No-FDE automation
The pilot worked. Then everyone waited for the engineers.
Most enterprise AI programs don’t stall on the model. They stall on people: a small team of engineers who have to build, test, deploy and look after every automation. Thunk.AI takes that team out of the critical path.
How it usually goes
Four stages most AI programs know too well
01
The pilot gets a great demo
A small team builds one workflow, often with the vendor’s forward-deployed engineers. It works well enough to win a budget.
02
Every next workflow joins a queue
The same few engineers are needed for the second workflow, and the tenth. Business teams wait months for a slot.
03
Every change becomes a ticket
Policies change, inputs drift, edge cases appear. Each fix goes back to the engineers, because nobody else can safely touch the code.
04
The program plateaus
Cost grows with every workflow, the backlog grows faster, and the knowledge of how anything works sits with people who may not stay.
Hiring more forward-deployed engineers doesn’t fix this. It just makes the queue more expensive.
What Thunk.AI does instead
Take the engineers out of the critical path
Process owners define the work
The people who run the process describe it in plain language, in the same terms they would use to train a new hire. There is no requirements document to hand over and no code to read.
An agent does the building
The Forward Deployed Agent turns that description into a thunk, writes the test cases, runs them and fixes what fails. When the process changes, the process owner asks the agent, not an engineering queue.
The platform makes it safe to run unattended
Thunks run on a runtime built for reliability, so people review exceptions instead of checking every result. That is what lets one process owner look after many workflows.
Side by side
Who does the work
FDE-led deployment
Thunk.AI
Defines
The process owner
The process owner
Builds
Forward-deployed engineers, in code, on SDKs and frameworks
The process owner with the Forward Deployed Agent, as a no-code thunk
Checks
People review the AI’s output, because it can’t be trusted unattended
The platform is reliable enough that people handle only the exceptions
Operates
The same engineers keep it running
Thunks run autonomously on the Thunk.AI runtime
Changes
A new ticket in the engineering backlog
Ask the agent; it updates the thunk and re-runs the tests