Performance
Measurable standards every agent is graded against, proven exhaustively before deployment and audited continuously in production. Every result, every signal feeds back into the system for a continuous self-improvement loop. Every check is graded on its own, so a run that reaches the right outcome through the wrong sequence still fails.
Book a demoNorthstars
Metrics that agents optimize themselves toward, how they behave and the outcomes they are responsible for.
- Behavioral standardsCommunication style, phrases to avoid, which tools to use and in what order.
- Business outcomesResolution rate, containment and escalation conditions, tracked the same way behavior is.
- Automatically extractedReal production examples teach the evaluator what good and bad look like, and accuracy improves as sessions are reviewed.
Tests
Agents and whole workflows proven end to end before anything reaches production.
- Adversarial testsAI mock users try to break your agent with prompt injection, topic derailing and data extraction.
- Custom and Regression testsAssert the exact response and tool calls you expect, and turn every production failure into a permanent case.
- End to End testsRun the whole workflow from first contact to written outcome, with every tool call and check graded.
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Audits
Live sessions are graded continuously, so issues surface on their own and every finding feeds a flywheel for agents to self-improve.
- Behavioral auditsLive runs are sampled and graded against your northstars by an AI judge, at rates you set.
- Error trackingTechnical failures are captured and deduplicated, with occurrence counts and links to the affected sessions.
- Voice quality monitoringEvery voice session is scored on transcription accuracy, speech quality, conversation flow and latency.
Self-improving Agents
Every audit result and human correction feeds back, so agents propose their own fixes and nothing changes until you approve it.
- Closed-loop feedbackA thumbs up or down on any audit result becomes a calibration example on the relevant northstar.
- Proposed fixesAudit failures come back as concrete changes, like a prompt edit, a new northstar, a regression test.
- A/B testing across versionsSplit production traffic between versions and measure the impact before rolling a change out broadly.
Optimizing for the highest order outcomes
Whole cohorts bound to a few key northstars like cost to serve, customer retention, etc. instead of thousands of agents chasing their individual metrics. This way the operation climbs toward a global maximum instead of local optima trapped in silos.
Enterprise SuperintelligenceFAQs
Performance is the layered evaluation and audit system that defines what correct agent behavior looks like, continuously verifies production behavior against that standard, detects deviations early, and validates changes safely before they reach customers. It's what separates a pilot from a production-grade deployment.
LLM-as-a-Judge is HappyRobot's evaluation methodology for automatically scoring agent behavior against defined standards. It achieves 85 to 90% alignment with human expert reviewers, validated across several benchmarks, with higher accuracy on binary pass/fail evaluations.
During development teams write northstars, evals, and adversarial tests before deploying. In production, live sessions are sampled for behavioral and audio audits while observability tracks health. Failures generate new regression tests, fixes are validated against the full suite, and audits confirm the fix holds, closing the loop.
Before deployment, agents are validated through Custom Tests, manually designed scenarios with expected behaviors and expected tool calls that validate planned flows and edge cases; Regression Tests, built directly from real production conversations so every past failure becomes a locked-in test case that all future releases are checked against; and Adversarial Tests, where an AI-powered mock user actively tries to break the agent through prompt injection, topic derailing, or data extraction attempts. End-to-end testing also allows you to test entire workflows. Nothing reaches production without passing this full suite.
Once live, agent sessions are continuously sampled and evaluated against northstar standards through several audit types: Behavioral Audits, which check whether the agent followed the right sequence and avoided what it should avoid; Audio Audits, which monitor voice quality metrics like word error rate, TTS accuracy, and latency by component; Node Error Audits, which track technical workflow failures; and Manual Flags, structured human feedback from transcript review. Together these confirm the agent is still performing correctly at scale, long after initial deployment.
