Forward Deployed Agent
An expert builder on every team
Most enterprise AI programs wait on a handful of engineers to build each workflow. The Forward Deployed Agent does that work alongside your process owners. It reads the SOP, builds the thunk, writes the test cases, runs them, and tells you what it found.
Example session
You
Here is our adverse-event intake SOP. Build a benchmark from it: generate the test data, compare three frontier models, and write up the results.
Forward Deployed Agent
✓ Built a seven-step case intake thunk from the SOP
✓ Generated 75 test reports with expected outcomes
✓ Ran gpt-5.1, gemini-3.5-flash and muse-spark-1.3, on their own and on Thunk.AI
✓ Report: 97.9% to 99.8% on Thunk.AI, against 88.1% to 95.4% on their own
Illustrative session. The results are from our October 2026 drug safety benchmark.
What people ask it
Weeks of specialist work, asked for in a sentence
Build
“Here is a SOP document. Create the thunks needed to automate it, then test and validate them.”
You get a working workflow and the tests that prove it, in one session.
Benchmark
“Build a benchmark set of thunks from this SOP. Generate the test data, compare three frontier LLMs, and write a full report.”
Test data, a side-by-side model comparison and a written report you can share.
Operate
“Every morning, troubleshoot the last five errors on thunk X and tell me what you fixed.”
A daily note on what broke, why, and what changed.
Evolve
“Here is user feedback on thunk X. Produce a new version that addresses it, and explain what you did for each issue.”
A new version, with every change traced back to the feedback that prompted it.
Optimize
“Cut the token cost of thunk X significantly. Show me the result on a copy.”
A cheaper copy to compare side by side before you switch anything over.
Watch it work
Two real sessions, start to finish
Build a thunk with the agent
7 min 30 s. From a plain-language description of the process to a tested thunk.
Analyze and improve a thunk
4 min 22 s. The agent reviews an existing thunk and makes it better.
What changes
The people who know the work build the automation
No translation layer
Today a process owner writes requirements, an engineer interprets them, and the result comes back weeks later, a little wrong. With the agent, the process owner describes the work and gets a thunk they can read, test and change themselves.
Iteration at the speed of a conversation
Most of the effort in automation is the loop after the first draft: test, find the gap, fix, test again. The agent runs that loop for you, so a change that used to wait for the next sprint happens the same afternoon.
You decide what ships
The agent works on your private Thunk.AI instance through MCP, from the AI assistant your team already uses. It explains every change it makes and can try one on a copy before you commit. Everything it builds runs on the same reliable runtime, with the same access controls and audit trail as any other thunk.