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Rohan
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Why actuaries should care about agentic AI

Agentic AI moves models from answering questions to completing tasks. Here is what that shift means for pricing and reserving work.

A traditional model answers a question you pose. An agent decides which questions to ask, calls tools, and works toward a goal you set. For actuarial work, that is a meaningful change in how software fits into the day.

A small example

Consider a pricing check you run every month. An agent can pull the exposure data, run the model, compare against last month, and flag what moved, all before you open the workbook.

price_check.py
def price_check(portfolio):
    exposure = load_exposure(portfolio)
    result = run_pricing_model(exposure)
    delta = compare_to_prior(result)
    return flag_material_moves(delta, threshold=0.05)

The actuary stays in control of judgement and sign-off. The agent removes the mechanical steps in between.