Agent optimization from human
and AI feedback.
ZeroEval captures every run, scores quality with calibrated judges, and turns user feedback into better prompts, so your agents improve after launch, not just before it.
“You are a helpful assistant.”
“You are a technical support specialist. Keep answers under 3 sentences. Always reference the error code.”
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
One system from trace to better prompt
Trace
Trace every run
Capture inputs, outputs, tool calls, latency, cost, and errors from the same production traffic your users generate. Two lines to instrument.
- Python & TypeScript SDKs, REST API, OpenTelemetry
- Auto-instruments OpenAI, Anthropic, Google, LangChain
- Sessions, tags, and metadata for filtering
Evaluate
Score what matters
Run built-in or custom judges against your own quality bar, not a generic benchmark. Binary pass/fail, rubric-based scoring, sample rates. You decide what to evaluate and how.
- Built-in judges for hallucinations, safety, frustration
- Custom rubrics for domain-specific quality
- Suggested judges from your production traffic
Calibrate
Calibrate the judge
When the judge gets it wrong, correct it. ZeroEval learns your standards over time. The evaluation layer itself improves, not just your prompts.
- 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.”
Optimize
Optimize the prompt
Turn feedback into candidate prompt versions. Compare them to the baseline, validate across models, and deploy without redeploying your app.
- Candidates generated from real failure patterns
- Side-by-side comparison with version history
- One-click deploy via runtime prompt fetching
“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.”
The Payoff
See how failures become a better prompt
A weak prompt creates bad outcomes. User feedback and judge results reveal the pattern. ZeroEval proposes a better version, compares it to the baseline, and lets you ship the winner.
“You are a helpful assistant.”
“You are a technical support specialist. Keep answers under 3 sentences.”
Generate a candidate from real feedback
Compare against the baseline
Validate across models
Deploy without redeploying your app
Use ZeroEval from code,
CLI, or agents.
Instrument with the SDK or OpenTelemetry. Inspect failures from the CLI. Let coding agents query ZeroEval directly through the MCP server. They read where they’re failing and use that context to improve their own behavior.
- Python & TypeScript SDKs, REST API, OpenTelemetry
- CLI for querying evaluations and triggering optimization
- MCP server for Cursor, Claude Code, and 30+ agents
See the feedback loop on a real agent
Book a demo to see how ZeroEval turns traces, judges, and user feedback into better prompts and fewer regressions.
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