AI Agentic Workflow for Interactive, Real-Time Quote Negotiation Support

Manual back-and-forth with brokers over email and phone creates days of delay. A custom workflow provides an AI co-pilot that simulates alternative terms (deductibles, limits, endorsements) and calculates the impact on technical premium and loss ratio in real-time during a call or chat session. This allows the underwriter to respond with counter-proposals immediately, compressing multi-day negotiations into a single, conclusive interaction.

Slow responses and generic declines cause brokers to take submissions elsewhere. By enabling dynamic, informed counter-offers, the workflow keeps more viable risks in play. The AI agent retrieves comparable bound policies and market pricing data to ground proposals in reality, increasing broker confidence and the likelihood of closing on favorable terms. This directly increases premium throughput per underwriter.

Ad-hoc concessions during negotiation can inadvertently degrade portfolio quality. The workflow integrates real-time analytics that evaluate any proposed term change against concentration limits, catastrophe exposure, and line-of-business profitability targets. The underwriter receives instant guardrails, preventing decisions that compromise portfolio economics for the sake of a single deal.

Underwriters waste time alt-tabbing between PAS, Excel, document systems, and external data sources to model 'what-if' scenarios. The custom architecture orchestrates agents to fetch exposure details, loss runs, and guideline documents on-demand via integrated APIs. It performs live premium and loss ratio calculations, presenting the underwriter with analyzed options instead of raw data.

Verbal agreements and fragmented email chains create compliance and recall risk. The workflow automatically logs every simulated scenario, data point retrieved, and final term agreement into an immutable audit trail linked to the policy in the DMS. This creates a complete narrative of the negotiation rationale, essential for internal reviews, reinsurance reporting, and regulatory inquiries.

Implementation involves a central orchestration engine (e.g., LangGraph, Temporal) that coordinates specialized agents: a Retrieval Agent queries the PAS, DMS, and external data APIs; a Simulation Agent runs pricing and exposure models; a Guidelines Agent checks proposals against rules. This layer sits over legacy systems like Guidewire or Duck Creek, integrating via secure APIs or RPA wrappers, and exposes a conversational interface directly in the underwriter's workstation.

A primary conversational agent embedded in the broker portal or underwriting workstation that interprets broker counteroffers in natural language, parses requested changes to limits, deductibles, or conditions, and triggers the simulation pipeline. It maintains dialogue state, requests clarifications, and formats the underwriter's final response for communication, reducing friction and keeping negotiations within the digital channel.

A dedicated computational agent that receives the proposed term changes and instantly re-executes the rating engine and actuarial models. It calculates the direct impact on premium, projected loss ratio, and technical price, presenting the financial delta to the underwriter. This component integrates with core pricing systems (e.g., Guidewire Rating) and internal models to provide grounded, auditable calculations.

An agent that queries a vector database of recently bound policies using the current risk profile and proposed terms as a search query. It retrieves the 3-5 most relevant precedents, summarizing key similarities and differences. This system is built on a RAG (Retrieval-Augmented Generation) architecture over the policy administration system (PAS) data, giving the underwriter concrete, internal benchmarks to support their position.

A rules-based agent that cross-references the simulated terms against the underwriter's official guidelines and binding authority in real time. It flags any breaches of territory, class, or limit authority that would require a referral, and can check for compliance with specific state regulations or internal product rules. This guardrail ensures all discussed options remain within operational guardrails.

A risk-modeling agent that assesses how the proposed policy would affect the insurer's aggregate exposure in a specific geographic zone or peril class. It queries real-time portfolio data to check for catastrophe accumulation or line-of-business concentration limits, providing a second-order risk assessment that might not be apparent from the single-risk view.

The central workflow engine (e.g., built with LangGraph or Temporal) that sequences the agents, manages data flow between the PAS, document systems, and external APIs, and enforces approval gates. Crucially, it immutably logs every agent interaction, data point retrieved, simulation run, and user decision, creating a complete audit trail for compliance, training, and model risk management (MRM).