Demand-Driven Context (DDC) Framework

DDC is TDD for knowledge bases — failing agents drive curation, not failing tests.

A methodology for building enterprise AI agent knowledge bases through problem-driven curation.

Stop trying to document everything. Give your agent a real problem. Let it tell you what's missing. Repeat 30 times.

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The Problem

Enterprise AI agents excel at reasoning but lack the domain-specific context to make accurate decisions. Current approaches fail because they try to anticipate what an agent needs:

• Context window stuffing — too large to compress, agent can't prioritize
• RAG over wikis — retrieves documents, not understanding
• Top-down curation — takes months, half is never used, stale before you finish
• Skills.md — great for patterns, insufficient for deep domain knowledge

The Solution: DDC

DDC flips the approach: instead of telling agents what to know, let problems reveal what to curate.

Real Problem -> Agent Attempts (fails) -> Information Checklist
     -> Human Fills Gaps -> Agent Curates -> Agent Succeeds
                                              |
                              Repeat 20-30x --+-- Knowledge Base Emerges

Think TDD for knowledge bases: failing tests drive code, failing agents drive curation.

What's In This Repo

Directory	Contents
METHODOLOGY.md	The DDC framework — how it works
WHY.md	The hypothesis and motivation
CLAUDE.md	AI agent guidance for DDC knowledge bases
meta/	Entity and relationship type definitions
templates/	Starter kit — domain skeleton, entity templates, cycle log template
tooling/	Web UI for exploring knowledge bases (FastAPI + React)
examples/healthcare-claims/	Synthetic domain with ~40 entities and 5 DDC cycle logs
demo/	Empty knowledge base + script for trying DDC yourself
ddc-cycle-log/	Cycle log format spec and analysis scripts

Try DDC in 30 Minutes

Have Claude Code (or any AI coding agent) installed? Follow the Getting Started guide to:

1. Browse the example healthcare claims knowledge base
2. Copy the template and start your own domain
3. Run your first DDC cycle on a real problem

See demo/DEMO-SCRIPT.md for a live demo walkthrough.

Quick Start

1. Explore the Example Domain

cd tooling
DDC_KNOWLEDGE_BASE_PATH=../examples/healthcare-claims/domain-knowledge ./start.sh

Open http://localhost:3000 to browse the healthcare claims knowledge base.

2. Start Your Own DDC Knowledge Base

cp -r templates/domain-skeleton my-domain/
# Edit CLAUDE.md with your domain scope
# Create your first sandbox problem
# Run your first DDC cycle

3. Read the Methodology

1. WHY.md — The hypothesis
2. METHODOLOGY.md — The DDC framework
3. examples/healthcare-claims/ddc-cycle-logs/ — See it in action

The Convergence Hypothesis

After 20-30 real problems:

• Each new problem requires fewer new entities (knowledge converges)
• Learning paths emerge naturally from most-referenced entities
• The knowledge base is smaller, more relevant, and more maintainable than top-down

Contributing

This is an active research project. If you're experimenting with enterprise AI agent context — whether with DDC or your own approach — contributions and experience reports are welcome.

License

MIT License. See LICENSE.