How Autonomous Decisioning is Transforming the Insurance Industry

The Sapiens Insurance 360 podcast recently featured Denzil Wasson, CTO of Sapiens Decision, discussing the rise of Agentic AI and its impact on insurance decisioning. From underwriting to claims and fraud prevention, AI is reshaping workflows across the industry, and Agentic AI promises a new level of autonomy and efficiency.

From Decision Management to AI-Driven Decision Intelligence

Wasson traced the evolution from traditional business rule engines to modern decision management platforms. Initially, business logic was embedded in code, making rapid changes difficult and leaving business analysts with limited control. Decision management platforms shifted logic ownership to the business side, allowing analysts to manage and publish rules for consumption by operational systems.

The introduction of machine learning and generative AI enabled probabilistic decisioning, combining traditional declarative rules with AI-driven predictions. This convergence created decision intelligence platforms—systems that integrate business logic, machine learning models, and automation to guide actionable outcomes.

Understanding Agentic AI

Agentic AI represents the next step. Unlike generative AI, which primarily provides recommendations, Agentic AI can act autonomously within defined goals, interacting with other agents and systems to execute decisions in real-time. In insurance, this means AI agents can:

→ Adjust premiums automatically based on risk signals

→ Identify emerging risks and apply preventive measures

→ Execute underwriting decisions for standard policies without human intervention

These agents allow humans to focus on oversight and governance while automating repetitive, rules-based tasks.

How Agents Support Business Analysts

Wasson described a typical workflow: a business analyst defines goals, constraints, and KPIs for a task, and the agent proposes solutions, generates test cases, and executes logic where authorized. The analyst can choose their level of involvement, from reviewing every step to only validating final outcomes.

This approach accelerates the lifecycle of business logic, reduces operational friction, and allows rapid responses to market changes. For example, if mortgage interest rates drop, agents can adjust loan-to-value ratios across portfolios and propagate changes dynamically to other processes.

Safeguards and Trust

Trust remains a key concern. Agents operate under constraints defined by business logic, ensuring compliance and risk management. Humans remain in the loop to supervise sensitive decisions, apply guardrails, and approve actions when needed.

Future of Agentic AI in Insurance

Looking ahead, agents will become more powerful, reliable, and autonomous. This shift will allow organizations to offload routine tasks, increase creative problem-solving, and personalize customer offerings at scale. Agents could autonomously optimize complex systems, providing hyper-personalized experiences while maintaining compliance and oversight.

Key Takeaways

→ Agentic AI extends decision intelligence by enabling autonomous execution, not just recommendations.

→ Business analysts define goals and constraints, while agents perform the operational tasks.

→ AI agents improve speed, efficiency, and responsiveness across insurance processes.

→ Trust and governance remain critical, with humans supervising strategic and high-risk decisions.

→ The future promises hyper-personalization and more creative, value-driven work as routine tasks are automated.