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Architectural patterns for building AI agent systems, ranging from simple single-agent setups to complex multi-agent orchestrations
Overview
Agentic patterns define how AI agents are structured, coordinated, and controlled to accomplish tasks. Choosing the right pattern involves trade-offs between control, flexibility, cost/latency, and complexity.
Pattern Comparison
| Pattern | Use Case | Control | Flexibility | Cost & Latency | Complexity |
|---|---|---|---|---|---|
| Single Agent | Simple prototypes | Full (one agent) | Low | Low | Low |
| Sequential / Parallel | Structured workflows | Moderate | Moderate | Moderate | Moderate |
| loop_review_critique_pattern | Tasks with strict criteria | Iterative | Moderate | High (loops) | High |
| coordinator_router_pattern | Complex task routing | Delegated | High | High | High |
| agent_as_tool_pattern | Fine control with sub-agents | Full (primary) | High | High | High |
Key Takeaways
- Single / Sequential / Parallel agents suit most straightforward workflows
- Loop agents shine when outputs must meet non-negotiable constraints (e.g., budget limits, safety checks)
- Coordinator agents excel at decomposing complex requests into subtasks handled by specialists
- Agent-as-tool keeps centralized state management while leveraging specialized capabilities
- More advanced patterns increase latency and cost — only use them when simpler patterns fall short
- Foundational concepts: llm_tool_use (how agents interact with external functions) and agent_memory_and_state (how agents retain context across steps)