Short answer
A multi-agent system is several specialized AI agents, each with its own role, plus an orchestrator that routes work and combines results. The main patterns are sequential, parallel, hierarchical and event-driven. Costs run roughly 3x to 15x a single agent. Use a multi-agent system when one agent's context window can't hold the task, or when parallel work saves real time.
What is a multi-agent system, really?
Several agents with separate jobs, plus a coordinator. One agent runs one loop: call the model, call a tool, read the result, repeat. A multi-agent system runs several of those loops at once, each with a defined role, and an orchestrator hands out the work and pulls the results together.
The f3fundit.com orchestration guide (March 2026) explains it well: 'Your AI agent handles customer support. Another scrapes competitor pricing. A third writes follow-up emails. They all work, but they don't talk to each other. You're running three isolated agents when you need an orchestrated system. The difference? Orchestration lets Agent A pass context to Agent B, which triggers Agent C only when conditions match.' That coordination is the whole point.
So it isn't about throwing ten agents at a job one could do. It's specialisation. A researcher that only researches. A coder that only codes. An ops agent watching both. Wire them together with clear handoffs and you get work none of them could produce alone.
LangGraph, CrewAI or AutoGen: which framework should I use?
LangGraph if you need control, CrewAI if you need speed, AutoGen if your agents should argue it out.
LangGraph is the most widely used in production. It treats a workflow as a graph: each node is a step (a model call, a tool call, a human check) and the edges say what happens next. You get exact control over order, branching and parallel runs, plus checkpointing with PostgresSaver and the ability to rewind and replay. It takes a lot of setup. Once it's built, it does exactly what you told it.
CrewAI works at a higher level. You describe agents by role (researcher, writer, analyst), give them tasks, and it handles the coordination. It's quicker to get going than LangGraph, with less say over the exact flow. f3fundit's March 2026 AutoGen vs CrewAI comparison calls it the fastest way to a working prototype: 'no manual glue code, no brittle cron jobs, just workflow logic that adapts.' AutoGen, from Microsoft, is built around conversation. Its agents talk to each other instead of passing tasks, which suits work where they debate or refine each other's output.
Hermes Agent has coordination built in. The main agent can start up to 3 subagents at once, give each a structured task and combine what they return, with no extra framework. Hivra runs Hermes on each agent's own computer but adds no orchestrator of its own, so the coordination there is Hermes's own delegation.
Which orchestration pattern fits my job?
| Pattern | How it works | Use it for | Watch out for |
|---|---|---|---|
| Sequential pipeline | A hands to B, B hands to C | Stages that build on each other: extract, clean, analyse, report | Simple, and if B fails, A's and C's work is untouched |
| Parallel | An orchestrator hands out N independent tasks, waits for all of them, combines them | Research where each agent covers one competitor | Hermes's 3-subagent limit covers most real research jobs |
| Hierarchical (supervisor) | A supervisor splits the task, reviews each worker's output, sends work back if needed | Hard reasoning tasks | The most expensive, because the supervisor re-reads every worker's output |
| Event-driven | Agents fire when something happens, not on a timer | An alert agent wakes a research agent when a competitor's page changes | Hermes supports it: gateway hooks fire on every message in and out, plugin hooks catch tool calls to route them |
Start with the simplest pattern that fits. You can always add a supervisor later. Taking one out is harder.
When should I not use multiple agents?
Most of the time, honestly. Multiple agents add coordination overhead, harder debugging and a bigger bill. They're worth it when one agent's context really can't hold the job, when different parts of the job need different models or settings, or when running in parallel saves time you actually care about.
Skip it if one coherent loop can do the job (most jobs), if your single-agent version still has bugs (fix that first), or if money is tight, since coordination can mean 15x the tokens of a single chat. Gartner's 2026 enterprise AI report found that most teams who went straight to multi-agent setups in 2025 ended up rebuilding simpler single-agent versions once they saw the overhead outweighed the gain.
How much do multi-agent systems cost?
Somewhere between 3x and 15x a single agent, depending on the pattern. A simple sequential pipeline runs about 3 to 5x (several agents each doing part of the work, plus the coordinating calls). Five agents researching in parallel come to roughly 5 to 8x. A supervisor with 3 workers hits 10 to 15x, because the supervisor reads everything every worker produces.
One team in a Promethium platform comparison watched its bill jump from $1,200 to $4,800 a month after moving search, a chatbot and internal tools from one agent to several, with no way to see where the tokens were going. What fixed it: sending simpler subtasks to cheaper models, and giving each agent its own token budget. Hermes Agent lets you set both per subagent profile.
Common questions
What is the difference between a multi-agent system and running multiple separate agents?
Separate agents work alone, with no shared context and no coordination. A multi-agent system has an orchestrator that routes work, lets agents pass results to each other and combines everything into one answer. Without that layer you just have several agents running side by side.
Which multi-agent framework should I use in 2026?
LangGraph when you need exact control over flow, branching and checkpoints in production. CrewAI for quick prototypes built around roles. AutoGen for agents that work by talking to each other, especially in a Microsoft stack. And if you already use Hermes, its built-in subagents give you multiple agents without adding another framework.
Do multi-agent systems actually perform better than a single capable agent?
When the work splits into independent pieces, yes, by a lot. One agent researching 10 competitors one after another takes about 10 times as long as 10 agents doing it at once. When each step depends on the last, extra agents just add overhead.
How does Hermes Agent handle multi-agent coordination?
The main agent can start up to 3 subagents at once with its delegation tool. It writes each one's task, runs them in parallel, waits for the results and combines them. The handoff is structured (set task payloads and output formats), not open-ended chat between agents.
What is the main operational risk of multi-agent systems?
The bill, usually first. Token use grows faster than the number of agents, and a complex supervisor setup can easily hit 15x a single agent. Debugging comes second: when the final answer is wrong, you need a full record of what each agent passed to the next to find out who got it wrong.