How planning divides work across specialist agents
Multi-agent systems rely on a planner that decomposes complex tasks into subtasks, then routes each to an agent with domain expertise. A planner might break down 'analyze a codebase and propose optimizations' into separate retrieval, analysis, and synthesis steps, dispatching them to researcher, coder, and architect agents. Each agent operates on its narrower domain, reducing hallucination and improving speed.
The handoff between agents preserves context through structured passes. The researcher returns findings that the coder uses as input, which the verifier cross-checks against the original goal. This staged execution forces each agent to produce testable outputs rather than speculating across multiple domains.
Why orchestration beats single-agent chains
A single LLM attempting all roles in sequence often compounds errors. The researcher may over-retrieve, the coder inherits that noise, and verification misses domain-specific flaws. Orchestration isolates failure: if the researcher fails, the planner reroutes; if the coder fails, only that subtask retries.
Specialization also enables different model sizes. A verifier may need less capability than a planner. This lets teams run small, fast models for straightforward tasks and reserve stronger models for true reasoning bottlenecks.
Routing decisions in practice
The planner must decide not just which subtasks exist, but also when to parallelize versus sequence them. Independent subtasks (gathering data from three sources) can run in parallel; dependent ones (code generation after requirements) must wait. This routing overhead is usually small compared to the time saved by agent specialization.