Multi-Agent Systems: When and How to Coordinate

A single agent with access to many tools can accomplish a lot. But some tasks are complex enough that one agent becomes unwieldy: too many tools to track, too many decision points, too much context to manage.

The solution seems obvious: use multiple agents, each specialized for a part of the task. A researcher agent finds information, a writer agent synthesizes it, an editor agent refines the output.

This can work beautifully. It can also create a coordination nightmare where agents duplicate work, contradict each other, or wait indefinitely for each other to finish.

Multi-agent systems are powerful but have overhead. Use them when the benefits outweigh the complexity.

When One Agent Is Enough

Simple linear workflows: If the task is a sequence of steps without much branching, a single agent can handle it. The agent calls Tool A, observes the result, calls Tool B, and so on. No need for multiple agents.

Shared context requirements: If every decision depends on information gathered earlier, splitting across agents means constantly passing context between them. A single agent with full context is simpler.

Low tool count: If the agent only has access to 5-10 tools, managing them all is straightforward. Splitting into multiple agents adds coordination cost without reducing cognitive load.

Fast iteration cycles: If the task requires many tight feedback loops (like debugging code), context-switching between agents adds latency. Keep it in one agent.

When Multiple Agents Make Sense

Distinct skill domains: If the task requires both deep technical analysis and creative writing, two specialized agents with different tool sets and prompts might work better than one generalist.

Parallel execution: If subtasks can happen independently (research Topic A and Topic B simultaneously), multiple agents can work concurrently, reducing total latency.

Scale and specialization: If you’re building a system that handles many different user requests, routing to specialized agents (customer support agent, billing agent, technical agent) is cleaner than one mega-agent trying to do everything.

Clear ownership boundaries: If the task naturally divides into phases (research → synthesis → editing), dedicated agents for each phase can enforce quality gates and separation of concerns.

Coordination Patterns

How do multiple agents work together?

Sequential handoff: Agent A completes its task, passes results to Agent B, which completes its task and passes to Agent C. This is the simplest coordination pattern—no parallelism, just a pipeline.

Parallel execution with aggregation: Multiple agents work on subtasks simultaneously, and their results are combined at the end. For example, multiple research agents each investigate different sources, then a synthesis agent combines findings.

Hierarchical delegation: A manager agent breaks down a complex task into subtasks and assigns them to worker agents. The manager monitors progress and decides when the overall task is complete.

Peer collaboration: Agents work on the same task concurrently and negotiate or vote on decisions. This is the most complex pattern and requires careful design to avoid deadlocks or conflicts.

The Communication Problem

Agents need to share information. How do they do this without creating coupling nightmares?

Shared state: Agents read from and write to a shared data store (like a database or key-value store). This works but requires careful synchronization to avoid race conditions.

Message passing: Agents send structured messages to each other. This is cleaner than shared state but requires a message broker or orchestration layer.

Orchestrator-mediated: Agents don’t communicate directly. An orchestrator collects outputs from Agent A and passes them as inputs to Agent B. This centralizes coordination logic but creates a single point of control.

Event-driven: Agents emit events when they complete tasks or encounter issues. Other agents subscribe to relevant events and react accordingly. This is flexible but can be hard to debug.

Handling Agent Failures

In single-agent systems, if the agent fails, the whole workflow fails. In multi-agent systems, partial failures are common.

Graceful degradation: If Agent B depends on Agent A’s output but Agent A fails, can Agent B proceed with partial information or fallback data?

Retry and alternative routing: If Agent A fails, can the orchestrator retry with Agent A or route to a backup agent?

Failure propagation: If Agent A fails, should the entire workflow terminate, or should other independent agents continue?

Compensation logic: If Agent A completes but Agent B fails, can you roll back Agent A’s side effects (like deleting created resources)?

These questions don’t have universal answers—they depend on your use case. But you need answers before deploying multi-agent systems to production.

Context Management Across Agents

Each agent needs enough context to do its job, but not so much that token limits are exceeded.

Minimal context passing: Only pass the information the next agent actually needs. If Agent A collects 50 data points but Agent B only needs 3, pass those 3.

Summarization layers: If Agent A generates a long research report, have it or an intermediary summarize key points before passing to Agent B.

Contextual retrieval: Instead of passing all context explicitly, store it in a shared system and let agents retrieve what they need on-demand.

Hierarchical context: Manager agents maintain high-level context; worker agents maintain detailed context for their subtasks. The manager doesn’t need to know implementation details.

Coordination Overhead

Every agent added to the system increases complexity:

More failure modes: Each agent can fail independently, creating combinatorial failure scenarios.

More communication: Agents need to exchange information, which means serialization, deserialization, validation, and potential network latency.

More debugging: When something goes wrong, you need to trace across multiple agents to understand the root cause.

More testing: You need to test each agent individually and all interactions between agents.

This overhead is only worth it if the benefits (specialization, parallelism, modularity) are significant.

When to Use a Single Agent with Tool Delegation

Often, you don’t need multiple agents—you need one agent with access to tools that are themselves complex subsystems.

Instead of a “research agent” and a “writing agent,” you have one agent with a “research tool” (which might internally use an LLM to search and summarize) and a “writing tool” (which might internally use an LLM to generate prose).

This gives you modularity without multi-agent coordination. The agent calls tools, observes results, and iterates—all in one context.

This is usually simpler and more reliable than true multi-agent systems.

Practical Multi-Agent Architectures

Router-worker pattern: A router agent classifies user requests and routes to specialized worker agents. Each worker handles its domain independently. This scales well and keeps agents simple.

Map-reduce pattern: A manager agent splits a task into independent subtasks, worker agents execute them in parallel, and an aggregator agent combines results. This works well for parallelizable tasks.

Pipeline pattern: Agents arranged in a sequence, each doing one transformation step. Data flows through the pipeline like an assembly line. Simple to reason about and debug.

Committee pattern: Multiple agents attempt the same task independently, and their outputs are combined (by voting, averaging, or another aggregation method). This improves robustness but increases cost.

Observability in Multi-Agent Systems

Debugging multi-agent systems requires rich tracing:

Distributed tracing: Assign each workflow a trace ID and propagate it across all agents. This lets you reconstruct the full execution flow.

Agent-level logs: Each agent logs its decisions, tool calls, and observations. Correlate these with the trace ID.

Coordination logs: The orchestrator or communication layer logs all handoffs, messages, and synchronization points.

Visualization tools: Build dashboards that show agent interactions, data flows, and timing. This makes it much easier to understand complex workflows.

The Right Mental Model

Multi-agent systems are distributed systems. All the challenges of distributed computing apply: coordination, consistency, failure handling, debugging complexity.

Don’t use multiple agents because it sounds sophisticated. Use them when:

• Subtasks are truly independent and benefit from parallelism
• Specialization significantly improves performance or reliability
• The task is complex enough that a single agent’s context becomes unmanageable
• You’re willing to invest in coordination infrastructure and observability

Start with a single agent. Only split into multiple agents when you have clear evidence that the benefits outweigh the complexity.

And when you do build multi-agent systems, invest heavily in orchestration, error handling, and observability—because the failure modes are far more complex than single-agent systems.