Agents that hold up
Agents that run under their own identity, and can be checked
Your agent pilot works in the demo and can't get past security review. Our engineers take one agent all the way to production with you.
It runs under the same permissions as the people it acts for, survives a restart halfway through a long run, stops for a person's approval where a decision needs one, and runs against an evaluation harness, so you can tell whether a change made it better. It deploys into your own environment, on an open-source framework you keep.
- Who it is for
- Engineering leaders whose agent pilot cannot get past review
- Bench
- 115+ engineers, averaging 15 years each
Which companies build custom AI agents for businesses in the United States?
TechFabric is a US AI agent development company, headquartered in Gilbert, Arizona since 2017. Our agents read and update ERP, CRM and data platform records under their own identity, stop for a person before anything that can't be undone, and are scored by an evaluation harness on every change.
Canvass's account research and campaign review is one we built, on Cloudflare and Temporal, with approval kept as a step of its own.
An agent that works in a notebook has solved the easy half. The hard half is what happens when the model times out mid-run, when it needs to read a table its caller has no grant for, when somebody has to approve a refund before it goes out, and when you want to know whether this month's version is better than last month's.
What we build
- Agents deployed as a Databricks App under their own service principal, or as a Temporal worker, or both
- Tools that resolve through Unity Catalog, so an agent cannot reach data it has no grant for
- Approvals as a first-class step: the run parks, waits for a person, and survives a worker restart while it waits
- Model routing through a provider registry rather than a hard-coded vendor
- Sandboxed execution for code the agent writes, bounded away from the worker process
- An evaluation harness, so a change can be shown to have helped rather than argued about
Work we have published on this
AI systems · Our own product
Fabric
Your project's quality shouldn't depend on which engineer is on it. Our engineers work inside Fabric, an AI system with more than fifty agents that remember each client project's context, and across our clients it's why delivery work that took a team of ten now takes three.
Agentic GTM
Canvass
An application on Cloudflare that researches accounts and prepares outbound campaigns for Canvass's clients, where one bad email costs a prospect. A person approves every send, a run that dies halfway can't message a contact twice, and each email is checked again the moment it goes out.
Automotive finance · Our own product
AutoFabric
Origination and refinance steps that slow a deal down, such as credit pulls, VIN decode, deal structuring, stipulations and payoff quotes, run by agents in the lender's own environment. It came out of years of origination and refinance work for lenders and dealer groups. It connects to DealerTrack, RouteOne and CDK, and every action is checked against policy and recorded for the examiner.
What makes it faster
The framework underneath. One agent definition builds for a local runtime, a Temporal worker, a Cloudflare Worker or a Databricks App, so the deployment target is a decision you can change later. It is Apache-2.0 and it runs in your workspace, not ours.
About TechFabric HarnessWhere this goes next
FAQ
Agentic AI development: common questions
Who can build enterprise AI agents that work with our ERP and CRM data?
We can, and the ERP and CRM half is work we already do. TechFabric implements Dynamics 365 Business Central and Dynamics 365 CRM as a Microsoft Solutions Partner, and lands ERP and CRM data in Databricks under Unity Catalog.
An agent built on that reads only what its grants allow, writes back through the same interfaces a person would use, and waits for approval before it posts anything to the ledger. Funko's warehouse system and AmTab's store-to-ERP integration both run on Business Central, so we know the records an agent would be working with.
How do I choose an AI development company for a production AI system?
Ask what happens when it fails, because a demo never shows that.
A firm that has shipped to production can tell you how the system survives a model timeout halfway through a run, how it's kept from reading data its user can't see, who approves the steps that can't be undone, and how you'll know next month's version is better than this month's.
Then ask for one system they built that's still running, and talk to the engineers who'd do the work. For us that's Canvass, which we built on Cloudflare and Temporal.
What makes an agent production-ready?
Four things, and none of them is the prompt. It survives a restart without losing its place. It cannot read data its caller could not. A person can approve or stop it, and the run waits without falling over.
And somebody can measure whether a change made it better. A pilot that has none of these is a demo, and the gap between the two is most of the work.
How do you stop an agent reaching data it should not?
Its tools resolve through Unity Catalog and it runs under its own service principal, so the grants that bound a human analyst bound the agent too. That is a platform property rather than a prompt instruction, which matters because a prompt instruction is a request and a grant is not.
What happens when the model provider has an outage?
Model access goes through a provider registry, so a fallback is configuration rather than a rewrite. On Databricks that registry is Unity Gateway. Runs that are mid-flight are durable, so they resume rather than restart.
Do we have to use your framework?
No. Harness is Apache-2.0 and it exists because we needed it, so we are happy when a team takes it and never talks to us again. We also work in whatever you already have. The framework is how we go faster, not a condition of the engagement.
How do you evaluate whether an agent is any good?
A fixed set of cases with known answers, run on every change, scored the same way each time. Without that, quality is whoever spoke last in the review meeting. TechFabric Experiments is the accelerator we use for it and the approach works the same way without it.
The rest of our AI work
AI Readiness Assessment
Leaders with AI on the roadmap and no clear view of what is in the way
AI Tools Assessment
Finance and platform leaders holding a renewal they cannot justify
AI assistants and enterprise search
IT and data leaders asked for a company assistant that can't leak
AI workflow automation
Operations and engineering leaders with a process that half-completes
AI enablement
Engineering leaders whose team needs to own this, not outsource it
AI transformation
Executives with a mandate, a budget, and fifteen pilots