Context Management

Conclusion first: the value of multi-agent doesn’t come from more models; it comes from the right structure. Multi-agent without structure is just more expensive concurrency — burn 2 to 3 times the tokens to watch a group of models nod at each other. Multi-agent with structure is an execution system that can delegate, parallelize, and verify. Why a Single Agent Fails at Long Tasks # Diagnosis first. A single agent executing complex tasks has five structural defects — blame the execution structure, not the model’s intelligence:

Large language models possess no biological memory at the hardware tier; they operate entirely on Key-Value Caches that scale linearly in GPU High-Bandwidth Memory (HBM) alongside attention weight distributions that mathematically dilute as sequence length grows. The notion of long-horizon conversations presented by Claude Code is, at its engineering core, an eviction and compaction pipeline operating within the tight margins of memory capacity and numerical precision, trading away information entropy and prefix caching efficiency.

Claude Code Skill in Anywhere Implementing Claude Skills’ Progressive Disclosure via MCP: Reusing Claude Code Skills Anywhere # — A feasible approach to capability encapsulation and dynamic context extension (technical proposal and verification) # Preface # Claude Skills introduced a remarkably advanced concept: encapsulating professional knowledge, process standards, and task methods into “skill packages” that load dynamically, only when a task actually needs them.