Build self-improving agents
Calibrated LLM judges score every output. Feedback refines them. Your prompts improve automatically.
The Why
You ship an agent. It works in demos. Then production happens. Failures pile up in logs, users hit edge cases nobody tested for, and the agent just sits there. Same as the day you deployed it.
We believe the companies that win the next decade of AI won’t be those that build the best agents. They’ll be the ones whose agents get better over time.
Your users already know what’s wrong, but the tooling to close that loop just didn’t exist. So we built it.
We think software should grow, not just get shipped and maintained. Every AI system improving through its own usage. We’re early. We’re building for the long run.
The How
From install to self-improving agents in minutes
Install
Let your agent handle it
Install ZeroEval Skills and tell your coding agent to set up tracing. Or add the SDK manually - two lines to instrument.
- Works with Cursor, Claude Code, Codex, and 30+ agents
- Agent handles SDK install, first trace, and judge setup
- Or drop in the SDK manually in seconds
Evaluate
Define what “good” looks like
Create LLM judges that score your agent outputs in production. Binary pass/fail, rubric-based scoring, sample rates. You decide what to evaluate and how.
- Pass/fail or 1–10 scoring with custom rubrics
- Sample a percentage of traffic to control cost
- Filter by span type, tags, or custom properties
Calibrate
Correct the judge, it learns
When a judge gets it wrong, flag it with a reason. Your corrections train the judge to match your quality bar, not a generic one.
- Thumbs up/down with reasoning
- Provide expected output for comparison
- Watch alignment score climb over time
“You can request a full refund within 90 days of purchase.”
“You can request a full refund within 90 days…”
“Our refund window is 30 days from purchase…”
Optimize
Ship better prompts automatically
ZeroEval extracts patterns from feedback and rewrites your prompts to address them. No manual prompt engineering. Just review, approve, and deploy.
- New prompt versions generated from real failures
- Full version history with diff view
- One-click deploy to production
“You are a helpful customer support assistant.”
“You are a technical support specialist. Keep answers under 3 sentences. Always reference the specific error code. Be direct and specific.”
Your agent’s interface
to evaluation.
The CLI lets agents and automation inspect traces, filter evaluations, read judge reasoning, and kick off prompt optimization - all from the terminal.
- Query evaluations, filter by result, date, judge
- Read judge insights and calibration data
- Trigger and promote optimization runs
Agents that diagnose
themselves.
The MCP server gives your AI agents direct access to ZeroEval. They query where they’re failing, read the judge’s reasoning, and use that context to improve their own behavior. The loop closes itself.
- Agent reads evaluation results in context
- Surfaces failure patterns and judge reasoning
- Uses insights to refine its own prompts and behavior
Your agents should get better every day.
See how ZeroEval turns user feedback into higher-performing prompts, without manual tuning.
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