Date: 2026-03-19 Severity: High — directly impacts search precision and credit cost Endpoint: POST /screener/person/search (Realtime People Search)

Date: 2026-03-19 Context: Response to Tim's email with optimized query recommendations and LiveScan suggestion.

We're building a candidate sourcing pipeline that searches for people by job title at a specific company. The pipeline:

This case study examines how a single change to tool response design — adding real-time state summaries (status_counts) to every tool call response — transformed an AI agent's behavior from short, conservative work sessions (~11 iterations) to sustained, strategic execution (~34 iterations, 3x improvement). We analyze 187 messages from a real production conversation where an AI agent triaged 3,261 recruitment candidates, comparing behavior before and after the change. We ground our findings in published research on closed-loop feedback, state observability, and LLM agent planning.

We operate an AI-powered recruitment co-pilot where an LLM agent (GPT-4o) uses a set of tools to manage candidate pipelines: listing candidates, viewing details, updating statuses individually or in bulk, and querying project summaries. The agent operates in a multi-turn loop with a configurable iteration limit.