Production agents and workflows
Agents that run a defined business process, with human review and monitoring in place.
Forward-deployed AI engineering
Turn AI pilots into production workflows.
We embed with PE portfolios and operating teams to pick one high-value workflow and build the agent or internal tool that runs it. We ship it with evaluation and governance built in, and we train the operators so the system survives real work.
- Day 30
Start with
One named operating workflow
- Day 60
Measure with
A scorecard the system has to pass
- Day 90
Finish with
A capability your team owns
What we do
Build the workflow and the capability to run it.
Internal AI tools and automation
Focused tools that speed up repeatable work inside the systems teams already use.
Agentic-coding rollout
Claude Code and Codex adoption for engineering teams, with governance fitted to the codebase. Claude Code training for teams covers the training side.
Evaluation and governance
The scorecard, tests, and controls a production system runs under.
Portfolio deployment playbooks
Repeatable deployments sequenced across a PE portfolio, so lessons carry between companies.
For private equity
AI for private-equity operating teams.
We help funds and portfolio companies turn one operating workflow into a production AI system, and we support diligence teams evaluating AI claims.
What we learn in the field
Field lessons from production deployments.
The podcast and field notes document what it takes to get these systems into production, told by the builders and operators doing the work.
Latest episodeSpencer Whitman - Gray Swan AI's $200M Plan to Secure AI Systems
Episode
Why enterprise agents are a data and workflow problem
A practical conversation about data access and tribal knowledge, and where forward-deployed engineering fits into customer engagement.
Fireside panel
What production agentic engineering requires
Builders from Cognition, Semgrep, Factory, and Composio talk through evaluation and autonomy in real engineering workflows.
Episode
How an enterprise operates with AI agents
An operator-level conversation about owning and evaluating deployed agents, and about the organizational patterns behind them.
Guests on the Forward Deployed podcast include builders and operators from LangChain, Vercel, Meta, ThoughtSpot, Abnormal AI, Ramp, Cognition, Semgrep, Factory, Composio, Glean, Decagon, Sierra, Vapi, Fin (formerly Intercom), Cerebras, Mercor, Turing, Rovio, and AI Engineer.
All episodes
Listen on Apple Podcasts · Spotify
Field notes
Common questions
What is forward-deployed AI engineering?
An engineer embeds with your team, takes one named workflow, and builds the agent or internal tool that runs it. The build happens inside your systems and data, and the engagement ends when your team operates the result on its own.
Who is this for?
We work with PE portfolios and operating teams. The common thread is a workflow that matters, an accountable owner, and systems and data we can reach.
What does an engagement look like?
A 30/60/90 deployment. The first month selects and scores the workflow, the second builds and ships it, and the third hardens the system and transfers ownership to your operators.
How do the podcast and the consulting work relate?
The episodes and field notes record how other teams get AI systems into production. That access informs how we build, and publishing what we learn keeps the practice honest.
Forward Deployed field notes
Practical AI engineering, straight from the field.
One practical email on production AI, with new field notes and episodes as they ship.
Next step
Pick the workflow that should move first.
Tell us about the workflow and the systems it touches, plus the outcome it needs to improve. The first conversation scopes whether a 30/60/90 deployment fits.
Discuss a deployment