How We Architect Multi-Agent AI Systems

Most AI features begin life as a single prompt glued to an API call. That works for a demo. It falls apart the moment the task needs multiple steps, tools, or decisions — the prompt balloons, behavior becomes unpredictable, and nobody can explain why the model did what it did.

When a workflow genuinely needs more than one reasoning step, we reach for a multi-agent architecture: several focused agents, each with a narrow job, coordinated by an orchestrator. Done well, this is easier to reason about than one giant prompt. Done badly, it's a distributed system with a language model in the critical path — which is as scary as it sounds.

Here's how we keep it on the "done well" side.

Start with the workflow, not the agents

Before writing a single prompt, we map the actual workflow as a graph: what are the discrete steps, what information flows between them, and where does a decision need to be made? Only then do we decide which steps deserve their own agent.

A good rule of thumb: an agent should have one responsibility you can describe in a sentence. "Extract structured data from the document." "Decide which tool to call." "Draft the reply." If you can't describe it that cleanly, it's probably two agents.

Give each agent the smallest possible context

The biggest cost and reliability wins come from context discipline. Each agent should receive only what it needs — not the entire conversation history, not every tool result, not the kitchen sink.

• Smaller context means lower token cost.
• Smaller context means the model is less likely to get distracted or hallucinate.
• Smaller context means you can actually read the prompt when debugging.

Make the orchestrator boring

The orchestrator — the thing that routes between agents — should be as deterministic as you can make it. Where possible, we use plain code for control flow and reserve the model for the parts that genuinely need judgment.

The best multi-agent systems use the LLM for reasoning and regular software for everything else.

This is the single most important lever for reliability. A model deciding "what should happen next" on every step is a system you can't test. A model deciding "what's the answer to this specific sub-question," inside a control flow you wrote, is a system you can.

Build evaluation in from day one

You cannot improve what you cannot measure. For every agent we build, we keep a small set of representative inputs and expected behaviors, and we run them on every change. It doesn't need to be fancy — even a handful of cases catches the majority of regressions before they reach a user.

Instrument everything

When something goes wrong in production (and it will), you need to see exactly what each agent received and produced. We log the inputs, outputs, tool calls, and token usage for every step. This turns "the AI is acting weird" into "agent 3 received a malformed input on line 42" — a problem you can actually fix.

The payoff

Structured this way, a multi-agent system stops being a black box. Each piece is small, testable, and replaceable. You can swap a model, tighten a prompt, or add a step without holding your breath. That's the difference between an AI feature that demos well and one that survives contact with real users.

If you're building something that's outgrowing a single prompt, get in touch — this is exactly the kind of work we love.