Quick take
More agents doesn’t mean better results. It means more coordination overhead and more failure modes. Start with a simple pipeline, add a verifier, and only go multi-agent when you can clearly define who owns each decision. If your agents don’t have contracts, you don’t have orchestration – you have chaos.
I keep getting asked about multi-agent architectures. Teams see the demos – agents collaborating, debating, building things together – and they want that. What they usually need is simpler.
The uncomfortable truth about agent orchestration is that it’s just distributed systems with worse debugging tools. Every coordination problem you’ve seen in microservices shows up again: unclear ownership, implicit state, cascading failures, and the seductive illusion that more components mean more capability.
That said, there are real use cases where multiple agents outperform a single one. The key is choosing the right pattern and being honest about the tradeoffs.
The four patterns
After building and reviewing agent systems in production, I’ve landed on four patterns that cover most real-world use cases.
1. Sequential pipeline
The simplest pattern. Agent A does research, passes results to Agent B for analysis, then Agent B passes to Agent C for writing. Each agent has a clear input and output contract.
When it works: Tasks with a natural sequence of distinct steps. Content generation pipelines. Data processing workflows. Anything where each step builds on the previous one.
When it breaks: Early agents produce weak output and later agents can’t recover. Errors compound. The pipeline is only as good as its weakest step.
My rule: Add explicit checkpoints between stages. If Agent B receives garbage from Agent A, it should reject and request a retry rather than trying to work with bad input. We learned this the hard way on a project – a research agent that returned vague summaries poisoned every downstream step.
2. Parallel execution
Multiple agents work on the same problem independently, then results are merged. Think: three agents each review a PR from a different angle (logic, security, performance), and a synthesis step combines their findings.
When it works: Tasks where multiple perspectives add value. Review workflows. Risk assessment. Brainstorming alternatives.
When it breaks: The synthesis step. Merging conflicting agent outputs is hard. If your merge strategy is “average the results” or “take the longest response,” you’re losing the benefit of parallel execution.
My rule: Define merge rules explicitly. Conflicts get escalated to a human or resolved by a designated arbiter agent with clear criteria.
3. Hierarchical orchestration
A coordinator agent breaks work into subtasks, delegates to specialist agents, and assembles the final result. This is the manager-worker pattern.
When it works: Large, complex tasks that can be decomposed. Project planning. Multi-file code generation. Report compilation from multiple data sources.
When it breaks: The coordinator overfits to its initial plan. If subtask results invalidate the plan, the coordinator needs to replan. Most implementations don’t handle this well – the coordinator stubbornly follows the original decomposition even when evidence says it shouldn’t.
My rule: Give the coordinator explicit replanning triggers. If a subtask fails or returns unexpected results, the coordinator reassesses before continuing.
4. Debate and verification
Two or more agents argue opposing positions. A judge agent evaluates the arguments and makes a final call. This pattern surfaces assumptions and edge cases that a single agent misses.
When it works: Decisions with genuine uncertainty. Code review where the tradeoffs are unclear. Risk assessment where different framings lead to different conclusions.
When it breaks: Agents generate artificial disagreement to fill their roles. Or the judge defaults to the more verbose argument. The pattern needs real divergence to add value.
My rule: Only use debate when the single-agent answer has measurable uncertainty. If the task has a clear correct answer, debate is overhead.
Pattern comparison
| Pattern | Best for | Failure mode | Complexity | Agent count |
|---|---|---|---|---|
| Sequential pipeline | Step-by-step workflows | Error compounding | Low | 2-4 |
| Parallel execution | Multi-perspective review | Bad merge logic | Medium | 3-5 |
| Hierarchical | Large decomposable tasks | Rigid planning | High | 3-8 |
| Debate/verification | Uncertain decisions | Artificial disagreement | Medium | 2-3 |
The coordination basics nobody talks about
The pattern is the easy part. The hard part is the coordination contract between agents. Every agent needs:
- Defined inputs and outputs. Not “whatever seems relevant.” A schema. Required fields. Validation at the boundary.
- Pass/retry/escalate criteria. What does the next agent do when it receives bad input? Accept it? Reject it? Ask for clarification? This must be explicit.
- Short, stable context. Don’t pass the entire conversation history between agents. Pass a structured summary of what the previous agent decided and why. Long contexts lead to confusion and drift.
- Decision logging. Every agent decision gets logged with reasoning. When the final output is wrong, you need to trace which agent made the bad call and why.
Without these, adding agents just multiplies failure modes. You get more components and less reliability. I’ve seen teams build five-agent systems that performed worse than a single well-prompted model because coordination overhead drowned out the benefits.
When to not use multi-agent
Most of the time.
I’m serious. A single agent with good tools, clear instructions, and a verification step handles 80% of use cases better than a multi-agent system. Multi-agent adds value when:
- The task genuinely requires different capabilities or perspectives
- Verification needs to be independent from generation
- The work can be parallelized for speed
- No single prompt can hold all the necessary context
If none of those apply, you’re adding complexity for its own sake.
How I start
Two agents. One that does the work. One that checks the work. That’s it. The generator-verifier pattern is the simplest multi-agent setup and the one with the highest reliability improvement per unit of added complexity.
Once the generator-verifier is stable and measured, you can consider whether splitting the generator into specialized sub-agents would help. Usually it doesn’t. But when it does – when you have distinct expertise domains that benefit from isolation – the improvement is real.
Start simple. Add complexity only when you can measure the improvement. Orchestration isn’t a goal. Reliability is.