AI PM Ops Copilot: JD → Fit-Gap → Tailored Assets (Human-in-the-loop)

Most job-application automation fails because it’s either too generic or too risky (hallucinates, misrepresents experience). The goal was a controlled agent workflow that produces high-signal assets while keeping the user in charge via approvals and evidence-backed drafting.

• Designed a tool-using workflow: parse JD → extract requirements → map evidence from resume/projects → draft assets.
• Added Human-in-the-loop checkpoints before final writing (approve/edit/regen).
• Implemented validation rules (no claims without evidence; consistent dates/titles; format checks).
• Created run history: inputs, outputs, decisions, and timestamps for auditability.
• Shipped a simple UI so users can iterate quickly and track outputs per role.

• Demonstrates real-world agent design: tool constraints, HITL approvals, logging, and reliability tradeoffs.
• Shows AI PM thinking: user journey, risks/guardrails, measurable outcomes, and iteration loops.
• Creates a practical demo that recruiters immediately understand (and you can show live).

• Deployed with a lightweight UI + backend workflow runner.
• Logging: captures prompts, tool calls, and outputs for debugging and reliability.
• Monitoring: failure rate, regeneration rate, and time-to-final per artifact.
• Security note: user data stays private; supports deleting runs/artifacts.

• Over-automation risk: prevents misrepresentation by requiring evidence mapping + approvals.
• Prompt drift: outputs vary over time — stabilized with templates + validation checks.
• Tool failures: added retries + graceful fallback to manual steps.