Multi-Agent Orchestration: A Production Guide

One agent is a tool. Multiple agents are a team. And the difference between a productive team and a chaotic group of individuals is orchestration.

I run multi-agent systems in production. Not demos. Not prototypes. Production systems that handle real workloads, serve real users, and need to be reliable at 3am on a Sunday when nobody is watching.

The gap between "multi-agent demo that works on stage" and "multi-agent system that works in production" is enormous. Demo systems assume perfect conditions. Production systems assume everything will break.

Why Multiple Agents?

The temptation is to build one super-agent that does everything. Give it every tool, every piece of context, every capability. One prompt to rule them all.

This doesn't work. Not at scale, not in production, not for complex tasks.

A single agent with 50 tools makes poor tool selection decisions. It conflates contexts. It gets confused about which step of the workflow it's in. It produces inconsistent results because the prompt is trying to do too much.

Specialized agents solve this. A code agent that only writes code. A review agent that only reviews code. A deployment agent that only handles deployments. A testing agent that only writes and runs tests.

Each agent has a focused system prompt, a small set of relevant tools, and a clear mission. The cognitive load per agent is low. The output quality per agent is high.

The orchestrator coordinates them. It breaks complex tasks into subtasks, assigns each subtask to the appropriate agent, manages the flow of information between agents, and assembles the individual outputs into a coherent result.

The Orchestration Layer

The orchestrator is the most critical component in a multi-agent system. Get it wrong and you get chaos, duplicated work, conflicting outputs, and cascading failures.

A good orchestrator does four things:

Task decomposition. Take a complex request ("Build a user authentication feature") and break it into ordered subtasks ("Design the database schema", "Create API endpoints", "Build the UI components", "Write tests", "Update documentation"). The decomposition should be deterministic for common task types.

Agent routing. Match each subtask to the agent best suited to handle it. This is a mapping problem: which agent has the right tools, context, and capabilities for this specific subtask?

State management. Track the progress of each subtask. Know which agents are busy, which have completed their work, and which have failed. Maintain the shared context that agents need to build on each other's outputs.

Result assembly. Combine the individual agent outputs into a single coherent result. Resolve conflicts. Ensure consistency. Validate the final output against the original request.

I implement orchestrators as state machines. Each state represents a phase of the workflow. Transitions are triggered by agent completions or failures. The state machine ensures that the workflow progresses correctly even when individual agents fail or produce unexpected outputs.

Error Handling at Every Level

In a multi-agent system, errors propagate. An agent fails. Its output is missing. The next agent in the chain receives incomplete input. Its output is wrong. The agent after that builds on the wrong output. By the time you detect the error, four agents have done wasted work.

The solution is error handling at every level.

Agent level: Each agent validates its inputs before starting work. If the input is incomplete or malformed, the agent reports the error immediately rather than producing garbage.

Orchestrator level: The orchestrator checks each agent's output before passing it to the next agent. If the output doesn't meet quality criteria, the orchestrator retries the agent, routes to an alternative agent, or halts the pipeline and reports the failure.

System level: Circuit breakers detect sustained failures in specific agents and remove them from the rotation. Health checks verify that agents are responsive and producing valid outputs. Fallback strategies route work to backup agents when primary agents are unavailable.

This three-level error handling makes the system resilient to individual failures without sacrificing overall reliability.

Resource Management

Each agent consumes resources. API tokens for the LLM calls. Memory for the context. Compute for tool execution. Network bandwidth for API calls.

In a multi-agent system, these costs multiply. Five agents running concurrently, each making LLM calls, each executing tools, each maintaining context. Without resource management, costs spiral and performance degrades.

Concurrency limits prevent resource exhaustion. Don't run more agents simultaneously than your infrastructure can support. Queue excess work and process it when capacity is available.

Token budgets cap the cost of each agent interaction. If an agent is burning through tokens without producing results, terminate it and report the failure rather than letting it consume unlimited tokens.

Request deduplication catches cases where multiple agents request the same information. If two agents need the same database query result, execute the query once and share the result.

Intelligent scheduling optimizes the order of agent execution. Independent subtasks run in parallel. Dependent subtasks run sequentially. The scheduler minimizes total wall-clock time while respecting resource constraints.

Observability

You cannot operate what you cannot observe. Multi-agent systems need comprehensive observability because the system's behavior emerges from the interactions between agents, and those interactions are difficult to predict.

Log every agent invocation with its input, output, duration, and cost. Log every orchestrator decision with its reasoning. Log every inter-agent communication with the message content and routing.

Build dashboards that show the real-time state of the system. Which agents are active. What tasks are in progress. Where bottlenecks are forming. What the current cost burn rate is.

Set alerts for anomalies. An agent that usually completes in 10 seconds is taking 60. An agent that usually succeeds is failing repeatedly. The overall system throughput has dropped by 50%.

Without this observability, debugging production issues in a multi-agent system is like debugging a distributed system without logs. Theoretically possible. Practically impossible.

Start Small, Scale Deliberately

Don't launch a ten-agent system on day one. Start with two agents and an orchestrator. Get the coordination patterns right. Get the error handling right. Get the observability right.

Then add agents one at a time. Each new agent adds complexity to the orchestration layer. Validate that complexity is warranted by the capability it provides.

The best multi-agent systems I've seen in production have between three and seven agents. Enough specialization to be effective. Few enough to be debuggable.