The OpenAI Agent Builder agency.Agents that reach prod.
A canvas with a half-finished workflow ships nothing. We design your agent on the canvas, wire it to your real tools and data through the Connector Registry and MCP, set the guardrails and evals that make it safe to launch, then deploy via ChatKit or the Agents SDK.
★★★★★Verified Trustpilot reviews · AI, automation & growth agency
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GeminiAn Agent Builder agency gets it to production, not just onto the canvas.
Anyone can drag a few nodes around. Designing a workflow that holds up, wiring it to your real data, and hardening it with guardrails and evals is a different job. Here are the four things we own.
- Agent design
Workflows designed on the visual canvas, not a whiteboard
A demo agent and a production agent are different animals. We design your workflow on the Agent Builder canvas: drag-and-drop nodes for each step, typed inputs and outputs, branching and control flow, preview runs on real data. We scope what the agent owns and where a human stays in the loop, so what you deploy is the thing you actually tested, not a prompt that worked once in a screenshot.
See a typical build - Tools & data
Wired to your real tools, data and systems
An agent that can't reach your data just talks. We connect Agent Builder to your stack through the Connector Registry and MCP servers: your CRM, your docs, your database, your internal APIs, file search and web search where it helps. We wire the tool calls the workflow needs with scoped permissions you control, so the agent acts on your real systems instead of guessing from a generic model.
See the integrations - Guardrails & evals
Guardrails and evals, so it's safe to ship
An agent loose in production with no guardrails is a liability. We configure the guardrail nodes (PII, jailbreak, off-topic, schema checks), set human approval where the stakes are real, and build an eval suite with datasets and trace grading so you measure quality before and after every change. You ship on evidence the workflow behaves, not on a vibe that the demo looked good.
See the method - Deploy & enablement
Deployed with governance, owned by your team
We deploy the workflow where it earns its place: embedded in your product with ChatKit, or exported as Agents SDK code in Python, Node or Go to run on your own infra via the Responses API. We set up versioning, logging and rollback, then train your team to iterate without us. We're an automation and AI agency first, so this plugs into how you already build and ship.
See AI enablement
We build agents like software, not a slideshow.
Most Agent Builder projects stall the same way: a workflow half-built on the canvas, no tools wired, no guardrails, no evals, and nobody confident enough to ship it. So we treat the agent like a product: designed on the canvas, connected to real data, hardened with guardrails and a measured eval suite, then deployed with versioning and a human in the loop where it counts.
- Audit · map the process you want to automate and whether an agent is the right tool
- Design · build the workflow on the canvas, scope tools, set the human-in-the-loop steps
- Harden · guardrails, evals and trace grading, so it's safe and measured before launch
- Deploy · ship via ChatKit or the Agents SDK, with versioning, logging and handover
We build agents on Agent Builder, then prove they work.
We don't sell a partner tier. We build real agents on the canvas and ship them, so we design them the way they hold up in production: scoped tools, guardrail nodes, a measured eval suite, and a human approving the calls that carry weight. That's exactly what's missing when a project ends at a workflow drawn on the canvas.
- We build real agents with Agent Builder, so we design them the way they hold up in production, not the way a launch demo suggests.
- Guardrails and evals first: we wire safety nodes and a measured eval suite so the agent ships on evidence, not on a vibe.
- You leave autonomous: the workflow, its versions and the playbook live in your account, so your team iterates without us.
- We'll tell you when not to use it. Some agents are simple enough for the canvas; some need real code, and we say which.
Agent Builder at the core, your stack wired around it.
We configure the parts that turn a canvas workflow into a reliable production agent, then connect them to how you already build. Here's what a real Agent Builder project covers.
- Setup
Canvas workflow design
We build your multi-step workflow on the Agent Builder canvas: nodes for each step, typed inputs and outputs, branching, loops and control flow, with preview runs on live data before anything goes near production.
- Setup
Connector Registry & MCP
We wire the data and tools the agent needs through the Connector Registry and Model Context Protocol servers: your CRM, docs, database and internal APIs, all within scoped permissions your admins control.
- Setup
Guardrail nodes
We configure guardrails for PII, jailbreak attempts, off-topic input and output schema validation, plus human-approval steps where a decision carries real risk, so the agent stays inside the lines you set.
- Setup
Evals & trace grading
We build an eval suite with datasets, trace grading and automated prompt optimization, so you measure agent quality objectively and catch regressions before they reach a user.
- Setup
ChatKit & Responses API
We deploy the workflow with ChatKit embedded in your product, or export the Agents SDK code to run on your own stack via the Responses API, with the UI and streaming wired in.
- Setup
Versioning & handover
We set up workflow versioning, logging and rollback so changes are traceable and reversible, then hand your team the workflow and the playbook to iterate on their own.
We map the process you want to automate, you leave with a plan.
Before quoting anything, we take 60 minutes to look at the process you want an agent to run, the data it touches, and whether Agent Builder is the right fit. You leave with an honest read on what to build on the canvas, what to wire first, and the guardrails and evals you need. Zero pitch, just an engineer's take on your workflow.
- An honest read on whether Agent Builder fits your process
- The workflow and tool connections to build first
- The guardrails and evals worth wiring early
- A frank take on what it won't do well
How we run an Agent Builder project.
Five steps, in order. We don't connect your real data before the workflow behaves on preview, we don't launch before guardrails and evals are wired, and your team owns the account at the end. Each step has a deliverable and you sign off before we move on.
- Step 1 · Process audit
Map the process before you build the agent
We sit down and look at the process you want an agent to run: the steps, the data it touches, the decisions, where humans must stay in the loop. We check whether Agent Builder is even the right fit. Half the value is telling you when a visual workflow is perfect and when the job needs code or a simpler automation, so you don't build an agent against a problem it can't own.
