Multi-Agent Orchestration Patterns

Single-agent systems handle a wide range of enterprise tasks. Some problems exceed what one agent can address within a single session: the task requires parallel workstreams running simultaneously, different sub-tasks need genuinely different tools and capabilities, or the total context required exceeds what one agent can hold. Multi-agent systems distribute work across multiple agents that coordinate toward a shared goal.

Each agent in a multi-agent system retains its own reasoning model and harness: its own tools, memory, context management, and guardrails. What changes is the coordination layer on top. That layer introduces concerns that single-agent systems do not face: how context and state move between agents, how failures in one agent affect the rest, and how the combined system is observed and debugged.

Multi-agent is not automatically the right recommendation. The FDE proposes it only when the use case genuinely justifies the coordination cost. The orchestration pattern chosen before the build begins determines build complexity, cost per task, and what the customer’s team inherits when the engagement ends.

When multi-agent is the right architecture

Multi-agent architecture is justified when one or more of the following conditions hold:

• The task breaks down into sub-tasks that can run independently, where parallelism meaningfully reduces completion time.

• Different sub-tasks require distinct tool sets or specialized capabilities that a single agent cannot carry efficiently.

• The total context required exceeds what one agent’s context window can hold, even with compression.

When none of these apply, a single-agent system with a well-designed harness handles the problem at lower cost, with simpler debugging and easier handoff. Multi-agent adds coordination complexity, multiplies token consumption, and makes failures harder to trace. The FDE estimates cost ...