Purpose-built AI agents
Agents that perform actuarial work, not just answer questions.
They plan, calculate, control, document, and deliver. Your actuaries define the method, intervene at critical points, and retain the final decision.
Planning
The engagement is decomposed before it is executed.
The agent turns a business request into an explicit sequence of data, calculations, controls, and outputs.
- Visible and adjustable plan
- Technical dependencies orchestrated
- Configurable stops and approvals
Execution plan
White-box
The agent writes the code. The actuary can see it.
Operational reasoning does not disappear behind an opaque answer: scripts, intermediate results, and controls remain inspectable and editable.
- Visible, versioned code
- Inspectable intermediate results
- Manual takeover at any point
Actuarial Notebook
model = fit(severity, method="glm")
result = model.predict(portfolio)
validate(result, controls=rules)
94.2%
Fit score
Delivery
The work ends with an output the team can use.
The agent organizes results, documents decisions, and prepares a deliverable that actuaries can challenge and approve.
- Structured Insight Report
- Assumptions and limitations documented
- Traceability back to sources and calculations
Insight Report
Human-agent collaboration
Agents execute. Actuaries exercise judgement.
Responsibilities are explicit, controllable, and adapted to the risk level of each engagement.
- 01
Actuary
Objective
The actuary defines the expected outcome and business context.
- 02
URLab agent
Plan
The agent proposes the steps, tools, data, and controls required.
- 03
URLab agent
Execution
The agent performs approved operations and documents its work.
- 04
Actuary
Decision
The actuary challenges, adjusts, and approves the conclusion.
Actuarial foundation
Agents that master actuaries’ reference libraries.
Across Python, R, and Julia, agents use the actuarial and statistical libraries suited to each study. They also draw on documented methods and sourced regulatory references.
Actuarial libraries
Mastery of leading actuarial and statistical libraries.
Documented methods
Statistical functions, machine learning, survival, time series, and copulas.
Regulatory references
Structured, documented regulatory knowledge connected to its sources.
Verifiable sources: every reference can be reviewed and checked.
Visible code: functions, assumptions, and transformations remain inspectable.
Expert validation: decisions and deliverables remain subject to human review.
Pilot engagement
Give a complete study to a URLab agent.
Choose representative actuarial work. Observe the plan, execution, controls, and deliverable, then assess control at each stage.