Building an Agentic Research System with Claude Code: A Practical Implementation

A developer on r/ClaudeAI shared a production-grade implementation of an agentic research system built entirely with Claude Code. The system maintains Applied, a living map of ~250 real AI adoption cases across industries. Instead of chasing 100% autonomy, the key insight is keeping a human in the loop for edge cases.

The Six Agents

Each agent is a .md file with clear instructions. They run as cron jobs and coordinate by reading/writing to a shared knowledge store (the living map) and a report log:

• Scout Agent: Finds use cases from official sources, diversified across industries, tools, and business functions.
• Extractor Agent: The most critical. Understands cases, identifies entities and outcomes, and decides whether to add or discard.
• Enrichment Agent: Adds context and supplements cases with extra information.
• Translator Agent: Handles bilingual output (English/Spanish) while preserving context and tone.
• QA Agent: Scans for errors — website issues, UI/UX bugs, incorrect data. Fixes if straightforward; flags otherwise.
• Match Maker Agent: Matches users to cases based on preferences, via email or notifications.

Orchestration Pattern

No complex agent frameworks. Coordination is dead simple: all agents can read and write to the living map (the common knowledge base). Each also writes a report log accessible by the human and by other agents. Agents reference their own logs to understand where they left off. Borderline decisions or problems are flagged to the human, who makes the final call.

The entire stack runs on Claude Code. The agents themselves are plain .md files with instructions that get updated over time. Third-party tools fill in the gaps (no building a DB from scratch).

If you want to see the output, visit Applied (linked in the original post). The deep dive on this system is in the reports section.