Case Study 6-Agent System Job Search Automation

Career OS: Job Search Automation

A 6-agent AI system that automates job searching, reducing manual effort and improving match quality.

~20
Hours Saved Per Week
95%
Noise Reduction
6
Weeks Development

The Problem

Manual job search wastes 20+ hours per week on repetitive tasks: searching multiple job boards, filtering through irrelevant positions, researching companies, and customizing applications. Even worse, traditional keyword searches miss 90% of relevant positions due to inconsistent job titles and descriptions.

The result? Talented professionals spending more time searching than applying, missing opportunities that could be perfect matches, and experiencing burnout from the mechanical nature of modern job hunting.

The Solution: 6-Agent Architecture

Career OS uses a multi-agent system where each agent handles a specific part of the job search workflow. This modular approach allows for optimization at each step while maintaining flexibility for different career paths.

1

Scraper

Crawls 50+ job sites with intelligent rate limiting to gather comprehensive listings without triggering anti-bot measures.

2

Filter

Scores each job on a 0-100% match scale based on skills, experience, location, and cultural fit indicators.

3

Enricher

Gathers company intelligence from multiple sources to understand culture, growth trajectory, and team dynamics.

4

Applier

Generates customized application materials using a 500+ line narrative database for authentic personalization.

5

Tracker

Manages application status in Google Sheets, providing real-time visibility into pipeline and follow-up needs.

6

Reviewer

Human checkpoint for final approval before submission, ensuring quality and catching edge cases.

Key Product Insights

  • Authenticity beats perfection: Generic AI-generated content performs worse than slightly imperfect but genuine materials.
  • Specific metrics matter: Including named projects and quantified achievements dramatically improves response rates.
  • Context layers drive quality: The 500+ line narrative database enables nuanced personalization that feels authentic.
  • Human judgment amplification: The system works best when enhancing human decision-making, not replacing it.

"Successful automation amplifies human judgment rather than replacing it."

Personalization Approach

The system maintains a comprehensive narrative database with over 500 lines of personal history, project details, and career stories. This isn't just a resume—it's a living document that captures the nuances of experience and personality.

By drawing from this rich context, the Applier agent can generate cover letters and customize applications that maintain consistent voice while highlighting relevant experiences for each opportunity. This approach achieves 70% automation efficiency while preserving the authenticity that resonates with hiring managers.

Future Roadmap

ML-Based Scoring

Learn from successful applications to continuously improve match scoring algorithms.

A/B Testing Framework

Test different personalization strategies to optimize response rates.

Culture Matching

Advanced algorithms to assess company culture fit beyond surface-level keywords.

Network Effects

Identify and leverage referral opportunities within existing professional networks.

Build Your Own AI Systems

Career OS demonstrates how thoughtful automation can transform time-consuming processes. Let's explore how AI can enhance your operations.

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