Agent Orchestration Patterns for Production

Agentic AI systems are harder to reason about than pipelines. A pipeline has a fixed execution graph; an agent chooses its own path. Here are the orchestration patterns that bring structure to that chaos.

Pattern 1: Sequential Chain

The simplest pattern. Agent A produces output; Agent B consumes it. No parallelism, predictable behavior, easy to debug.

Use when: order matters and each step genuinely depends on the previous one.

Breaks when: any step in the chain has high latency or variable reliability. A 3-step chain where each step has 95% reliability has 85.7% end-to-end reliability.

Pattern 2: Parallel Fan-Out / Fan-In

Dispatch multiple agents simultaneously, aggregate results. Reduces latency proportional to the slowest sub-task rather than the sum.

Use when: sub-tasks are independent. Research agents querying different knowledge sources, evaluators checking different quality dimensions.

Breaks when: sub-tasks have hidden dependencies that create inconsistent outputs when run simultaneously.

Pattern 3: Routing Agent

A lightweight classifier or LLM decides which specialized agent handles a request. Cheaper than running every agent on every request.

Use when: you have clearly separable intent categories with different tool access requirements.

Breaks when: the router makes poor classification decisions. This failure is invisible — the wrong agent runs and produces a confident-sounding wrong answer.

Pattern 4: Hierarchical Planning + Execution

A planner agent decomposes a complex goal into sub-tasks; executor agents carry out each sub-task. The planner reviews results and adjusts.

Use when: tasks are genuinely complex and require dynamic replanning.

Breaks when: the planner generates invalid sub-tasks or loses track of completed work across long sessions. Memory management is the hard problem here.