Continuous AI Evaluations for Better Production Agents

AI agent evaluation in pre-production involves running agents against curated test datasets and scoring outputs for quality, safety, and compliance. Teams define the metrics that matter for their use case (hallucination, faithfulness, PII, toxicity, jailbreak vulnerability) and test against them before deployment. Fiddler supports experiments where you compare different prompts, models, and configurations side by side against the same test data to find the highest-performing setup before it reaches production.

Agentic pipelines involve multiple steps, tool calls, agent handoffs, and reasoning chains, so your LLM evaluation pipeline needs to cover more than just the final output. Effective evaluation scores behavior at every level of the agentic hierarchy, from the application down to individual spans, so you can trace a bad outcome back to the specific step that caused it. Fiddler evaluates across the full hierarchy (application, session, agent, trace, span) with the same metrics in testing and production.

Key metrics for AI agent evaluation include hallucination and faithfulness, safety (jailbreak, toxicity, harassment, harmful content), PII/PHI detection, sentiment, profanity, and language detection. Domain-specific KPIs like accuracy, relevance, and compliance metrics are also important depending on the use case. Fiddler supports 80+ out-of-the-box metrics across these categories, plus the ability to define custom evaluators for requirements that off-the-shelf metrics don't cover.

Pre-production evaluation tests agent behavior against curated datasets before deployment. Production evaluation scores live traffic to catch drift, new failure modes, and emerging risks. Most tools cover one or the other. Fiddler runs the same Centor Models in both, so what you measured before launch is exactly what you're monitoring after. Production findings feed back into pre-production test suites.

A continual learning loop is a process where production evaluation findings feed directly back into pre-production testing, so each iteration of an AI agent improves based on real-world failures rather than synthetic benchmarks. The loop has four steps: evaluate before deployment, observe every decision in production, debug and annotate failures, and feed those annotated traces back into your evaluation datasets. Without this loop, teams patch agents blindly and can't validate whether fixes actually worked.

When evaluation costs scale per trace, teams sample to control spend, which means a portion of production traffic goes unscored for hallucinations, safety incidents, PII exposure, and policy violations. Fiddler Centor evaluates 100% of traces at a fixed cost, so there's no trade-off between coverage and budget.