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/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 4–5 agents, a single context-passing failure in the first agent 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 Agent B 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 that uses 5 agents, each consuming 3,000 tokens per step, running 3 steps on average, costs 45,000 tokens per workflow execution. At scale (1,000 executions/day), this is 45M tokens/day — a material cost that must 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 to use orchestration vs a single capable agent Multi-agent orchestration adds value when: Different steps require fundamentally different capabilities (code execution + web search + document analysis) Steps need different context windows (one agent needs the full document, another needs only a summary) Reliability requires redundancy (critical decisions verified by independent agents) Workflows are modular and individually testable Single-agent approaches are preferable when: The entire task fits within one model’s capabilities and context window Coordination overhead would exceed the benefit of specialisation Latency constraints preclude multi-step agent communication The task is simple enough that orchestration complexity is unnecessary For teams evaluating AI agent frameworks for production, the orchestration capabilities of the framework — how it handles state management, failure recovery, and observability across agents — are typically more important than the individual agent features it supports.
