Multi-Agent Orchestration

When and how to decompose AI tasks across multiple agents: orchestration patterns, coordination strategies, and the data infrastructure that makes multi-agent systems reliable.

Multi-agent systems exist because some tasks are too complex, too long, or too multi-dimensional for a single agent context window. A single agent writing a 50-page report degrades over its context; an orchestrator that fans out to specialized sub-agents and synthesizes their outputs does not. The pattern is the same as parallel computing: decompose the problem, execute in parallel where possible, aggregate the results. The data engineer's job is to build the infrastructure that makes the decomposition and aggregation reliable.

The orchestrator-subagent pattern is the dominant design. An orchestrator agent receives the high-level task, decomposes it into sub-tasks, dispatches them to specialized agents, collects results, and synthesizes a final output. Data engineers own the infrastructure: the message passing between agents, the shared state store, the result aggregation pipeline, and the failure and retry logic. These are data pipeline problems with agent skins on them.

Coordination is the hardest problem in multi-agent systems. When agents share state -- a database, a file system, a work queue -- you have all the classical distributed systems problems: write conflicts, ordering guarantees, partial failures, and partial completions. Use the same tools you use for distributed data pipelines: idempotent writes, explicit ordering, transactional boundaries where you can afford them, and compensating actions where you cannot. The multi-agent framing does not make these problems go away; it just makes them harder to see.

Trust is a non-obvious design constraint that expands with every agent layer. A subagent receives instructions from an orchestrator, which may itself have been given instructions from an uncontrolled external source. The attack surface for prompt injection grows at each hop. Apply the same principle as distributed system security: minimum necessary permissions per agent, treat all inter-agent messages as untrusted input, validate tool arguments regardless of which agent generated them.

Key resources

• Building a multi-agent research system (Anthropic Engineering)

  A detailed technical account of a real multi-agent system in production. Covers orchestration patterns, failure modes, and the infrastructure decisions that determined system reliability. Read this before designing your own.

• LangGraph Documentation

  The most production-ready graph-based agent orchestration framework. LangGraph models agent workflows as directed graphs with explicit state transitions -- which makes multi-agent coordination debuggable in ways that flat chain approaches are not.

• AutoGen Documentation (Microsoft Research)

  Microsoft's research-oriented multi-agent framework. Strong on multi-agent conversation patterns and role specialization. Useful for understanding the design space beyond LangGraph and seeing what agent coordination looks like from a research angle.