- Step 2 · Canvas design
Design the workflow on the visual canvas
We build the workflow on the Agent Builder canvas: a node for each step, typed inputs and outputs, branching and control flow, and the tool calls the agent needs. We run it on preview data as we go, so by the time we connect your real systems the logic already behaves. You see the workflow take shape and sign off on what the agent owns versus what a human approves.
- Step 3 · Tools & data
Connect it to your real tools and data
We wire the agent to your stack through the Connector Registry and MCP servers: your CRM, docs, database, internal APIs, plus file search and web search where they help. Each tool call gets scoped permissions you control. The agent stops guessing from a generic model and starts acting on your real data, which is the difference between a clever demo and a useful agent.
- Step 4 · Guardrails & evals
Harden it with guardrails and an eval suite
Before launch we configure the guardrail nodes (PII, jailbreak, off-topic, schema validation) and add human-approval steps where the stakes are real. We build an eval suite with datasets and trace grading so you measure quality on real cases, not anecdotes. Every change after that gets re-run against the evals, so you catch a regression before a user does, not after.
- Step 5 · Deploy & hand over
Deploy, then get out of the way
We ship the workflow where it belongs: embedded with ChatKit, or exported as Agents SDK code on your own infra via the Responses API. We set up versioning, logging and rollback, then train your team to read traces, tweak nodes and run the evals on their own. If you want us on call for the next agent, we talk about that separately. The account and the playbook stay yours.
We're judged on the agents that ship.
No partner badge to display, so we lead with what matters: feedback from the teams whose Agent Builder workflow we designed and deployed, and whether the agent kept earning its place after we left. Our Trustpilot reviews come from those teams, not from a marketing deck.
- The workflow, its versions and the playbook live in your account
- Guardrails and evals wired before the agent goes live
- Tool calls scoped, human approval kept where stakes are real
- Trustpilot reviews come from the teams we built agents for
The questions we get asked on repeat.
What does an OpenAI Agent Builder agency actually do?
An OpenAI Agent Builder agency designs, wires and deploys production agents on AgentKit so they actually work, instead of leaving you with a canvas nobody finished. We map the process, build the workflow on the visual canvas with typed nodes and control flow, connect your tools and data through the Connector Registry and MCP, configure guardrails and an eval suite, and deploy via ChatKit or the Agents SDK. The point is an agent live in production with governance, not a demo that worked once.How much does an Agent Builder project cost?
It depends on scope: a single canvas workflow with two tool connections is nothing like a multi-agent system wired into your CRM, your database and your internal APIs with a full eval suite. We don't throw out a flat package. We start with a free 60-minute audit to see whether Agent Builder fits your process and what to build first, then quote a fixed scope. The OpenAI usage itself you pay OpenAI directly; we design the workflow so token and tool costs stay predictable.Is Agent Builder good enough for production, or do we need code?
Both have their place, and we'll tell you which you need. Agent Builder's visual canvas is excellent for a large class of workflows: support triage, lead qualification, research, internal copilots, document processing. For those, the canvas plus guardrails and evals gets you to production fast. Some agents (heavy custom orchestration, unusual latency or state needs, deep system logic) are better written as code with the Agents SDK. We design on the canvas where it fits and drop to code where it doesn't, instead of forcing one tool on every job.Can you connect Agent Builder to our tools and data?
Yes, that's where an agent earns its place. We wire it through the Connector Registry and MCP servers to your real systems: CRM, docs, database, internal APIs, plus file search and web search where they help. Each tool call runs with scoped permissions your admins control. An agent that can only talk is a chatbot; an agent that can read and act on your data inside guardrails is the thing worth deploying, and connecting it properly is most of that work.What are guardrails and evals, and why do they matter?
Guardrails are the safety nodes you place in the workflow: PII detection, jailbreak filtering, off-topic blocking, output schema validation, and human approval where a decision carries risk. Evals are how you measure the agent objectively, using datasets and trace grading to score quality on real cases. Together they let you ship on evidence the agent behaves, then catch regressions when you change a prompt or a node. Skipping them is how a slick demo becomes a production incident.How do you deploy an agent built on the canvas?
Two main ways, and we pick the one that fits. ChatKit embeds the workflow into your product as a chat experience by passing the workflow ID, so you get a UI and streaming with little custom code. Or we export the workflow as Agents SDK code in Python, Node or Go and run it on your own infrastructure via the Responses API, which gives you full control over orchestration and hosting. Either way we wire versioning, logging and rollback so deployment is traceable, not a one-way door.Will an agent replace our team?
No, and we won't pretend otherwise. An Agent Builder workflow is very good at the repetitive, well-defined parts of a process (triage, lookups, drafting, routing) and it still needs people to set direction, handle the judgment calls and own the outcome. We design the workflow with human-in-the-loop steps exactly where a decision carries weight. The win is freeing your team from the mechanical work, not removing them from it, and we'll be honest about where the agent still needs a human.How long does an Agent Builder project take?
For a scoped single-workflow agent (canvas design, a couple of tool connections, guardrails, a starter eval suite, deploy), count a few weeks: audit and design first, then tools, hardening and launch. A multi-agent system wired across several internal systems runs longer. We build in batches so you get a working, guardrailed agent in production early, rather than waiting on a big buildout before anything is live and earning its keep.
Stop leaving it on the canvas. Ship it to prod.
A 60-minute audit, the process you want to automate mapped, a build plan with the guardrails and evals baked in. If your team can run it in-house after the build, we'll hand you the playbook. If we're the right fit, we handle it.