AI Agent Architecture Patterns

The most effective AI agent architectures aren't novel inventions. They're translations of organizational structures that have worked for humans for decades. The manager-worker pattern, the assembly line, the specialist consultant, the review board -- these team structures map directly to agent architectures that AI systems execute reliably.

Understanding this connection transforms agent design from an abstract technical challenge into a concrete organizational modeling exercise. If you can describe how a team of humans would handle a task, you can design an agent architecture to handle it.

Key Takeaways

• Effective agent architectures mirror human team structures -- manager-worker, assembly line, specialist pool, and review board patterns all have direct agent equivalents
• The choice of pattern depends on the task structure, not the technology -- sequential tasks need pipelines, parallel tasks need pools, complex tasks need hierarchies
• Agent communication protocols matter as much as agent capabilities -- how agents share context determines system reliability
• Error handling in multi-agent systems requires escalation paths that mirror how human teams handle exceptions
• Start with the simplest pattern that works and add complexity only when the simpler pattern fails

Pattern 1: The Manager-Worker Hierarchy

Human Equivalent

A project manager decomposes a project into tasks, assigns them to team members, reviews results, and assembles the final deliverable.

Agent Architecture

A coordinator agent receives the high-level goal, breaks it into subtasks, delegates each subtask to a specialized worker agent, evaluates results, and synthesizes the output.

Coordinator Agent
├── Worker Agent: Research
├── Worker Agent: Implementation
├── Worker Agent: Testing
└── Worker Agent: Documentation

When to Use

Tasks that are naturally decomposable into independent subtasks with a clear assembly step. Code generation projects, documentation tasks, and analysis workflows fit this pattern well.

Key Design Decision

How much autonomy do workers get? In human teams, micromanaged workers are slower but produce more consistent results. Autonomous workers are faster but may diverge from the plan. The same tradeoff applies to agents.

For worker agents with high autonomy, provide clear acceptance criteria rather than detailed instructions. For tightly controlled workers, provide step-by-step instructions and checkpoint validation. The multi-agent coordination guide covers this tradeoff in detail.

Pattern 2: The Assembly Line (Pipeline)

Human Equivalent

A manufacturing process where each station performs one operation and passes the result to the next station. In software, this is the editorial workflow: writer creates, editor refines, fact-checker verifies, publisher formats and distributes.

Agent Architecture

A chain of agents where each agent's output becomes the next agent's input. Each agent has a narrow, well-defined responsibility.

Input → Agent A (Generate) → Agent B (Refine) → Agent C (Validate) → Output

When to Use

Tasks with a clear sequential workflow where each step transforms the output. Code review pipelines, content creation workflows, and data processing chains fit naturally.

Key Design Decision

What happens when an agent in the middle of the pipeline fails or produces substandard output? The assembly line needs quality gates between stages that can either reject and retry, or escalate to a supervisor.

async function pipeline(input: string) {
  const generated = await generateAgent.process(input)

  if (!qualityGate(generated, "generation")) {
    return await generateAgent.retry(input, generated.feedback)
  }

  const refined = await refineAgent.process(generated.output)

  if (!qualityGate(refined, "refinement")) {
    return await escalate(input, generated, refined)
  }

  return await validateAgent.process(refined.output)
}

Pattern 3: The Specialist Pool

Human Equivalent

A consulting firm where client requests are routed to the most relevant specialist. A tax question goes to the tax specialist, a legal question goes to the legal specialist. No single person handles everything.

Agent Architecture

A router agent examines incoming requests and delegates to the most appropriate specialist agent. Specialists are deep experts in narrow domains.

Router Agent
├── Database Specialist
├── Frontend Specialist
├── Security Specialist
├── Performance Specialist
└── Testing Specialist

When to Use

Tasks where the required expertise varies significantly between requests. Support systems, code analysis tools, and multi-domain development assistance benefit from specialist routing.

Key Design Decision

How does the router decide which specialist to use? Simple keyword matching works for obvious cases. For ambiguous requests, the router can use a lightweight AI model to classify the request before routing to the appropriate specialist. The tool calling patterns used in agent design apply directly to specialist selection.

Pattern 4: The Review Board

Human Equivalent

A peer review process where multiple reviewers independently evaluate the same work and their assessments are aggregated. Academic peer review, code review committees, and editorial boards follow this pattern.

Agent Architecture

Multiple evaluator agents independently assess the same input. A synthesis agent aggregates their evaluations into a final assessment.

Input → [Evaluator A, Evaluator B, Evaluator C] → Synthesis Agent → Output

When to Use

High-stakes decisions where a single agent's judgment is insufficient. Code security reviews, architectural decisions, and quality assessments benefit from multiple independent evaluations.

