Why multi-agent systems need explicit orchestration A single AI agent calling a single tool is manageable. Multiple agents — each with their own tools, context, and objectives — interacting to complete a workflow is a coordination problem. AI orchestration coordinates multiple models and agents through defined handoff protocols. Without orchestration, multi-agent systems produce inconsistent outputs that compound downstream. The coordination failures are predictable. Agent A passes incomplete context to Agent B. Agent B makes a decision based on that incomplete context. Agent C receives Agent B’s decision without knowing the context was incomplete. The error propagates and amplifies through the chain. In a system with four or five agents, a single context-passing failure in the first stage can produce wildly incorrect final outputs — with no stage in the pipeline detecting the problem. Orchestration patterns Pattern Structure Best for Risk Sequential pipeline A → B → C → output Linear workflows with clear stage boundaries Single point of failure; one slow agent blocks all Router + specialists Router dispatches to specialist agents based on input type Variable input requiring different expertise Router misclassification sends input to wrong specialist Supervisor + workers Supervisor delegates sub-tasks, aggregates results Complex tasks decomposable into independent sub-problems Supervisor must understand enough to validate worker outputs Consensus / voting Multiple agents process same input, majority rules High-stakes decisions requiring redundancy Expensive (3×+ compute); correlated errors still propagate Event-driven Agents react to events and publish results for others Asynchronous workflows, microservice-style independence State consistency across agents is hard to maintain What makes orchestration work Effective orchestration requires explicit state management between agent steps — implicit context passing (just appending to conversation) breaks at production complexity. The specific requirements: Defined handoff schemas. Each agent must declare what it produces and what it expects as input. Handoffs without explicit schema validation produce “works in testing, fails in production” failures when one agent’s output format drifts slightly. State persistence outside agents. The orchestration state — what has been done, what remains, what intermediate results exist — must live in a persistent store, not in any single agent’s context window. Agent context windows are limited, lossy, and non-auditable. Failure propagation boundaries. When Agent B fails, the orchestrator must decide: retry Agent B, skip it and proceed with partial results, or abort the entire workflow. These decisions cannot be delegated to the failing agent itself. Observability at orchestration level. Per-agent logging is necessary but insufficient. The orchestrator must log the flow: which agents were called in what order, what was passed between them, how long each took, and where failures occurred. This is the only way to debug multi-agent failures post-hoc. The cost multiplication problem Multi-agent orchestration multiplies costs in ways that are not obvious at design time. A workflow using five agents, each consuming roughly 3,000 tokens per step, running three steps on average, costs around 45,000 tokens per execution. At 1,000 executions per day, that is 45M tokens daily — a material cost that has to be budgeted and optimised. Cost management in orchestrated systems requires token budgets per agent per step, early termination when partial results are sufficient, caching of repeated sub-queries, and careful routing to avoid calling expensive agents for simple tasks. When should you use orchestration vs a single capable agent? Multi-agent orchestration adds value when different steps require fundamentally different capabilities (code execution plus web search plus document analysis), when steps need different context windows (one agent needs the full document, another needs only a summary), when reliability requires redundancy (critical decisions verified by independent agents), or when workflows are modular and individually testable. Single-agent approaches are preferable when the entire task fits within one model’s capabilities and context window, when coordination overhead would exceed the benefit of specialisation, when latency constraints preclude multi-step agent communication, or when the task is simple enough that orchestration complexity is unnecessary. For teams working through the broader question of how multi-agent systems coordinate and where they break, the orchestration layer — how it handles state, failure, and observability across agents — is typically more important than any individual agent’s capabilities.
