Multi-Agent Systems — When One AI Isn't Enough
See how multi-agent architectures work with animated traces, orchestration patterns, and side-by-side comparisons. Learn when to split one agent into many — and when not to.
Multi-Agent Systems — When One AI Isn’t Enough
One agent that does everything eventually does nothing well.
A single AI agent can handle simple tasks. But give it a 10-step task that requires research, writing, fact-checking, and formatting? It loses context by step 5, starts hallucinating by step 7, and produces garbage by step 10. The fix: split the work across specialized agents.
1. Single Agent vs Multi-Agent — The Core Difference
A single agent handles every step sequentially in one long context window. A multi-agent system assigns each step to a specialist. Same task, completely different architecture.
One Agent vs Many — The Difference
A single agent handles everything. A multi-agent system divides and conquers.
The multi-agent approach looks more complex — and it is. But each agent has a focused role, a small context window, and a clear success criteria. That’s why the quality is higher.
2. Three Ways to Wire Agents Together
Not all multi-agent systems work the same way. The orchestration pattern you pick changes everything — speed, quality, cost, and debuggability.
3 Ways to Wire Agents Together
Click each pattern to see how it works and when to use it.
01Hub & Spoke (Orchestrator)One boss, many workers. Most common.▼
A central orchestrator agent receives the task, breaks it down, and delegates to specialized agents. Each worker reports back. The orchestrator assembles the final answer.
02Pipeline (Sequential)Each agent's output feeds the next.▼
Agents run in sequence like an assembly line. Agent 1 researches, Agent 2 drafts, Agent 3 reviews. Each transforms the output and passes it along.
03Debate (Adversarial)Agents challenge each other for quality.▼
Two or more agents generate competing answers, then a judge agent picks the best one — or asks for revisions. Used in high-stakes decisions where accuracy matters more than speed.
Start with Hub & Spoke. It’s the easiest to build, the easiest to debug, and handles 80% of real-world use cases. Move to Pipeline for assembly-line workflows. Use Debate only when wrong answers have real consequences.
3. The Handoff — Making Agents Talk to Each Other
The biggest mistake in multi-agent systems: passing raw conversation history between agents. It fills up context windows with noise. Instead, pass structured data — summaries, key findings, and tagged outputs.
The Handoff — How Agents Pass Context
Clean handoffs make or break multi-agent systems. Here's the right way.
Think of it like a relay race. You don’t pass the entire track history to the next runner — you pass the baton. Clean, minimal, exactly what they need to do their job.
4. Watch Three Agents Collaborate — Full Trace
Here’s a real multi-agent execution trace for writing a technical blog post. Watch how the orchestrator delegates, agents report back, the reviewer catches errors, and the writer fixes them.
Multi-Agent Trace — Watch Them Collaborate
Task: "Write a technical blog post about Kubernetes security"
Notice the feedback loop: the review agent found issues, and the writing agent fixed them. This self-correcting behavior is impossible with a single agent — it can’t review its own blind spots.
5. The Numbers — When Multi-Agent Pays Off
Multi-agent systems cost more and add complexity. They’re not always the right call. Here’s the data on when they’re worth it and when a single agent is better.
When Multi-Agent Is Worth the Complexity
More agents = more cost. Here's when the trade-off pays off.