Production Multi-Agent AI Recruiter with Voice Interface

A production-grade multi-agent recruiting system that treats candidate evaluation as an engineering problem, not a prompt problem. Most AI recruiting tools are a single LLM call wrapped in a UI, with no structured evaluation and no reliability guarantees. This one is built differently.

The pipeline runs as a deterministic state machine, not a free-running agent loop. Four sequential stages: role extraction, criteria parsing, project ranking, CV Q&A. Each stage has a defined input/output contract, so no stage can hallucinate its way into the next.

MCP-based tool registry with an A2A critic layer. The recruiter agent hands off responses to an independent critic agent for structured automated review before results reach the user.

LLM-as-a-Judge evaluation suite with golden datasets and multi-metric scoring (faithfulness, relevancy, factuality).

Session-based memory and full trajectory logging across the entire pipeline.

Voice interface: Deepgram nova-2 STT and Google Neural2-D TTS over persistent WebSockets. ~600ms time-to-first-audio with sentence-level parallel synthesis and barge-in cancellation.

Every agent span instrumented with OpenTelemetry and exported to Langfuse for live trace visibility. Deployed to GCP Cloud Run.

~600ms time-to-first-audio on the voice pipeline

LLM-as-a-Judge scoring across faithfulness, relevancy, and factuality with regression logging

Zero hallucination pass-through: the critic layer blocks unverified outputs before they reach the user

Stack: Python, FastAPI, LangGraph, Deepgram, Google Cloud Run, OpenTelemetry, Langfuse, WebSockets, MCP.