Workflow & system design
A map of inputs, tools, decision points, human approvals, and failure handling, tied to one agreed business workflow.
Agent Engineering Sprint / 3–8 weeks
AI Agent Development
Build an agent your team can evaluate and operate.
Take a defined workflow from system design to an evaluated implementation, with clear tool boundaries, recovery paths, and operating documentation.
Discuss this sprint ↗Scope, fee, and acceptance criteria agreed before work begins.
The decision
When this sprint is useful
- Which steps need an agent, and which should be deterministic?
- How should tools, memory, permissions, and recovery work?
- How will we measure success and investigate failures after launch?
The handover
What you receive
Agent implementation
A prototype or production implementation as agreed in scope, including the model and tool integrations needed for the workflow.
Evaluation & observability
Task-level checks and traces for inspecting behavior, tracking failures, and understanding resource use.
Operating handover
Setup instructions, architecture notes, known limitations, and a runbook for your engineering team.
Before we start
A clear scope on both sides
Useful inputs for scoping
- A specific workflow and success criteria
- Tool and data access requirements
- Deployment constraints and a technical owner
Scope boundaries
The proposal defines prototype versus production scope, supported integrations, and acceptance criteria. Ongoing support and additional workflows are scoped separately.
Fees depend on the decision, system complexity, and access requirements. Share your budget range and timeline to help establish fit.
Our research
Explore the thinking behind the work
Agentic RL →
Agentic RL applies reinforcement learning to systems that plan, use tools, interact with software, and act over long horizons. The hard problems extend beyond the base model: state, memory, environment resets, credit assignment, reliable verifiers, and safe exploration all become part of the training system.
Environments →An RL environment defines the tasks, observations, actions, transitions, and outcomes through which a policy gains experience. For frontier AI, environment engineering increasingly includes browsers, code repositories, simulators, synthetic users, and tool APIs. Environment diversity and fidelity often determine whether gains transfer beyond a benchmark.
Rewards & Verifiers →Rewards translate outcomes and preferences into the signal used to improve a policy. They may come from humans, learned reward models, AI judges, tests, or executable verifiers. This topic follows how reward signals scale, where they fail, and how evaluation can detect proxy optimization before it becomes a production problem.
Read our RL Scaling Bottleneck Map →Common questions
Before an engagement
Can you work with our existing stack?
Existing APIs, tools, hosting, and model choices are reviewed during scoping. The implementation plan identifies what can be reused and which integrations need additional work.
Will the agent be fully autonomous?
That depends on the workflow. Permissions, human approvals, escalation, and recovery are designed around the consequences of each action. Autonomy is an explicit design choice.
Does every sprint deliver a production system?
No. The proposal specifies a prototype or production implementation based on the workflow, dependencies, and acceptance criteria. Production readiness is assessed against that agreed scope.
Next step
Bring a real problem.
Start with the scope.
Describe the goal, constraints, and timeline. We’ll review the fit and discuss a useful next step.