First Outcome Evidence Request

Send This Review Request

Choose one AI/ML engineer, professor, mentor, or advanced student who can inspect agent architecture.

Copy-Ready Message

Hi {name}, I am collecting public review evidence for my Data Quality Agent project so I can make resume claims only when they are backed by real, redacted GitHub evidence. Could you spend 8-15 minutes submitting this review form: https://github.com/sunnnn2005/data-quality-agent/issues/new?template=ai_engineer_review.md. The public tracking slot is https://github.com/sunnnn2005/data-quality-agent/issues/26. The ask is: Inspect the LLM tool-calling loop, guardrails, and evidence trail for AI Engineer credibility. Please create/submit your own public review issue, share only public, non-private details, and include the permission sentence in the issue if you are comfortable letting me count it. Shortest path if you do not want to read every doc: https://sunnnn2005.github.io/data-quality-agent/one-click-evidence-links.html.

Inspection Targets

• app/tool_agent.pyshows LLM-driven tool choice, loop state, and tool-result feedback
• app/llm.pyshows structured model calls, fallback handling, and output validation boundaries
• app/models.pyshows structured request and response schemas used by the API and agent
• app/postgres_adapter.pyshows read-only PostgreSQL access for realistic tabular business data
• app/verifier.pyshows deterministic checks that keep LLM conclusions tied to evidence
• evals/scenarios.jsonlshows the project is evaluated against repeatable agent behavior cases

Review Prompts

• Does the model choose tools from evidence, or does the code force a fixed workflow?
• Are tool outputs fed back into the agent before the final report is produced?
• Are findings, hypotheses, recommendations, evidence, confidence, and limitations separated?
• Where would prompt injection, sensitive data, or unsupported claims be blocked?
• What one change would make this more credible for an AI Engineer Intern resume?

Required Public Evidence

• implementation paths inspected
• strongest AI-agent signal
• least credible gap
• permission sentence if the reviewer allows the evidence to count

Locked Resume Line

Locked until the public evidence gate passes.

Received external AI Engineer review of the tool-calling loop, guardrails, structured output, and evidence trail.

Counting Boundary

This request page is not evidence by itself. The outcome becomes resume-countable only after a non-owner public GitHub issue includes permission to count, no private data, inspected paths, and passes the external reviewer evidence gate.

Not Claimed

• No AI Engineer review has been accepted yet.
• No reviewer message is recorded as sent yet.
• No external user, customer feedback, business impact, production deployment, or GitHub star is claimed.
• The future resume line is locked until the public evidence gate passes.