When You Need Multiple Agents
A single AI agent works fine for straightforward tasks: answer a question, search the web, do a calculation. But some problems require different capabilities in sequence or in parallel. A research task might need one agent to search, another to analyze, and a third to write a summary. A coding workflow might use one agent to plan, another to code, and a third to review.
Multi-agent systems split the work across specialized agents, each with its own tools, prompts, and responsibilities. The challenge is coordination.
Pattern 1: Supervisor Agent
One agent acts as the manager. It receives the task, breaks it into subtasks, assigns each subtask to a worker agent, collects the results, and synthesizes the final answer.
This pattern works well when the subtasks are clearly defined and the supervisor can determine the right order. It is the most common pattern in production systems because it gives you a single point of control.
Pros: Clear hierarchy, easier to debug, single agent responsible for final output quality.
Cons: The supervisor is a bottleneck. If it misunderstands the task, all downstream work is wasted. It also adds an extra LLM call per coordination step.
Pattern 2: Pipeline (Sequential Handoff)
Agents run in a fixed sequence. Agent A does step 1 and passes the result to Agent B for step 2, which passes to Agent C for step 3. There is no supervisor deciding what happens next. The order is hardcoded.
This works when the process is predictable. Content pipelines (research, write, edit, format) and data processing workflows (extract, transform, validate, load) fit this pattern naturally.
Pros: Simple to build and debug. Each agent has one job. No coordination overhead.
Cons: Inflexible. If one agent fails, the entire pipeline stops. Cannot handle tasks that require dynamic routing.
Pattern 3: Swarm (Peer Communication)
Agents communicate directly with each other, without a central supervisor. Each agent decides whether to respond, delegate, or pass the conversation to another agent based on the content of the message.
OpenAI's Swarm framework and AutoGen's group chat mode use this pattern. It is the most flexible but also the hardest to control.
Pros: No single point of failure. Agents can self-organize around complex tasks.
Cons: Hard to predict which agent will respond. Conversations can loop or drift. Debugging requires reading long chat transcripts.
Choosing a Pattern
| Pattern | Best for | Avoid when |
|---|---|---|
| Supervisor | Complex tasks with clear subtask decomposition | High-latency environments (adds coordinator calls) |
| Pipeline | Predictable sequential workflows | Tasks that need dynamic branching |
| Swarm | Research/brainstorming with exploration | Production systems that need deterministic behavior |
Common Pitfalls
Too many agents
Every agent adds latency and cost. A 5-agent system with 3 coordination steps means 8+ LLM calls per task. At $0.02 per call, that is $0.16 per query. At 10,000 queries per day, costs add up. Use the minimum number of agents needed.
Vague agent roles
If two agents have overlapping responsibilities, they will step on each other or both skip the work. Define sharp boundaries. Agent A does X and only X. Agent B does Y and only Y.
No error recovery
What happens when an agent returns garbage? Most multi-agent demos do not handle this. In production, add retry logic, output validation per agent, and a fallback path when an agent fails.
No observability
With multiple agents, you need to trace the full execution path. Log every agent call, its input, output, and latency. Without this, debugging is guesswork.
Frequently Asked Questions
Do multi-agent systems outperform single agents?
Not always. For simple tasks, a single well-prompted agent is faster, cheaper, and more reliable. Multi-agent systems help when the task genuinely requires different capabilities or perspectives.
Which framework should I use?
LangGraph for production systems (best control and state management). CrewAI for straightforward task delegation. AutoGen for research prototypes.