In Part 1, I treated Claude Code as a bounded loop: perceive, reason, act, then feed the evidence back in. The reasoning engine is only as good as the working set it sees. A precise goal still fails if the window is full of directory trees, stale chat, and a vague tool contract.
This is Part 2 of 6 in my Agentic Coding with Claude Code series. The working model I am keeping is simple: context is assembled, not typed, and it is a budget, not a bucket.
CLAUDE.md, path-scoped rules, skills, memory, tool results, and the conversation all compete for the same tokens. Unfiltered tool output is paid for on every later turn. The job is to load the right slice at the right time.
Later posts will cover the harness and Agent SDK, MCP, evals, and guardrails. This one stays on the context layer that shapes every decision in the loop.