Agents Built on OpenAI's Models and Tooling, When That's the Right Fit
We build agents on OpenAI's models and function/tool-calling framework for the tasks where they're the strongest option — and say so plainly when a different provider fits your task better. This page is about how we evaluate and build, not a spec sheet for someone else's product.
A Common Starting Point, Not the Only One
A lot of clients arrive already having tried OpenAI's tools directly — a prototype in a playground, a proof of concept that answered questions well but never touched a real system. That's usually the gap we're closing: the model was never the hard part, wiring it safely into your actual tools and defining what it can decide alone was.
Function/Tool Calling, Scoped Tightly
We define the exact set of actions the agent is allowed to call — nothing broader — and treat every one of them as something to test against edge cases before it goes live.
Retrieval Wired to Your Real Content
Answers and decisions are grounded in your actual documentation and records, not the model's general knowledge guessing at your business.
Explicit Escalation Boundaries
What the agent can decide alone and what it hands to a person is written down and tested — not left to the model to infer case by case.
What We Actually Weigh, Vendor-Neutral
We don't rank providers in the abstract — strength shows up differently depending on the task. These are the categories worth evaluating for yourself, whichever provider you're comparing.
Tool-Use Reliability
How consistently the model calls the right function with correctly formed arguments, especially under an ambiguous or partially-worded request.
Context Handling
Whether the agent stays grounded and accurate as the conversation or the document it's working from gets longer, not just at the first turn.
Cost Per Call
Priced against your expected call volume, not a headline rate — a cheaper-looking model that needs more retries can cost more in practice.
Latency for the Use Case
A live chat needs a fast response; a document batch processed overnight does not. We match the model's speed profile to how the agent is actually used.
We evaluate these against your specific task during scoping and tell you plainly where the trade-offs sit — not which provider we'd rank first in general, because there isn't a single honest answer to that question.
The Model Is One Layer, Not the Whole Agent
The model generates a decision. Everything around it — what data it's allowed to see, which actions it can call, what triggers a human handoff, what gets logged for review — is engineering work we do regardless of which provider sits underneath. That's the layer that determines whether an agent is trustworthy in production.
Practically, that means we design the decision boundaries and integration layer to be provider-agnostic where we can, so a future change in pricing, availability or capability at OpenAI doesn't mean rebuilding the agent from scratch.
See Our Engineering ApproachWe Test Against
Realistic inputs, ambiguous requests, and the edge cases most likely to trip up a tool call before launch, not just the happy path.
We Monitor For
Failed or malformed tool calls, unexpected model behaviour after a provider-side update, and drift in answer quality over time.
The Same Agent, Different Underlying Model
The job the agent does — support, sales, document processing, workflow — and the model it runs on are separate decisions. Start from the job.
Claude Agent Development
The comparable approach built on Anthropic's Claude models and tooling.
LangGraph Agent Development
For multi-step workflows better expressed as an explicit graph of steps and decisions.
AI Agent Engineering
How we approach evaluation, testing and monitoring, independent of the model choice.
OpenAI Agent Questions
Are you an official OpenAI partner?
We don't claim any formal partner or certification status, and we'd rather you trust what we show you about a build than a badge on this page. We build with OpenAI's publicly available models and tooling the way any development team can, and we're upfront about that.
Is OpenAI the model you always recommend?
No, and we'd be skeptical of anyone who always recommends the same model. We evaluate against the task — tool-use reliability, context handling, cost per call — and OpenAI's models are a strong fit for some of those, not all of them. See Claude Agent Development for the other side of that comparison.
Do you commit to a specific model version for a project?
We name the model family in scope and document what we tested against, but we don't promise a frozen version indefinitely — providers update and deprecate models on their own schedule, and pretending otherwise would set a client up for a surprise. Version changes get evaluated before an agent is moved onto one.
Can you build the same agent on a different model later?
Often, yes, if the agent's decision boundaries and integrations were designed cleanly in the first place — which is part of why we design that layer independently of the model underneath it. It's not always a trivial swap, and we'll say so plainly rather than promise a one-click migration.
What do you actually evaluate before choosing a model for a build?
Tool-use reliability on the specific actions the agent needs to take, how it handles the amount of context your task requires, latency against how the agent is used, and cost per call at your expected volume. We share that reasoning in scoping rather than asserting a winner up front.
Tell Us What the Agent Needs to Do
We'll tell you plainly whether OpenAI's tooling is the right fit for it, or whether another approach serves the task better.