Key Design Decision

How are conflicting evaluations resolved? Majority voting works for binary decisions. Weighted scoring works for nuanced assessments. For critical decisions, disagreement should trigger human review rather than automated resolution.

The reflection pattern is a simplified version of this architecture where a single agent reviews its own work.

Pattern 5: The Blackboard

Human Equivalent

A team working on a complex problem with a shared whiteboard. Team members read the current state from the board, contribute their insights, and update the board. No one directs the process -- agents self-select tasks based on the current state.

Agent Architecture

A shared state object (the blackboard) is accessible to all agents. Each agent monitors the blackboard for conditions it can address, performs its work, and updates the blackboard.

Blackboard (shared state)
├── Agent A monitors for: incomplete analysis
├── Agent B monitors for: unresolved errors
├── Agent C monitors for: optimization opportunities
└── Agent D monitors for: missing documentation

When to Use

Complex, emergent problems where the solution path isn't known in advance. Debugging complex systems, exploring design spaces, and conducting open-ended research fit this pattern.

Key Design Decision

Concurrency control. When multiple agents try to update the blackboard simultaneously, conflicts arise. Use optimistic locking, event sourcing, or designated update windows to prevent conflicts.

Pattern 6: The Debate

Human Equivalent

A structured debate where participants argue opposing positions, and a judge evaluates the arguments to reach a decision.

Agent Architecture

Two or more agents argue different positions on a decision. A judge agent evaluates their arguments and selects the strongest position.

Decision Question
├── Advocate Agent A (argues for approach X)
├── Advocate Agent B (argues for approach Y)
└── Judge Agent (evaluates arguments, selects winner)

When to Use

Design decisions with genuine tradeoffs, technology selection, and architectural choices where multiple valid approaches exist.

Key Design Decision

How many rounds of debate? A single round captures initial arguments. Multiple rounds allow agents to respond to each other's points. Diminishing returns typically set in after three rounds.

Cross-Cutting Concerns

Communication Protocols

All multi-agent patterns require clear communication protocols:

Structured messages. Agents should communicate through typed, structured messages, not free-form text. This prevents misinterpretation and makes the system debuggable.

Context passing. Each agent needs enough context to do its job but not so much that it's overwhelmed. Design explicit context contracts that specify what information flows between agents.

Status reporting. Every agent should report its status (idle, working, blocked, failed) so the system can detect and respond to problems.

Error Handling

Multi-agent error handling requires escalation paths:

1. Retry -- the agent retries with the same input
2. Retry with feedback -- the agent retries with error context
3. Escalate -- a supervisor agent takes over
4. Fail gracefully -- the system returns a partial result with an explanation

Human teams handle exceptions the same way: try again, ask for help, escalate to management, or acknowledge the limitation.

Testing Multi-Agent Systems

Testing multi-agent architectures is harder than testing individual agents. Integration tests must verify not just that each agent works correctly in isolation, but that the communication between agents, the error handling, and the overall workflow produce correct end-to-end results.

For practical testing approaches, see our guide on building your first AI agent.

Choosing the Right Pattern

Start with the simplest pattern that addresses your task structure. Most teams begin with Manager-Worker and evolve toward more sophisticated patterns as their requirements become clearer.

FAQ

Can I combine multiple patterns?

Yes. A Manager-Worker system might use an Assembly Line for each worker's subtask and a Review Board for final quality assessment. Composition is the norm in production systems.

How many agents is too many?

When debugging becomes harder than the problem you're solving, you have too many agents. Start with two or three and add agents only when a clear need arises.

Do all agents need the same AI model?

No. Use cheaper, faster models for routing and classification agents. Reserve expensive, capable models for specialist agents that handle complex reasoning. This mirrors how human teams assign tasks based on seniority and expertise.

How do I debug multi-agent systems?

Log every message between agents with timestamps. When something goes wrong, replay the message log to identify where the communication or reasoning broke down.

What's the overhead of multi-agent versus single-agent approaches?

Multi-agent systems consume more tokens due to inter-agent communication. The overhead is justified when the task complexity exceeds a single agent's reliable capability. For simple tasks, a single agent is always more efficient.

Sources

• Multi-Agent Systems: Algorithmic, Game-Theoretic, and Logical Foundations - Academic foundations of multi-agent architecture
• Design Patterns: Elements of Reusable Object-Oriented Software - Classical pattern foundations adapted for agent design
• Anthropic Agent SDK Documentation - Practical agent implementation guidance

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