Evaluation Overview

Why evaluate your agent

LLM agents fail in production in ways you can’t predict upfront. A prompt that works for 90% of queries might hallucinate on edge cases, ignore context, or produce verbose responses when users expect concise answers. Manual review doesn’t scale, and you can’t anticipate every failure mode before shipping.

You need automated regression testing — but not the kind where you sit down and write a test suite from scratch. The most effective test suites are built incrementally, from real production failures. Every time you find a bad response, you turn it into a test case. Over time, your suite becomes a comprehensive guard against the specific failure modes your agent actually encounters.

Test suites are created as you debug and improve your agent — they grow organically from real failures, not from a separate test-writing phase.

The evaluation loop

Find an issue in production

Start in the Opik dashboard. Browse traces, filter by error status or low feedback scores, and click into a trace to see the full span tree — every LLM call, tool invocation, and retrieval step with its inputs, outputs, and latencies.

Add it to a test suite

Turn the failure into a test case. Add the trace to a test suite with a natural-language assertion that captures the expected behavior — for example, “The response must not hallucinate facts not present in the context”. You can do this through Ollie (Opik’s AI assistant), the UI, or the SDK.

Update your agent

Fix the root cause. Update a prompt via the Prompt Library, adjust tool definitions, or change retrieval parameters. Use Ollie to help diagnose the issue and suggest fixes.

Validate with the test suite

Run the test suite against your updated agent. The suite checks every test case — including the new one — so you confirm the fix works and nothing else regressed.

Each cycle adds a new test case. Over time, your test suite becomes a comprehensive regression guard tailored to the real failure modes of your agent.

Comparing two test suite experiment runs side by side

Two approaches to evaluation

Opik provides two complementary approaches to evaluation:

Test Suites: Define natural-language assertions and let an LLM judge check them automatically. Best for pass/fail testing of specific behaviors.
Datasets & Metrics: Score your agent’s outputs against a dataset using pre-built or custom metrics. Best for measuring quality across many traces with quantitative scores.

Key features

Test Suites with natural-language assertions and execution policies
30+ pre-built metrics for hallucination, relevance, coherence, and more
Custom metrics for domain-specific evaluation
Experiment tracking to compare versions side-by-side
Annotation Queues for human-in-the-loop review

Next steps

Getting started — Run your first evaluation in minutes
Concepts — Understand Test Suites vs Datasets & Metrics
Building Test Suites — Create and manage suites via the SDK, UI, or Ollie
Debugging agents with Ollie — The full workflow for turning production failures into test cases

Why evaluate your agent

Test suites are created as you debug and improve your agent — they grow organically from real failures, not from a separate test-writing phase.

The evaluation loop

Find an issue in production

Add it to a test suite

Update your agent

Fix the root cause. Update a prompt via the Prompt Library, adjust tool definitions, or change retrieval parameters. Use Ollie to help diagnose the issue and suggest fixes.

Validate with the test suite

Run the test suite against your updated agent. The suite checks every test case — including the new one — so you confirm the fix works and nothing else regressed.

Each cycle adds a new test case. Over time, your test suite becomes a comprehensive regression guard tailored to the real failure modes of your agent.

Two approaches to evaluation

Opik provides two complementary approaches to evaluation:

Test Suites: Define natural-language assertions and let an LLM judge check them automatically. Best for pass/fail testing of specific behaviors.
Datasets & Metrics: Score your agent’s outputs against a dataset using pre-built or custom metrics. Best for measuring quality across many traces with quantitative scores.

Key features

Test Suites with natural-language assertions and execution policies
30+ pre-built metrics for hallucination, relevance, coherence, and more
Custom metrics for domain-specific evaluation
Experiment tracking to compare versions side-by-side
Annotation Queues for human-in-the-loop review

Next steps

Getting started — Run your first evaluation in minutes
Concepts — Understand Test Suites vs Datasets & Metrics
Building Test Suites — Create and manage suites via the SDK, UI, or Ollie
Debugging agents with Ollie — The full workflow for turning production failures into test cases