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Lesson 4 of 5

Multi-Agent Orchestration

4 min read

Why Single Agents Hit Limits

A single agent can handle a lot, but some tasks are inherently too complex for one reasoning chain. Consider a security assessment: you need to scan infrastructure, analyze code, review configurations, check compliance, and synthesize findings. One agent trying to do all of this loses focus, hits context limits, and produces shallow results.

Multi-agent systems solve this by dividing cognitive labor. Each agent specializes, and an orchestration layer coordinates their work.

Multi-Agent Patterns

Supervisor

One agent acts as a manager, delegating tasks to specialist agents and synthesizing their results. The supervisor decides what needs to be done, assigns work, reviews outputs, and handles conflicts.

Supervisor Agent
  ├── Security Scanner Agent
  ├── Code Reviewer Agent
  ├── Compliance Checker Agent
  └── Report Writer Agent

Best for: tasks where a central authority needs to coordinate diverse subtasks and make final decisions.

Pipeline

Agents are arranged sequentially — each agent's output becomes the next agent's input. Think of an assembly line where each station adds value.

Requirements Agent → Design Agent → Code Agent → Review Agent → Test Agent

Best for: workflows with clear stages where each stage's output is well-defined.

Debate

Multiple agents analyze the same problem independently, then a judge agent evaluates their arguments. This surfaces blind spots and produces more robust conclusions.

Best for: high-stakes decisions, code review (multiple reviewers catch more issues), and situations where diverse perspectives improve quality.

Swarm

A pool of similar agents works on a collection of tasks. A dispatcher assigns tasks dynamically based on availability and capability. No strict hierarchy — agents self-organize.

Best for: processing large batches of similar tasks in parallel, like reviewing hundreds of pull requests or scanning multiple repositories.

Communication Between Agents

Agents need to share information effectively:

Shared context: All agents read from and write to a common knowledge base. Simple but can lead to conflicts and information overload.

Message passing: Agents communicate through structured messages. More controlled but requires defining a message protocol.

Blackboard architecture: A shared workspace where agents post findings and read others' contributions. The blackboard serves as both communication channel and collective memory. This pattern works well when agents need to build on each other's work without tight coupling.

Task Decomposition

The supervisor (or orchestrator) must break complex goals into assignable subtasks. Effective decomposition follows these principles:

  • Independence: Subtasks should be as independent as possible to enable parallel execution.
  • Clear boundaries: Each subtask has well-defined inputs, outputs, and success criteria.
  • Right granularity: Too coarse means agents struggle; too fine means overhead dominates.

Monitoring and Observability

Multi-agent systems are harder to debug than single agents. Essential practices:

Structured logging: Every agent action, decision, and communication is logged with timestamps and agent IDs. Use tools like OpenTelemetry for distributed tracing.

Token and cost tracking: Each agent's token usage is tracked independently. This reveals which agents are efficient and which are wasteful.

Decision auditing: When things go wrong, you need to trace back through the decision chain. Why did the supervisor assign this task? Why did the agent choose this approach?

Cost Management

Multi-agent systems multiply costs. Key strategies:

  • Use cheaper models for simpler agents — not every agent needs a frontier model. A dispatcher or formatter can run on a smaller model.
  • Cache aggressively — if multiple agents need the same data, fetch it once.
  • Set hard token budgets per agent and per task.
  • Short-circuit — if an early agent determines the task is trivial, skip the remaining pipeline stages.

Real-world systems like code review pipelines, security assessment teams, and research workflows all benefit from multi-agent orchestration — but only when the added complexity is justified by the task at hand.