Custom AI agent development for the workflow only you run
Most of the work worth automating is specific to one company: a process built around a system nobody else uses, encoding rules that live in one person’s head. We scope that honestly, prove it on seeded data, and ship it with the same approval gates as everything else we build.
Example agent run, run · custom-workflow-agent. plan: workflow mapped, 6 steps. tool call: internal.fetch_batch(day="2026-09-09"). result: 212 records, 9 need judgement. approval: waiting for human. approved: by ops@client.com. tool call: internal.commit_batch(...).
How this work happens today
The process lives in one person
It is not written down because it changes, and it changes because the rules are judgement calls. That person is the reason it works, and also the reason it cannot scale or take a holiday.
Off the shelf tools cover eighty percent
Which sounds good until you notice the remaining twenty percent is where all the actual difficulty is, and now the work is split across a tool and a spreadsheet with a person reconciling them.
The last automation attempt was abandoned
Usually because it was built to a specification written before anyone understood the exceptions, went live all at once, and broke something visible. The team now treats automation as a risk rather than a tool.
How custom AI agent development works with us
The sequence matters more than the technology. Most failed agent projects failed in scoping, not in engineering.
We map the workflow with the person who runs it
Including the exceptions, because the exceptions are the specification. A process described from the outside is always simpler than the process actually is, and building to that description is how projects fail.
We tell you which parts should not be automated
Almost every workflow has a step that depends on judgement, relationship or accountability. Marking those as human by design is what makes the rest safe to automate, and we would rather scope work out than pretend.
You see it working before anything is connected
The whole system runs end to end on seeded data, with no live credentials anywhere. You evaluate the real behaviour, including failure behaviour, before you decide whether to connect it to anything of yours.
It ships in stages, behind flags
One workflow, one connector, one write path at a time, each reversible. Nothing goes from zero to running your operation in a single release.
You own the result completely
Source, prompts, evals and infrastructure config, in your repositories and your accounts, from the first commit. There is no runtime you rent from us and no component we hold back. If you decide six months in that you would rather maintain it yourself, or hand it to another firm, nothing about the way it was built stops you.
The same gate applies, whatever the workflow
A custom build does not mean custom safety rules. Reads run free, writes stop for a person with a diff of what will change, and the agent has no access it was not explicitly granted. When a workflow turns out to need an unattended write, that is a deliberate decision with its own review, not a default we quietly ship with.
How approval gates workWhat it connects to
If it has an API, a database or a file drop, it can usually be reached. If it has none of those, we will say so during discovery.
- REST and GraphQL APIs
- PostgreSQL and MySQL
- Internal tools
- SFTP and CSV drops
- Webhooks
- Message queues
- Claude API
- LangChain
Where to go next
Questions about this
How do you price something you have not scoped?
We do not. A two week discovery sprint produces a working prototype, an integration map and a build estimate, and it is priced on its own. If you stop there, you keep the prototype and the map.
What if discovery shows it is not worth building?
Who maintains it afterwards?
Can you work with our existing engineering team?
Describe the workflow
Two weeks from now you could be looking at it running on seeded data, with an honest estimate for the rest.