Building AI Agents
Multi-Agent Architectures
Supervisor, Pipeline, Debate & Swarm patterns with architecture diagrams.
Interview: High - Core system design question at AI-first companies.
When One Agent Isn't Enough
Complex tasks benefit from multiple specialized agents. But "just add more agents" is a recipe for chaos. You must choose the right coordination pattern based on the task structure.
1. Supervisor Pattern
A single "manager" agent receives the user request, plans the subtasks, delegates to specialized worker agents, and aggregates results.
Best for: Complex orchestration with clear subtask decomposition. Reports, analysis, code generation.
2. Pipeline Pattern
Agents process sequentially. Each agent refines the output of the previous one. Think assembly line.
Best for: Content creation, CI/CD automation, data processing pipelines.
3. Debate Pattern
Multiple agents argue opposing perspectives and a judge agent selects the best argument. Reduces hallucination through adversarial verification.
Best for: Fact-checking, risk analysis, decision-making under uncertainty.
4. Swarm Pattern (OpenAI)
Agents self-organize without a central coordinator. Each agent has a handoff() function to transfer control to another agent when the task is outside its scope.
Best for: Customer support with departments, triage bots, open-ended workflows.
Which Pattern Should You Use?
| Pattern | Control | Latency | When to Use |
|---|---|---|---|
| Supervisor | Centralized | Higher | Complex orchestration with subtask planning |
| Pipeline | Sequential | Predictable | Linear workflows (generate → review → publish) |
| Debate | Adversarial | High | High-stakes decisions, fact verification |
| Swarm | Decentralized | Low | Dynamic routing, customer support triage |
Use Cases
Enterprise customer support with specialized department agents
Automated code review with architect, security, and performance agents
Research pipelines that plan, search, analyze, and synthesize
Content workflows (research → write → edit → SEO → publish)
Common Mistakes
Using multi-agent when a single well-prompted agent would work — start simple first
No maximum iteration limit — agents can loop forever bouncing tasks between each other
Cost explosion: each agent step = API call. A 5-agent pipeline with 3 rounds = 15 LLM calls per request
Agents with overlapping responsibilities causing confusion about who handles what