@zhihao_qi569

Interview scoring with one LLM is fast but fragile: models can over-score vague answers, miss follow-ups, or invent reasoning not supported by the transcript. Transcript Evaluator solves this with a two-agent architecture. Agent 1 batch-evaluates interview JSON against a unified behavioral/non-behavioral rubric and exports a structured PDF report. Agent 2 runs as a local review service powered by Bright Data MCP, receiving each case plus the primary evaluation and using MCP tools (search_engine, extract, and AI insight tools like web_data_chatgpt_ai_insights) to extract context and cross-check the first agent’s output. In a separate PDF feedback loop, Agent 2 sends the report back to Agent 1, which polls external AI perspectives through MCP and returns bias and calibration feedback so Agent 2 can revise before finalizing scores—reducing single-model hallucination and improving auditability for hiring teams.

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