The Orchestration Tax: Why Multi-Agent Systems Get Expensive

How context propagation, supervisor loops, tool calls, memory, and observability quietly drive up the cost of production agentic systems. Multi-agent AI systems are quickly becoming a default pattern for building advanced LLM applications. Instead of relying on one model loop to plan, reason, retrieve, call tools, validate outputs, remember context, and produce the final answer, teams split the work across several specialised components. A planner decomposes the task. A supervisor routes work. Specialist agents handle narrow responsibilities. Tool agents interact with APIs, search systems, databases, or internal services. A verifier checks the output. A memory layer provides continuity. An observability system records what happened. This looks like sensible software architecture. Responsibilities are separated. Agents can specialise. Workflows become easier to monitor. Complex tasks can be broken down into smaller steps. But once these systems move from demos to production, a second re