For AI teams shipping agents to production

LLM Evaluation Built for AI Agents

Evaluations auto-generated for every new issue from real production failures, not synthetic benchmarks. Calibrated to your team's judgment via human annotations.

Free plan: 20K credits/month No credit card required Setup in under 5 minutes

Trusted by thousands of engineers at companies like

The problem

Your agents fail silently. You find out from users.

Production AI is non-deterministic. Traditional testing catches the easy cases. The hard failures reach your customers first.

Invisible failures — no alerts, no clustering, just user complaints

Generic benchmarks — standard evals miss edge cases and tool-call errors

Built for LLMs, not agents — multi-turn, tool use, non-deterministic paths

The GEPA system

How GEPA works: from failures to evaluations, automatically

Latitude's closed-loop system turns production failures into monitoring scripts, calibrated to your definition of quality.

Observe

Capture every agent interaction. Spans, traces, sessions. OTEL-compatible SDK.

Traces dashboard showing real-time spans, latency, and cost metrics

Discover

Some failures auto-detected. Find more via semantic search over traces — annotate failed ones to create named issues. Prioritized by frequency and impact.

Issues dashboard with failure patterns and lifecycle tracking

Evaluate

Eval scripts generated automatically every time an issue is created. Run continuously on matching traces.

Annotation queues for human evaluation and ground truth collection

Align

MCC metric measures how well automated evals agree with human judgment. Drift stays visible.

Human review interface showing automated and human verdicts side by side

Continuous loop. Every iteration improves the next.

Multi-turn agent support

Built for multi-turn agents, not just LLM calls

Traditional tools were designed for simple request/response. Agents are different — multi-turn conversations, tool use, non-deterministic paths. Latitude was built for this from day one.

Traditional tools

Simple request/response

Single LLM calls only. No multi-turn support.
Static benchmarks

Generic test suites disconnected from production reality.
Manual eval writing

Engineers write every evaluation by hand. Maintenance nightmare.
Log dumps, not insights

Scattered traces. You spot patterns manually or not at all.

Latitude

Multi-turn agent workflows

Sessions, tool calls, complex state. Native support.
Evals from real failures

Auto-generated on issue creation. Not synthetic benchmarks.
Issue discovery + lifecycle

Some auto-detected, others found via semantic search. Named, prioritized, tracked (New → Escalating → Resolved).
Human-calibrated scoring

Annotations + MCC alignment keeps evals honest.