# Interview Summary Agent

> An interview-summary agent that turns notes into evidence-based feedback against role criteria, screens out biased factors, and never makes the hiring decision.

- **Category:** HR & Recruiting
- **Difficulty:** Advanced
- **Setup time:** 40 min
- **Stacks:** Claude, LangGraph, OpenAI
- **Version:** 2.0.0 (updated 2026-06-21)
- **Source:** https://agent-kits.com/kit/interview-summarizer

## Overview

Turns interview notes into structured feedback mapped to the role's criteria.

Ties strengths and concerns to evidence the interviewer actually observed.

Screens out biased and non-job-related factors and flags thin evidence.

Defensive: stays faithful to the input, and never makes the hire/no-hire decision.

## When to use it

- You want interview notes turned into consistent, structured feedback.
- You want assessments tied to evidence and the role criteria.
- You want non-job-related and biased factors screened out.
- You want a summary that supports the panel's decision, not one that makes it.

## When not to use it

- You want it to decide who to hire — it won't, by design.
- You expect it to score candidates on factors unrelated to the job.
- You have no interview notes to summarize.
- You can't keep the hiring decision with people.

## Architecture

- **Notes intake** — Receives the interview notes or transcript under confidential candidate handling.
- **Observation extractor** — Pulls what the interviewer actually observed, keeping it faithful to their input.
- **Criteria mapper** — Maps observations to the role's competencies so feedback is structured and job-related.
- **Evidence checker** — Ties each assessment to a specific example and flags criteria with thin or no evidence.
- **Bias guard** — Screens out age, gender, race, accent, appearance, and other non-job-related or protected factors, flagging them.
- **No-decision guard** — Blocks any hire/no-hire verdict; output is evidence organized for the panel.
- **Summary assembly** — Produces the structured, neutral summary with thin-evidence and excluded-factor flags.

## Files in this bundle

- `system-prompt.md` — the agent's system prompt
- `tools.json` — tool/function definitions the agent expects
- `workflow.md` — step-by-step run loop
- `.env.example` — environment variables to fill in
- `kit.json` — full structured metadata
- `starters/` — minimal starter snippets
- `LICENSE` — Apache-2.0 (code, configuration, and schema)
- `LICENSE-CONTENT` — CC BY 4.0 (specification text and documentation)
- `NOTICE` — attribution and trademark notice
- `run.py` — **runnable demo**: real agent loop with mock tools (`pip install anthropic`, set `ANTHROPIC_API_KEY`, `python run.py`)
- `agentaz.json` / `agentaz.schema.json` — AgentAz™ governance spec + schema

## Implementation notes

- Tie every assessment to specific evidence from the notes; an interview summary that asserts qualities without examples invites bias and bad decisions.
- Actively screen out protected and non-job-related factors (age, gender, race, accent, appearance, vague 'culture fit') and flag them, rather than passing interviewer bias through.
- Never output a hire/no-hire decision or a ranking that functions as one; organize evidence for the panel and keep the decision human.
- Flag thin or missing evidence so the panel knows what to probe rather than over-reading a sparse note.
- Stay faithful to the interviewer's meaning; don't embellish, soften, or put words in their mouth.
- Keep candidate information confidential and minimize unnecessary personal data in the summary.
- Keep the strong model on bias screening and evidence-tying given the fairness and legal stakes.

## Variations

- **Basic — Feedback summarizer:** Organizes interview notes into structured feedback by competency with evidence. No decision.
- **Advanced — Fair, evidence-based summary:** Adds bias screening with flags, evidence-tying, thin-evidence flags, and a strict no-hiring-decision guard.
- **Enterprise — Structured hiring support:** Adds ATS integration, configurable role rubrics, panel debrief aggregation, fairness controls, and audit trails — humans decide.

## FAQ

**Will it decide who to hire?**

No, and that's deliberate. It organizes evidence against your role criteria for the hiring panel to discuss. It never outputs a hire/no-hire verdict or a ranking that functions as the decision — the people decide.

**How does it handle bias in notes?**

It actively screens out non-job-related and protected factors — age, gender, race, accent, appearance, vague 'culture fit' — and excludes them from the assessment, flagging them so they don't influence the decision. It reduces bias rather than passing it through.

**Does it make up assessments?**

No. Every strength or concern is tied to a specific observation from the notes. Where evidence is thin, it flags it as thin rather than embellishing a quality the interviewer didn't actually support.

**Is it faithful to what the interviewer wrote?**

Yes. It summarizes what the interviewer actually observed without changing their meaning or putting words in their mouth, while organizing it against the role's competencies.

**What about candidate privacy?**

It treats candidate information confidentially and minimizes unnecessary personal data, focusing the summary on job-related evidence.

**Can it match our scorecard?**

Yes. It maps feedback to your defined competencies and rubric, producing consistent structured summaries across interviewers while keeping the decision with the panel.


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© 2026 AgentKits · www.agent-kits.com

**Open source.** Code, configuration, and the AgentAz™ schema are licensed under
**Apache-2.0**; specification text and documentation under **CC BY 4.0** (with
attribution to AgentKits, link to https://www.agent-kits.com). See LICENSE, LICENSE-CONTENT,
and NOTICE in this bundle. "AgentAz" and "AgentKits" are trademarks of AgentKits.

**Flagship reference blueprint.** View the source, AgentAz™ spec, and frozen v1.0 schema
in the public registry: https://github.com/agent-kits/agentaz
Original: https://agent-kits.com/kit/interview-summarizer
