17 Practical Agentic AI Design Patterns Published

The 17 agentic AI patterns, codified by AI developer Fareed Khan, provide a concrete implementation guide for moving beyond single-shot AI responses to multi-step, goal-oriented systems. These architectures cover foundational patterns like Reflection (self-critique), Tool Use (extending capabilities via APIs), and ReAct (interleaving reasoning and action), which form the building blocks of more complex agentic workflows. The repository includes hands-on implementations in LangChain and LangGraph for each pattern. In insurance, these patterns are being used to re-architect core processes. For claims automation, a multi-agent system can be designed where specialized agents handle distinct tasks like coverage verification, fraud detection, and damage assessment from photos, passing the claim through a sequential workflow. This approach mirrors a human claims processing team and has been shown to reduce claims cycle times from days to minutes. For underwriting, a "Coordinator" or "Meta-Controller" agent can decompose a complex submission, dispatching sub-tasks to specialized agents that analyze documents like ACORD forms, Statements of Value (SOVs), and loss runs in parallel. This significantly accelerates the quote-to-bind process, with some commercial P&C insurers reporting reductions of 60-99% and loss ratio improvements of 3-5 percentage points. From a backend perspective, building these systems requires a shift from traditional monolithic services to a more modular, microservices-like architecture where agents act as intelligent, goal-oriented services. This necessitates a robust API layer designed for machine consumption, focusing on clear intent and providing semantic context rather than just raw data. A key architectural decision is whether an agent's tools should run on the backend as a centralized, headless service or on the frontend. For platform engineers supporting these systems, the developer experience of the underlying AI platform is critical. This includes providing clear API documentation, robust SDKs, and seamless integration with existing CI/CD pipelines and monitoring tools. The goal is to create a low-friction, high-trust environment where developers can easily build, test, and deploy reliable agentic workflows. The insurtech venture landscape is increasingly focused on startups leveraging AI for B2B SaaS solutions that tackle core insurance functions. In 2024, 43% of insurtech VC funding went to B2B SaaS companies. While overall funding has decreased from its 2021 peak, investment in AI-centric insurtech remains strong, with a particular surge in the P&C sector. For technical founders, a primary lesson is to focus on solving a specific customer problem rather than becoming obsessed with the technology itself. Building a minimal viable product (MVP) to test the market and gain paying users is more critical than creating a feature-complete product from the outset. Successful technical founders often start by building a community and go-to-market strategy even before writing the first line of code.