Multi-Agent Systems

Multi-Agent Systems

Some tasks are too large, too parallel, or too specialized for one agent. Multi-agent systems split work across multiple LLM-powered agents that communicate and coordinate.

The Orchestrator + Subagent Pattern

A central orchestrator breaks down a high-level goal and delegates subtasks to subagents. Each subagent has its own context window and tools. Results flow back to the orchestrator, which synthesizes and decides what comes next.

Why Split Across Multiple Agents?

Context limits — 200K tokens sounds large, but a complex project with many files and a long task history will exhaust it. Subagents handle focused sub-problems with clean contexts.

Parallelism — Independent subtasks execute simultaneously, dramatically reducing wall-clock time.

Specialization — Different agents use different models. A cheaper model for classification; a more capable one for synthesis.

Real Example: Software Development Pipeline

1. 1.Planner agent — Reads requirements, creates task list
2. 2.Coding agent — Implements features, has file read/write tools
3. 3.Testing agent — Runs test suite, reports failures
4. 4.Review agent — Checks quality, security, style compliance

Frameworks

• LangGraph — Graph-based workflow, excellent for complex state machines
• CrewAI — Role-based, fast to prototype
• AutoGen (Microsoft) — Conversation-driven, strong human-in-the-loop support

Failure Modes

• Infinite loops — Always set a maximum iteration cap
• Cost explosion — A 5-agent system × 10 steps = 50x the token cost of a single call
• Conflicting edits — Two agents writing the same file causes corruption; implement locking
• Error propagation — Bad subagent output becomes bad orchestrator input; add validation checkpoints