FDE · Enterprise RSI · Agent training
Forward Deployed Engineering brings AI into your real workflows. Enterprise RSI turns feedback into controlled improvements. Our training gym adds expert data and verifiable tasks to that loop.
Start with one workflow. Measure every improvement.
01 / Forward Deployed Engineering
Put agents to work.
Real workflows + tools + a measured baseline
02 / Enterprise RSI
Improve within defined bounds.
Approval rules · Audit trail · Rollback
03 / Agent training
Expert data. Verifiable tasks. New practice.
One connected approach
FDE establishes the system in the field. RSI provides the improvement discipline. Training programs develop the capabilities the work demands.
Embed engineers with your team to build agents into real workflows. Connect your data and tools, establish evaluations, and hand over a system your team can operate.
Integrations · Production agents · Evaluation infrastructure
Build bounded recursive self-improvement: use traces to propose changes, test them against a held-out baseline, and promote only within your approval rules and change limits.
Proposals · Evaluation gates · Audit trails · Rollback
Create expert data, agent trajectories, evaluations, and RL tasks for the capabilities that need more practice. Bring verifiable learning signals into your model and agent development.
Expert data · Tasks · Verifiers · Rewards
Each engagement defines its data boundaries and permitted uses. Deployment data enters a training program only with explicit authorization.
The training gym
When evaluation reveals a capability gap, build a focused training program around it. Expert knowledge, realistic tasks, and clear rewards connect the training gym to FDE and RSI.
Turn specialist knowledge into challenging examples, reference solutions, and critiques that target a specific capability gap.
Domain context · Expert review · Source provenance
Capture the work behind the result: tool calls, state changes, failed attempts, corrections, and the final artifact.
Execution traces · Recovery paths · Reviewable outcomes
Define what good looks like before training. Held-out tasks and explicit rubrics reveal what improved, what regressed, and where to focus next.
Baselines · Graders · Failure analysis
Build tasks with clear starting states, completion criteria, and reward signals tied to verifiable outcomes. Use executable checks where possible and expert judgment where needed.
Task specifications · Verifiers · Reward criteria
Each pilot is scoped with your team: domain, sources and permitted uses, review standards, delivery format, and evaluation protocol.
Inside a learning episode
The task, the actions, the outcome, and the criteria for success belong together. Explore what a training episode can contain.
Episode anatomy
Illustrative examplesThe task
A versioned repository, a reproducible failure, and a bounded tool environment. The agent must repair a parser without breaking supported inputs.
Task specification · Tool trace · Patch · Test results · Review rubric
The observable trajectory
What the verifier checks
Reward design
Derive reward from test outcomes and constraint checks; use expert review for patch quality. Audit the verifier so changing a test cannot count as fixing the bug.
Conceptual task designs, not customer work or benchmark results. Domain coverage and verification are scoped for each pilot.
FDE + RSI + training
Measure both the agent in its workflow and the model behind it. Keep the baseline, evaluation gates, and approval rules explicit.
These are measurement criteria, not reported results. Improvement is established through evaluation, never assumed.
FDE brings the agent into a defined workflow, connects the tools, and agrees on the task outcomes and evaluation criteria.
RSI uses authorized traces and expert feedback to identify failures and propose versioned changes to prompts, retrieval, or tools within an agreed scope.
Test each candidate against held-out tasks and regression checks. Apply human approval where required, record the decision, and retain a rollback path.
Where a capability needs training, create expert examples, trajectories, and RL tasks. Use them in your training stack, then evaluate the resulting model before release.
The long view
Build from FDE deployments and bounded RSI. Extend that foundation with expert training programs, interactive environments, and infrastructure for continuous learning.
Year 01
2026–27Near-term focus
FDE deployments and bounded RSI programs, supported by expert data, trajectories, evaluations, and RL tasks.
FDE + RSI + training
Year 03
2029Our ambition
Extend FDE and RSI into repeatable infrastructure for diagnosis, expert verification, training feedback, and measured model improvement.
Measured improvement
Year 05
2031Our ambition
Give agents places to act, use tools, encounter consequences, and practice increasingly difficult work.
Environments
Year 10
2036Our ambition
Connect experience, outcome feedback, and evolving curricula into a continuous learning layer for intelligent systems.
Experience
A direction for the company, not a schedule of product releases. Later horizons describe our ambition; capabilities and delivery are agreed for each engagement.
One workflow. A measurable next step.
Bring a workflow to deploy, a system to improve, or a capability to train. Let’s define the work, the boundaries, and the evidence of progress.