career OS
6-agent job search automation — from 500+ daily positions to 5 perfect matches
problem
employed job search wastes 20+ hrs/wk. Manual processes miss 90% of relevant positions due to keyword limitations and time constraints.
solution
sourcing → dedupe → fit scoring → comp sanity → draft outreach; human-in-the-loop at critical decision points.
architecture
6-agent system:
- scraper: crawls 50+ sites with rate limiting
- filter: scores 0-100% match with weighted criteria
- enricher: gathers company intel from multiple sources
- applier: generates customized materials
- tracker: manages status in Google Sheets
- reviewer: human approval before submission
design & process reflection
built a multi-agent job search automation system that balances efficiency with authenticity, demonstrating how ai amplifies rather than replaces human judgment.
key pm challenges & decisions
the personalization paradox
discovered llms default to caution, generating generic content. solution: built 500+ line narrative database with explicit context, enabling authentic personalization while maintaining 70% automation efficiency.
multi-agent vs monolithic
chose specialized agents over monolithic architecture for independent testing, clear separation of concerns, and graceful degradation—critical for production reliability.
the 70% automation sweet spot
designed system to automate tedious work (scraping, formatting) while requiring human judgment at critical points (final review), with transparent scoring rationale.
core product insights
authenticity beats perfection: real personalization requires specific metrics ($50m arr, 27% improvement), named projects, and permission to be specific—not just data access.
context layers drive quality: each agent needs precisely scoped context: too little creates generic output, too much wastes resources. solution: on-demand narrative database.
structured failure modes: built explicit fallbacks, validation layers, and graceful degradation rather than preventing all failures—ensuring system resilience.
product evolution
phase 1
basic pipeline established core functionality but produced generic applications.
phase 2
added intelligence layer (enrichment, summarization) and persistence.
phase 3
narrative-driven approach achieved authentic, production-ready personalization.
future roadmap
key takeaway
successful automation amplifies human judgment rather than replacing it. the system works because it treats ai as a capability multiplier, not a decision maker.
proof: pipeline diagram, before/after inbox screenshot, sample n=1–2 users