As enterprise agents take on more complex workflows, the ability to evaluate and improve them on realistic edge cases becomes critical. RELAI helps turn hard use cases into evals, and evals into measurable agent improvements.
Agents fail. You can't manually debug your way to reliability.
Failure modes emerge across development, evaluation, and production. Manual debugging through logs and prompt patches doesn't scale — and it doesn't prevent regressions.
Every new fix creates new uncertainty. RELAI replaces it with a learning loop that improves and verifies automatically.
A loop that closes itself - failures in, improvements out.
Capture failures, diagnose root causes, generate improvements, and validate them against prior behavior — as one continuous optimization loop aligned with your business objectives.
Learning Environments
Every failure & feedback becomes a replayable learning environment.
A failed run or piece of feedback is one sample from the environment, not the environment. RELAI reconstructs a replayable learning environment — preserving inputs, state, tool calls, and memory — so improvements are validated under the conditions that produced the behavior.
Lifelong agent optimizer
Optimize the full agent harness, not just prompts.
RELAI continuously optimizes prompts, tools, memory, models, workflows, and agent logic as one unified system. Failures are traced to the right layer, prior scenarios become a living regression set, and optimization reuses replayable environments instead of exhaustively retesting. You define the objective — cost, task success, latency, or business KPIs — and RELAI proposes validated improvements as reviewable pull requests.
Learning system of record
Track every failure, scenario, diagnosis, candidate fix, evaluation, and shipped improvement.
RELAI gives teams a complete record of how an agent changed, why it changed, what was tested, and how performance moved across scenarios, benchmarks, and environments.
From failure to verified improvement in 2 commands.
A loop that closes itself — failures in, improvements out.
Failure detected
A failure or feedback is captured from production.
›charge.lookup()!Missing state validationrefund-flowfrom productionCreate environment
Reconstruct the context to reproduce the behavior.
$relai learning-environments create --log-file refund-flow.log✓Environment createdInputsMemoryToolsStateResponsesOptimize & validate
Find the root cause, propose improvements, and validate against prior behavior.
$relai optimize --learning-environment .relai/test_environments/refund-flowTask success94%↑ 8ppLatency p951.21s↓ 18%Cost / run$0.12↓ 14%✓128 / 128 regression checksReview improvements
Validated changes are proposed as a pull request.
⌥Improve refund validationrelai/optimizer/refund-flow → mainOpen✓ 128 / 128 checks
Agent maintenance doesn't scale. Learning systems do.
Shipping an agent is the easy part. Maintaining reliability over time is the grind — prompts decay, tools change, edge cases emerge, and the overhead of manual tuning and eval updates only compounds as agents scale. RELAI flips the curve: every failure and piece of feedback becomes a reusable learning signal, so reliability compounds.
Reduce agent improvement cycles from days to minutes.
Improve task success rate, latency, and token consumption with every loop — without changing the stack you've already built on.
Agent improvement cycles
Observed task success uplift
Faster end-to-end runs
Lower model spend
Build self-improving agents.
Turn failures and feedback into continuous, system-wide improvements.