Sentry, but for AI agents

LLM Evaluation Built for AI Agents

Latitude auto-generates evaluations from real production failures, not synthetic benchmarks. LLM-as-judge, RAG and multi-turn agent evals, calibrated to your product.

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 self-healing loop

From production failure to running eval in minutes

Latitude detects what breaks, generates the evaluation automatically, and keeps watching. No manual eval writing, no synthetic test suites.

Detect failures

Trace every agent interaction in production. Latitude analyzes sessions, surfaces failures, and groups recurring patterns into prioritized Signals with lifecycle tracking.

Sessions dashboard showing real-time agent traces, cost, latency, and failure indicators

Auto-generate evals

Every Signal triggers an evaluation script automatically. No synthetic benchmarks. Evals built from real production failures, calibrated to your product with human annotations and MCC alignment.

Issue detail view with impact metrics, trend analysis, and one-click eval generation

Fix and verify

Dispatch your coding agent to implement the fix. Evaluations keep running on live traffic. If the failure recurs, the signal reopens. Continuous loop, zero manual eval writing.

Signals dashboard tracking occurrences over time with status and trend indicators

Continuous loop. Every fix makes the next detection smarter.

Evaluation types

Every eval your agents actually need

One platform for LLM-as-a-judge scoring, RAG evaluation, and multi-turn agent evals. All generated from production failures and calibrated to your product.

LLM-as-a-judge

LLM-as-a-judge, calibrated to your team

Score any agent output with an LLM judge that runs as a sandboxed script on live traffic. Human annotations set the ground truth, and MCC alignment measures how closely the judge agrees with your domain experts, so you see drift instead of trusting a black box.

Judges run continuously on matching traces, not in one-off batches
Annotations and built-in flaggers supply the ground truth
MCC alignment score makes judge-vs-human disagreement visible

RAG evaluation

RAG and prompt evaluation from real traffic

Evaluate retrieval quality on the traces your RAG pipeline actually produced. Every issue Latitude opens generates its own eval script, so a retrieval failure you saw once becomes a check that runs on every request after it.

Evals generated from the failing traces themselves, not a benchmark set
Semantic search over trace conversations to find the failures worth scoring
Same eval scripts reused in simulations before you ship

Agent evals

Multi-turn agent evaluation

Agents fail across turns, tool calls, and non-deterministic paths, not in a single request/response. Latitude scores whole sessions, so the eval sees the same context your user did.

Session-level scoring across multi-turn conversations
Tool-call and agent-state failures captured in the trace
OTEL-compatible, so you instrument without replacing Datadog or Sentry

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.

Behavior analytics dashboard showing agent conversation patterns, session frequency, escalation and abandonment detection

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.