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:

The Think-Act-Observe loop has a natural enemy: the context window is finite.

Every loop iteration burns tokens — the model’s thinking, the tool-call requests, the tool results. Reading one large file can cost thousands of tokens; a single test run’s output can cost tens of thousands. A dozen rounds in, the context window is stuffed full.

What happens when the window fills? The model says “sorry, this conversation exceeds my context limit”. For casual chat, who cares — open a new window. For a coding assistant, it means amnesia — it forgets the project structure, the changes already made, the user’s preferences.

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.

This “progressive disclosure” mechanism is essentially a dynamic context extension protocol — it effectively reduces the model’s context burden and markedly improves the quality of complex task handling.