HOW CLAUDE CODE ACTUALLY WORKS: A 6‑LAYER AGENT RUNTIME
A deep dive maps Claude Code as a six-layer agent runtime with context compression and team orchestration. This visual explainer details Claude Code’s input ga...
A deep dive maps Claude Code as a six-layer agent runtime with context compression and team orchestration.
This visual explainer details Claude Code’s input gating, a knowledge layer with a skill registry, and a five-stage context compressor that kicks in near 95% window usage to extract structure instead of summarizing noise source. The execution layer routes typed tools with parallel streams and reuses stable prompt prefixes to cut costs.
It also covers the MCP integration surface, persistent markdown memory, and two levels of parallelism: subagents (scoped workers) and agent teams (independent instances coordinated via JSON task lists and a mailbox) details.
Claude Code behaves like an orchestrated agent runtime, which affects how you split tasks, manage context, and route tools.
Understanding its compression and team model helps you reduce token spend and avoid brittle, monolithic prompts.
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terminal
Load a large repo and push past 95% context to measure the compressor’s retention of file paths, error traces, and code snippets vs. baseline summarization.
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terminal
Benchmark subagents vs. agent teams for a multi-file refactor: throughput, contention on shared JSON task lists, and token savings from prompt cache reuse.
Legacy codebase integration strategies...
- 01.
Wrap existing CLI/scripts as typed tools and expose them via MCP; define permission tiers before enabling write/exec.
- 02.
Redirect agent_memory.md to a durable store (e.g., DB or versioned file) and add auditing around cross-session writes.
Fresh architecture paradigms...
- 01.
Design workflows around agent teams with a clear task graph and disk-backed mailbox to parallelize long-running codebase tasks.
- 02.
Model prompts as reusable prefixes to exploit the prompt cache and keep costs predictable under parallel load.
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