Multi-Agent System Development

A multi-agent system earns its coordination cost only when each role has a distinct job, bounded authority, traceable handoff, and a human completion owner.

Some jobs move from research to a tool action and then to evaluation, with no single agent equipped to finish the whole path. We build a bounded system for those jobs, then test stale state, role conflict, stalled work, budget pressure, and the route back to a person before your team releases it.

A named person can pause or accept the combined result, working from a replayable orchestration plan with its tested conflict responses and budget limits.

Discuss a multi-agent jobSee AI agent development
Illustration of Multi-Agent System Development: a team testing an AI agent's tools and decision boundaries

Some of the 500+ brands we've worked with

See all references
  • Hyundai
  • İyzico
  • Otsimo
  • Desa
  • Teyit.org
  • Vispera

Steps, gates, and who decides

How we work

We start at the seams between roles. The build has to show what crosses each handoff, who owns shared state, and how the system contains a disagreement, a stalled task, or a spent budget.

  1. A narrow job for every specialist

    With the workflow owner, we define the role's input, context, tools, authority ceiling, expected output, budget, finish condition, and the exact point where work moves to another agent or a person.

    AI assist
    Using the submitted workflow evidence, the model groups candidate role splits and budget assumptions for review.
    Human gate
    Does every role add a distinct capability and have a clear finish condition? The workflow owner sets each role's purpose, authority ceiling, budget, and finish condition.

  2. The handoff becomes a contract

    We connect routing, shared state, dependencies, conflict rules, and escalation. Every handoff names what moves, which role supplied it, who may change it, and who owns the combined result.

    AI assist
    The model flags shared-state dependencies, missing provenance, and handoffs with no usable receiver.
    Human gate
    Can every handoff and state change be traced to the role that supplied it? Routing, state ownership, conflict rules, and escalation need the workflow owner's approval.

  3. Conflict belongs in the test

    We run normal and adverse work through conflicting outputs, missing results, stale state, repeated tasks, incomplete handoffs, and pressure on token, time, tool, and concurrency budgets.

    AI assist
    The model proposes conflict and budget-pressure cases. A person chooses what enters the reviewed test set.
    Human gate
    Which critical coordination failures must resolve, stop, or reach a person before release. Every critical coordination failure goes to the workflow owner for a response decision.

  4. One accountable system changes hands

    We tie the tested traces to monitoring, budget ceilings, intervention rules, operating owners, and a review date. The handoff records which exceptions remain and who may pause the system.

    AI assist
    Reviewed traces give the model source material for a draft intervention and failure playbook.
    Human gate
    Accept, condition, pause, or reject the combined result, and assign intervention authority. The completion owner accepts the release decision, intervention authority, and review cadence.

Named artifacts you keep

What you get

The handoff leaves the orchestration inspectable. Operators can replay its decisions, see where each boundary sits, and find the person responsible when the system stops.

  • Playbook

    Replayable orchestration and intervention plan

    The implemented role map, routing and shared-state flow, conflict paths, budget ceilings, intervention rules, and responses exercised in testing.

  • Risk register

    Shared-state dependencies and budget-condition inventory

    Assumptions about each role's context and tools, plus shared-state dependencies, budget conditions, and open coordination questions.

  • Test evidence

    Coordination failure and exception report

    The observed results from normal coordination and from role conflict, missing work, stale state, repetition, budget pressure, and incomplete handoffs.

  • Decision record

    Combined-result acceptance and intervention log

    Completion criteria, remaining exceptions, operating owners, intervention authority, and the date the evidence is reviewed again.

Scope and honest limits

When to bring us in

Bring us a job that crosses genuinely different kinds of work and keeps failing when one agent is expected to carry the whole path.

A good fit when

  • One agent reaches its context or tool boundary halfway through the job, so the remaining evaluation and action work stalls.
  • Handoffs move shared state between specialist roles, but missing provenance and conflict rules make the combined result hard to trust.
  • The combined result reaches a human completion owner, yet coordination failures still leave them without a clear takeover point.
  • Each specialist needs a role contract, because its context, tools, authority ceiling, budget, finish condition, and handoff differ.
  • Shared state moves between roles, but nobody can trace who supplied it, who may change it, or who owns the combined result.
  • Token, time, tool, and concurrency budgets exist, but the stop and human-intervention rules are unclear.
  • Normal work completes in a demo, but adverse replays still expose stale state, repeated tasks, conflict, or incomplete handoffs.

Better handled as other work when

  • One agent or a simpler workflow already finishes the job, so multi-agent coordination would add handoffs without a distinct capability.
  • You want roles to expand their own remit or delegate without limits, while this build keeps authority ceilings and completion with people.
  • You need ongoing production operation or agent builds beyond the agreed workflow, because this handoff covers one tested orchestration.

If one of these is closer to your situation, start here instead: See AI agent development

Engineers who ship production AI

This is the part of Zeo that writes and ships code. Our senior engineers build agents, chatbots, and RAG pipelines, along with the automation and data work around them, and they keep operating those systems once they're live. We've worked with more than 500 brands since 2011.

Content we've produced on this topic

Articleby Burak PehlivanWhat is WebMCP? Agentic Web and Websites Interacting with AI AgentsArticleby Yiğit KonurMCP Server Architecture: State Management, Security & Tool Orchestration

Tools we use

Tools behind this work

  • LangGraph

    the state-graph framework where a handoff between specialists becomes an enforced contract

  • CrewAI

    the role-based framework giving each specialist its own narrow, declared job

  • Agno

    Lightweight multi-agent framework for building autonomous stateful AI agent workflows

  • Letta

    the persistent memory layer that keeps a long-running agent's state from going stale

  • Modal

    the compute layer scaling each specialist independently under budget pressure

  • Langfuse

    the connected trace across every specialist's steps in one multi-agent run

Next step

Bring us the job one agent can't finish

A useful first conversation starts with the job, the points where work changes hands, and whoever is accountable for completion. From there, we can see whether a specialist system deserves a build.
Review the role split

Before you decide

Questions we hear a lot

A role has to earn the coordination it adds. We look for a distinct context, tool set, or evaluation rule that the other roles should not absorb, then run representative work through the proposed split. One agent or a simpler workflow may still be the better design. If so, we keep it simple.