01The Forensic Mindset
Before you can fix an "Agentic Readiness" problem, you have to measure it. Corporate monoliths aren't just big; they are tangled. A human developer navigates this tangle using years of institutional knowledge. An AI agent, however, navigates it using **imports**.
In this series, we're going to dismantle a hypothetical (but painfully real) monolith. Starting with the most important step: **The Audit**.
02Mapping the Fragmentation
We use the @aiready/context-analyzer to perform what we call "Repository Forensics." We are looking for **Context Clusters**—groups of files that are logically linked but physically scattered.
03The Scorecard: Signal vs. Noise
Running aiready scan --score gives us our baseline. A score of 40/100 means your agent is spending 60% of its token budget on noise. We look for:
- **Circular Dependencies**: The death loop for LLM reasoning.
- **God Files**: 2000+ line files that blow the context window.
- **Deep Chains**: If a change in `A` requires reading `B, C, D, E, F`.
04What’s Next?
The audit is the map. In our next entry, **The First Cut**, we'll take the scalpel to our first context cluster and show you how to flatten an import hierarchy without breaking the build.