Multi-Agent System Development Services

Splitting a task across several agents can improve quality: one plans, one researches, one drafts, one checks. It also multiplies the ways things go wrong. Agents can loop, contradict each other, or pass along an early mistake that every later step builds on. Costs climb quickly when each step calls a large model.

We only recommend a multi-agent design after confirming a single agent with good tools cannot do the job. Then we define each agent's role, inputs and outputs, decide how they share state, and add a supervisor that limits loops, checks intermediate results and escalates to a person when confidence is low. We build with frameworks such as LangGraph, CrewAI or the OpenAI Agents SDK, depending on the task. You get tracing for every run, cost per task, and an evaluation set that tests the whole chain, beyond each agent alone.