Multi-Agent Basics

Why Multiple Agents?

Single agents hit limits: context windows fill up, specialists outperform generalists, and some tasks benefit from parallel execution.

Multi-agent systems solve these limits by distributing work across specialized agents with clean handoffs.

The Orchestrator-Worker Pattern

The most common multi-agent architecture:

Orchestrator — receives the goal, breaks it into tasks, assigns tasks to workers, collects results, synthesizes the final output.

Workers — specialized agents that execute specific task types (research, writing, coding, analysis, etc.)

Workers don't need to know about each other — only the orchestrator needs the full picture.

Task Decomposition

The orchestrator's primary skill is decomposing a complex goal into independent subtasks. Good decomposition:

• Creates tasks with clear, testable completion criteria
• Minimizes dependencies between tasks (enables parallelism)
• Matches task type to worker specialization
• Handles failure: if worker A fails, can the goal still be achieved?

Communication Patterns

Request-response — orchestrator sends task, worker returns result. Simplest; works for most cases.

Streaming — worker streams output as it's generated. Better UX for long-running tasks.

Message queue — tasks published to a queue, workers pull and process. Best for high volume, decoupled systems.

Example: Research Report Agent

Orchestrator receives: "Write a competitive analysis of X vs Y."

It spawns:

• Worker A: research X (web search tool)
• Worker B: research Y (web search tool)
• Worker C: analyze financials (data tool)

Orchestrator receives all results, then uses a final synthesis agent to write the report.

Total time: parallel workers run simultaneously, so total time ≈ slowest worker, not sum of all workers.