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Continuouslyevaluate and optimizeyour agent.

RELAI builds evals from your code, requirements, and traces, then uses them to optimize your agent against your business KPIs—without regressions.

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Works natively with your coding agent.

Your agent, continuously improvedYour agent supplies signals from traces, instructions, and code. RELAI builds evals and uses them to optimize the harness. Harness improvements return to your agent as a pull request, closing the loop.Your agentAny framework, any model.SignalsEvalsOptimizeHarnessimprovementsPull request
C3.ai
“RELAI has helped us turn hard use cases into evals, and evals into measurable improvements in the agents we ship to customers.”
Nikhil Krishnan, CTO & Chief AI Officer, C3.ai

Measured impact.Not just proposed fixes.

AutomationBench

Pass‑rate improvement and lower cost

+3–10

Pass‑rate points gained across 3 frontier models.

22–44%

Lower cost per successful task, same 3 models.

Terminal‑Bench 2.0

How improvements transfer to the next round

79.2%

Pass rate after the first optimization round.

Baseline: 62.5%

72.7%

Pass rate with new tasks added, no re‑optimization.

Combined task set · baseline: 56.8%

Find what your evals miss.

RELAI reads your requirements, code, evals, and traces to find missing coverage and unclear expectations.

EvalsWiki: your agent’s behavior, as Markdown in your repo.

From a living behavior map to an evaluated harness changeEvalsWiki, the living map of your agent, connects requirements, risks, coverage, evidence, and open questions as Markdown files. A scenario with a task and expected behavior runs your agent in a replayable environment with a simulated user, mock and real tools and APIs, data, and state. An LLM judge and a code evaluator check the full trajectory. The optimizer tests harness candidates against accumulated evals for quality, cost, and latency, rejects regressions, and returns an accepted change with evaluation evidence as a pull request.Requirementsrequirements.mdRisksrisks.mdCoveragecoverage.mdEvidenceevidence.mdOpen questionsopen-questions.mdEvalsWikiThe living map of your agentScenarioTask and expectedbehaviorEnvironmentSimulateduserYour agentMocktools/APIsRealtools/APIsDataStateEvaluatorsFull trajectoryLLM as a judgeCode evaluatorPassHarnessPromptsToolsWorkflowMemoryOptimizerQuality · cost · latencyCandidateTool changeWorkflow changeAccumulated evalsRejected · regressionAcceptedPull requestAll evals passNo regressionsReady for reviewEvalsWiki, eval environments, and harness optimizationEvalsWiki, the living map of your agent, connects requirements, risks, coverage, evidence, and open questions as Markdown files. A scenario with a task and expected behavior runs your agent in a replayable environment with a simulated user, mock and real tools and APIs, data, and state. An LLM judge and a code evaluator check the full trajectory. The optimizer tests harness candidates against accumulated evals for quality, cost, and latency, rejects regressions, and returns an accepted change with evaluation evidence as a pull request.Requirementsrequirements.mdRisksrisks.mdCoveragecoverage.mdEvidenceevidence.mdOpen questionsopen-questions.mdEvalsWikiThe living map of your agentScenarioTask and expected behaviorEnvironmentSimulated userYour agentMocktools/APIsRealtools/APIsDataStateEvaluatorsFull trajectoryLLM as a judgeCode evaluatorPassHarnessPromptsToolsWorkflowMemoryOptimizerQuality · cost · latencyCandidateTool changeWorkflow changeAccumulated evalsRejected · regressionAcceptedPull requestReady for reviewAll evals passNo regressions

Every model change is a new harness problem.

RELAI tests model and harness combinations against the same evals and returns a verified configuration.

Change models

Adapt the harness, not every use case.

Adopt open weights models

Find where behavior breaks and fix it.

Route across models

Verify routing before production.

Own the intelligence behind your agent.

Your business requirements, evals, and harness improvements capture how your business operates. They stay versioned in your repository, preserving that knowledge as models change.

Each iteration adds to what your team owns.

Your repository

Versioned with your code

EvalsWiki

Expected behavior and evidence, as Markdown.

Evals

Scenario, environment, evaluators. Harbor format.

Harness

Prompts, tools, workflow, memory.

Keep your observability stack.Put its traces to work.

RELAI turns its traces into current evals and harness improvements.

Arize

Braintrust

LangSmith

Langfuse

Helicone

Keep your evals and harness learning together.

Start with one agent.

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Questions

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