I built a small autonomous AI team pattern where a Research Analyst agent retrieves and summarizes candidate snippets and a Verifier agent checks those summaries against sources before final generation. In practice this split reduced hallucinations more than a single generator step did. The analyst model was tuned for extraction and short summaries; the verifier used a stricter prompt that asked for source citations and a confidence score.
My operational notes: pick the right model for each role, and add an automatic retry when the verifier confidence is low. Also log each agent’s scores so you can monitor drift. Using agent orchestration let us run multi-step reasoning while keeping each role simple. It doesn’t eliminate human review, but it makes the pipeline auditable and easier to improve over time.
What metrics do you use to decide when the verifier should hand off to a human?
we split tasks into analyst and verifier. the analyst extracts and tags candidates. the verifier checks against source text and returns a confidence score. set a threshold where low confidence routes to a human queue. track those rates to tune prompts and models.
i set the verifier threshold at 0.8 initially and monitored false positives. after a week i adjusted to 0.85. keep the threshold flexible as you change prompts or models.
I implemented an analyst + verifier loop for a support knowledge base workflow and learned that metrics matter more than intuition. Start with these: verifier confidence distribution, percent routed to humans, average time human spends on routed items, and the change in post-review error rate. We began with a conservative confidence threshold so humans got many cases; that gave us labeled data to tune prompts and retriever behavior. Over time we tightened the threshold and reduced human load. Also watch for concept drift: when source docs change frequently, verifier confidence drops and you need to refresh embeddings or retrieval prompts. Keeping separate dev and prod flows made it safe to test different thresholds before rolling them out.
Choose verifier metrics aligned with risk. For low-risk public content a lower threshold is acceptable. For legal or compliance content require high confidence plus source citation matching. Track routed case outcomes to iterate thresholds.