AI, explained
What is a multi-agent system?
Splitting one agent into five feels like architecture. Often it is just five times as many places for the thing to go wrong.
The pattern is real and useful in a narrower set of cases than the enthusiasm suggests. Understanding when it applies saves a lot of money.
The common shapes
- Orchestrator and workers. A coordinating agent breaks a task into pieces, dispatches them to workers, and assembles the output. The dominant pattern in practice.
- Pipeline. Each agent handles one stage and passes forward. Easier to reason about, but no parallelism benefit and often better implemented as ordinary code.
- Panel. Several agents attempt the same task from different angles and a judge picks or synthesises. Expensive, and worth it only where the cost of a wrong answer is high.
- Critic. One agent produces, another tries to find the flaw. This is the cheapest pattern with a real quality return.
When splitting genuinely helps
Three conditions, and you want at least two of them. The subtasks are independent, so they can run at the same time rather than in sequence. The context is large enough that one agent holding all of it degrades, which happens well before the context window fills. Or the subtasks need genuinely different instructions and tools, where a single system prompt trying to cover everything gets confused.
When it just adds failure modes
If the work is sequential, a multi-agent design gives you no speed benefit and adds handoff points where context gets lost. Each handoff is lossy: the worker only knows what the orchestrator told it, and the orchestrator only knows what the worker reported back. Latency compounds, cost multiplies because context is re-established per agent, and debugging gets substantially harder because a wrong final answer might have originated at any hop.
The practical rules
- Start with one agent. Split only when you can point at the specific problem the split solves.
- Brief workers precisely on first dispatch. Launching, waiting and re-briefing is where the cost goes.
- Do not use a worker to verify the orchestrator's work. Verification belongs in the main loop, and delegating it tends to produce agreement rather than scrutiny.
- Cap the number of concurrent workers. Without a ceiling, cost scales in a direction nobody predicted.
- Log every handoff with what was sent and what came back. Without that trace, a multi-agent bug is close to unfixable.
The honest summary
Multi-agent architectures are a parallelism tool and a context-isolation tool. If your problem is neither, a single well-instructed agent with good tools will usually be cheaper, faster and easier to fix. Digiton builds both and reaches for the single agent first.
Frequently asked questions
What is a multi-agent system?
It splits work across several AI agents, each with its own instructions, tools and context, typically coordinated by an orchestrator that dispatches subtasks and assembles results. Common shapes are orchestrator and workers, pipelines, judge panels, and a producer paired with a critic that hunts for flaws.
When should you use multiple agents instead of one?
When subtasks are genuinely independent and can run in parallel, when the context is large enough that a single agent degrades holding all of it, or when subtasks need genuinely different instructions and tools. If the work is sequential, splitting adds handoffs and latency without any benefit.
What goes wrong with multi-agent systems?
Every handoff is lossy, since a worker only knows what the orchestrator told it. Latency compounds, cost multiplies because context is re-established per agent, and debugging gets much harder because a wrong final answer could have originated at any hop. Log every handoff or it becomes close to unfixable.
Related
Ready to put AI to work?
Book a discovery audit and we will map the highest-ROI AI agents and automations for your business.
Book a discovery audit →