With the rise of GenAI and LLMs, industries are reimagining their IT platforms and realigning their business models to achieve key transformational goals in their operating models.
Typically, such realignment leads towards key shifts in the system architecture, signaling the advent of a unified conversational user interface for various user personas; and, AI agents supporting automated backend processes orchestrated in a business-aware manner. The key goals for insurance CXOs are:
Insurance is ripe for agentic, adaptive systems for many reasons, ranging from complex policy language to fragmented tools.
Let’s have a closer look at these:
Insurers' vision now requires reimagining the insurance platform architecture.
Figure 1, shows the shift from traditional reference architecture to next-generation reference architecture, embedding the contextual benefits of GenAI, LLM, agentic systems, and self-adaptive user interface. Notable changes in this next-generation reference architecture are described below:
Experience layer: Self-adaptive conversational UI demands the following key outcomes from the agentic system:
Business components as domain agents with agentic orchestration: Automated orchestration AI agents of various types will form the new view of the business component layer.
The business component layer will also enable key architectural patterns to adopt cognitive agents to leverage the LLM and communicate across systems. Key architectural aspects that would be undertaken to leverage GenAI features mapped to specific use cases are listed below:
Data platforms in new age architecture will have various AI-specific data planes in conjunction with modernized data planes for the real-time generation and processing of data insights and the embedding of such insights into user journeys for contextual predictive guidance for the user.
The focus in an agentic, self-adaptive system will shift from a traditional enterprise data platform with staged data processing and a disjointed data event streaming for specific use cases. The need for self-adaptive behavior in the user interface now necessitates real-time event and data streaming, real-time curation of enterprise data assets with data quality assured through the Medallion architecture model, which ensures high data quality through multi-step refinement,and finally, real-time generation of machine learning insights on the data features engineered from the ingested/ streamed data into the enterprise data platform.
The following data planes will now combine the transactional databases alongside the vector databases and data structures used for AI agentic processing:
With the above considerations, a mapping of the shift of architectural principles in traditional architecture to next-gen architecture is presented in Table 1:
Principle dimension |
Traditional architecture |
Next-gen architecture (Intent/plan/act) |
User interaction |
Static forms, fixed journeys |
Conversational, goal-oriented, adaptive journeys |
Processing logic |
Deterministic process services |
Domain agents with tools and humans in the loop approvals |
Process knowledge |
Scattered PDFs/wiki |
Retrievable, permissioned, and cited cognition chunked into vector DBs |
User interface |
Contextual persona apps |
Persona-aware, confidence-aware, accuracy-aware, and self-adaptive conversational chat through voice or text |
Governance |
Afterthought audits |
First-class policy, process and data lineage and automation-driven controls |
Outcomes |
Low STP, high handling time |
Higher STP, higher agentic automation, faster cycles, fewer handoffs |
Table 1: Comparison of architectural principles from traditional to next-gen agentic architecture
Some of the core solution patterns which would be applied to the insurance domain are listed below:
Retrieval augmented generation (RAG) over insurance knowledge.
Various types of information retrieval and generation steps will be undertaken to build the insurance intelligence within the system:
Self-adaptive UI mechanics.
Data architecture: The backbone for this agentic solution will rely heavily on various types of data stores based on the business context, purpose, and controls, and due considerations will need to be implemented for each of these specific types of data stores, as shown in Table 2.
Store |
Insurance-specific purposes |
Notes |
Vector DB |
Semantic search over wordings, claims, transcripts |
Enforce row level permissions; attach clause IDs |
Knowledge graph |
Entities/relationships and lineage |
Supports provenance and regulatory audits |
Document store |
Source of truth for artifacts |
Enables, versioning and jurisdiction tags |
Feature store |
Pricing, fraud, propensity features |
Allows privacy controls and drift monitoring |
Event stream |
Telemetry and feedback |
Drives evals, SLA alerts, and retraining signals |
Table 2: Various data store types in next-gen agentic architecture, their applicability in insurance business, and contextual controls.
Data design and access need to be planned end-to-end to ensure retrieval respects the line of business, role, and tenancy.
A typical architecture adoption roadmap for the carrier will follow a gradual shift to maturity and adoption path to an agentic world, as discussed below:
Key KPIs and use case-specific outcome improvements that would matter in the transformed business model are:
Insurance supports people when they need it the most. Design and decision systems for adaptable agents, self-adaptive user interfaces, and transparent decisions in an agentic world of next-gen insurance solution architecture not only streamline processes but also build a reliable, user-focused software platform.
TCS is already realizing the Agentic AI insurance reference architecture through targeted investments in its insurance products and solutions. TCS BaNCS™ for Insurance features adaptive conversational UI agents and AI-driven business process agents that replace human-intensive tasks like quotations, underwriting, claims intake, and claims assessment.
With TCS BaNCS™ for Insurance, a fully agentic platform complete with self-adaptive user interfaces, orchestrated AI agents, and MCP-based integrations is within reach, empowering global insurers in their transformation journey.