaluation, and live-traffic A/B testing.
Best for: AWS teams that want an integrated observe-evaluate-optimize-test workflow.
Recommendations use production traces and evaluator output to suggest prompt and tool-description changes
Batch evaluations test recommended changes against defined datasets before rollout
A/B testing compares agent versions by splitting live production traffic
Broader AWS agent platform rather than a lightweight standalone evaluation tool
Teams still configure evaluation data, traffic allocation, rollout policy, and promotion or rollback decisions
Beyond evaluation: closing the loop
Once a score drops, the work moves from measurement to change management. Some platforms now assist prompt optimization; AgentCore can also batch-test recommendations and run controlled live-traffic comparisons. The remaining question is whether those capabilities form one governed path from diagnosis through deployment, rollback, and an explicit outcome decision — or a set of steps your team assembles and operates.
Converra is designed to own that sequence as one managed loop. It pinpoints the step and turn where a conversation broke, generates targeted prompt variants, runs them head-to-head against synthetic personas derived from real traffic, deploys the winner with instant rollback, and measures before/after failure rates on live conversations — marking each fix verified, not fixed, or confounded.
On Salespeak's orchestrator agent, that loop eliminated 100% of hallucinated pricing and infrastructure claims and cut routing failures 74%, verified across production traffic, with zero engineering hours spent generating or testing the fixes. Use an eval tool to measure. Use Converra to make the number move.
Frequently asked questions
What is an AI agent evaluation tool?
An AI agent evaluation tool scores your agent's outputs against quality criteria — faithfulness, relevance, task completion, safety — so you can track behavior over time and catch regressions before they reach users. Traditional evaluators stop at measurement; newer platforms may also assist optimization or controlled testing.
Which AI agent evaluation tool should I use?
Choose based on your stack and how much workflow ownership you want: LangSmith for LangChain, Langfuse or Opik for open source, Braintrust for assisted prompt experimentation, AgentCore for an AWS-native performance loop, Galileo or Patronus for safety, and Arize for ML observability.
What do I do after evaluation flags a problem?
After evaluation flags a problem, determine which steps your platform already owns: diagnosis, change recommendation, regression testing, live comparison, deployment, rollback, and outcome verification. Converra is built to automate that full sequence and return a production verdict on each fix.
Is Converra an evaluation tool?
No. Converra is the autonomous improvement loop that acts on what evaluation measures — it diagnoses, generates and simulation-tests a fix, deploys it, and verifies the result on real traffic. Evaluation tools tell you what's wrong; Converra ships the fix and proves it worked.
Can I use Converra alongside my evaluation tool?
Yes. Keep your evaluation tool for scoring and monitoring, and connect Converra to turn failing scores into tested, deployed, verified fixes. They sit on different parts of the loop — measurement versus improvement.
Converra diagnoses the failure, tests the fix in simulation, and verifies it worked on your real traffic. Connect your production data and see it on your own agent.