Custom AI Agent Development

A custom agent is ready for release only when one checkable job, its tool permissions, stop conditions, recovery paths, traces, and human intervention rules form one bounded loop.

Bring us one repeatable job with an input your team recognizes and a result they know how to judge. We build the shortest agent loop that can do it, then trace where it finishes, gets stuck, fails, and hands the work back to a person. At handoff, your release owner receives a working agent slice, reviewed task traces, explicit tool authority, and a first-use plan for intervention, rollback, and recovery.

Discuss the first agent jobSee the AI agent service
Illustration of Custom AI Agent Development: a team testing an AI agent's tools and decision boundaries

Some of the 500+ brands we've worked with

See all references
  • Defacto
  • Tosla
  • Neova Sigorta
  • Ajansspor
  • DLive
  • Amazon
  • BMW
  • Shell
  • Hyundai
  • PepsiCo
  • Red Bull
  • Decathlon
  • MediaMarkt
  • Bayer
  • Sanofi
  • EY
  • KPMG
  • GE
  • 3M
  • Domino’s
  • Lexus
  • Trendyol
  • Hepsiburada
  • Yandex

The run order, and who signs each step off

How we work

We begin with one path all the way from input to a checked result. A second tool, another recovery route, or a broader release condition comes later, when the trace gives us a reason to add it.

  1. Put the job in writing

    We sit down with the workflow owner and pin down the input, acceptable result, forbidden actions, approved tools and data, and known failures. We also agree the cost and latency limits and who may approve an exception. The workflow owner draws the job boundary and has the final word on tools and exception authority.

  2. Make one complete path work

    We implement the goal, state, memory, grounding, tool contracts, permissions, and stop conditions needed to carry one representative task through a thin end-to-end path in the real workflow. Before the test set runs, your engineering lead signs off on tool access and permission boundaries.

  3. Test the paths that go wrong

    We run representative, edge, and adverse tasks. Their traces expose invalid actions, repeated side effects, stalled loops, recovery attempts, missed escalation, cost, latency, and incomplete work. If any failure classes are acceptable for a limited release, that call belongs to your security lead.

  4. Leave the team with working controls

    Before handoff, we tie the reviewed traces to release limits, monitoring, intervention, rollback, and recovery, then assign the operating duties. The release owner sets the rollout limits and names who can pause the agent.

Named artifacts you keep

What you get

You get the working slice and the record behind it. That gives the owner something concrete to use when deciding whether the agent stays narrow, changes, or takes on more work.

  • Architecture document

    Agent loop, state, and stop-condition map

    A readable map of the agent's goal, loop, states, memory, grounding, stop conditions, tool limits, and ownership.

  • Matrix

    Tool contracts and escalation authority record

    The contract for each approved tool, alongside its schema, permissions, approval points, forbidden actions, resource budgets, and escalation route.

  • Test evidence

    Reviewed trajectory traces and failure findings

    The representative, edge, and adverse tasks we ran, with traces for completion, invalid action, recovery, escalation, and resource use.

  • Playbook

    First-use intervention and recovery plan

    The agreed rules for first use, monitoring, intervention, rollback, recovery, and later review, with the operating duties attached.

Scope and honest limits

When to bring us in

The right first job is repeatable and already familiar to the people who own it. They can recognize the input, judge the result, and show us how the work usually goes wrong.

A good fit when

  • The job moves through tools and state changes, but one prompt or fixed rule cannot carry its trace from input to a checked result.
  • You can bring representative tasks, known failures, the required tool and data access, and somewhere safe to run the first tests.
  • Cost, latency, and recovery rules sit in different places, so the agent has no single boundary for intervention.
  • The repeatable job has a recognizable result, but the agent's state, memory, grounding, stop conditions, and completion loop are not defined together.
  • Approved tools exist, yet their contracts, permissions, budget limits, and routes back to a person do not form one authority boundary.
  • A working slice can complete familiar tasks, while its traces still do not cover the failures and bounded recovery paths agreed for testing.
  • End-to-end tasks can be tested, but first-use limits, intervention controls, and operating duties have not reached the release owner.

Better handled as other work when

  • You want an open-ended autonomous worker before its job, authority ceiling, and finish line are defined. Agent strategy should settle those choices first.
  • The agent needs high-impact access or irreversible actions before its controls are reviewed. Security design and authorization must come first.
  • You need Zeo to run production, acquire new data, or build integrations beyond the agreed slice. Those duties require separate scopes.

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

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 Can MutioğluOpenAI Models & APIs UsageVideoby Ozan KetenciA Hands-On Guide to Elevating Efficiency with OpenAI's Custom GPT - Ozan Ketenci, ZeoVideoby Yiğit KonurAI's Future: Mastering Prompt Engineering & AI Integration - @Yiğit Konur , Wope

Tools we use

Tools behind this work

  • OpenAI

    the model API and agent runtime the shortest working loop actually gets built on

  • LangChain

    the graph-based loop showing exactly where control hands back to a person

  • Pydantic AI

    the typed output layer that fails fast at the boundary instead of downstream

  • Langfuse

    the full step-by-step trace the page's own FAQ contrasts with the final answer

  • Braintrust

    the scoring harness that runs the paths that go wrong before rollout

  • Helicone

    the cost and latency log rollout authorization gets checked against, alongside correctness

  • Guardrails AI

    one of the working controls the team is left with, enforcing output at runtime

Next step

Bring us one job your team can judge

Show us the input, the known failures, and who owns the result. We'll build the smallest complete loop and give you the trace to inspect it.
Talk through the first job

Before you decide

Questions we hear a lot

How small can the first AI agent build be?

One job is enough. It needs a known input, a result someone can check, representative examples, familiar failure modes, and the minimum tools and data. Its owner must also set policy boundaries, cost and latency budgets, and who can approve an exception.

What does the trace tell you that the final answer does not?

A plausible final answer can hide a bad path. The trace shows every state change and tool call, so a reviewer can find invalid actions, repeated side effects, stalled loops, unsupported recovery, missed escalation, or resource spend that produced no useful progress. They don't have to take the agent's word for what happened.

What must be true before a limited rollout?

The agreed tasks have to pass their completion checks. Critical failures must stop or reach the right person, and operators need a clear way to pause or roll back the agent. The open limits stay visible. The release owner reads the evidence and decides whether the agent enters a limited rollout.

What happens when the agent sees a case we didn't test?

Some unfamiliar cases will become new exceptions because models vary, systems change, and work can fall outside the behavior tested, so the handoff only supports the job we actually ran.