Continuous Unsupervised Evals Catch Agent Failures Before Users Notice
Implement binary unsupervised evals on every production interaction to proactively detect issues like hallucinations or topic drift, using specific prompts with edge-case examples and cost-optimized models.
Replace User Complaints with Proactive Detection
Waiting for user reports lets agent failures erode trust and affect multiple users before fixes. Continuous evaluations run automated checks on live production traffic, catching regressions instantly since agents are non-deterministic and production inputs exceed test coverage. Unsupervised evals assess behavior using only the agent's context—no ground truth needed—enabling them to process every interaction, unlike supervised evals limited to offline testing. This delivers immediate signals: one customer exposes four evals (hallucination, answer completeness, goal accuracy, topic adherence) in a user-facing dashboard for transparency; another targets three user-reported modes (wrong format, unnecessary refusals, incorrect datastore protocol) to alert devs preemptively.
Design Unsupervised Evals for Reliability and Efficiency
Target concrete failure modes with single-sentence definitions, like "Did the agent reference info absent from retrieved documents?"—provide full context (e.g., docs for hallucination checks, system prompt for topic adherence). Output binary pass/fail plus explanations to spot patterns without manual trace reviews; avoid scored ranges due to LLM inconsistency. Anchor judgments with 2-4 edge-case examples in prompts (e.g., borderline hallucinations vs. grounded responses) rather than obvious wins/fails—these calibrate gray areas better than verbose instructions. Optimize costs by baseline-testing larger models then swapping to smaller ones if accuracy matches; refine prompts first if small models falter. Reserve LLMs for qualitative checks (tone, completeness, grounding) and use deterministic functions for quantitative ones (precision/recall, math verification, schema validation) to ensure speed and precision.
Act on Failures with Alerting or Triage
High-confidence evals trigger immediate alerts for investigation, minimizing user impact with low false positives. For noisier setups, route failures to human review queues, clustering them to prioritize fixes like prompt tweaks or tool updates. Production examples prove impact: evals prevent complaint compounding by surfacing issues in real-time, sustaining user confidence in agent reliability.