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Agent Research System

Autonomous research pipeline for staying current with AI developments

1 min read
AIResearchAgents

Problem

The AI field moves too fast for manual monitoring. Papers, blog posts, product launches, and open-source releases happen daily. Staying current requires a system, not willpower.

Architecture

The system operates on three cycles:

  1. Daily Scan — Agents monitor a curated list of sources (arXiv, HackerNews, Twitter, specific blogs) for new content.
  2. Weekly Synthesis — A synthesis agent reviews the week's findings, identifies themes, and generates a summary document.
  3. Monthly Analysis — A deeper analysis identifies trends, connects dots between disparate developments, and updates a running thesis document.

Key Insights

  • Curation matters more than coverage. Monitoring everything creates noise. A carefully curated source list produces better signal.
  • Summaries should be opinionated, not neutral. The value is in the interpretation, not just the facts.
  • The monthly synthesis step is where the real insight happens.

Current Limitations

  • Source quality varies; some automated summaries miss nuance
  • The system occasionally surfaces irrelevant content
  • Synthesis quality depends heavily on prompt engineering