Single-agent LLM systems are remarkable — until they aren’t. At some point in every complex operational workflow, you hit the ceiling: too many tools, too much context, too many decision branches for one agent to handle reliably.
The answer isn’t a bigger context window. It’s multi-agent orchestration.
Why Single Agents Break Down
The failure mode is predictable. You start with a clean agent: 5 tools, a clear objective, a focused system prompt. It works great in testing. Then production happens.
Your order exception agent needs to check 3 different systems, apply 12 different business rules, coordinate with the carrier API, update the ERP, and notify the right person — all while staying under a 30-second SLA.
A single agent trying to do all of this will:
- Lose track of context mid-task
- Use tools in the wrong order
- Hallucinate when the task graph gets complex
- Blow your token budget
The Architecture Pattern
We use a supervisor-worker pattern. The supervisor agent handles routing and coordination. Specialized worker agents handle discrete sub-tasks.
Supervisor Agent
├── Data Retrieval Agent (reads from ERP, WMS, TMS)
├── Decision Agent (applies business rules)
├── Action Agent (writes back to systems)
└── Notification Agent (handles comms)
Each worker has a narrow scope, a small tool set, and a focused system prompt. The supervisor maintains state, handles failures, and routes between workers.
What Goes Wrong
The two failure modes we see most often:
1. Supervisor overload. If the supervisor tries to do too much reasoning itself, you’ve just moved the problem up a level. The supervisor should route and coordinate, not solve.
2. Context collapse. Workers need enough context to do their job, but passing everything from the supervisor creates bloat. We use structured context objects — passing only what each worker needs.
Production Considerations
Before you ship a multi-agent system, you need answers to:
- What happens when a worker fails mid-task?
- How do you debug a decision made three agents deep?
- How do you attribute token costs to specific sub-tasks?
- What’s your retry and circuit-breaker strategy?
These aren’t edge cases. They’re Tuesday.
The observability layer is as important as the agent logic. Every agent call, tool use, and handoff should be logged with full context. When something breaks in production — and it will — you need a complete audit trail.
When to Use It
Multi-agent systems add complexity. Don’t use them unless you need them. You need them when:
- A single agent’s context window becomes a bottleneck
- You need parallel task execution
- Different sub-tasks require different specialized prompts or tools
- You want to gate expensive operations (like ERP writes) behind cheaper reasoning steps
For most starting points, start simpler. Add orchestration when the complexity earns it.