Agentic AI Workflows Explained: How Notch Automates Insurance End to End

Many carriers already run RPA bots, chatbots, and rules engines. Those tools break down when inputs are unstructured, cases are unpredictable, or workflows span multiple systems. That is where the RPA bot fails, and a human has to step in.

Agentic workflows are the architecture that handles those cases. Rather than executing a fixed sequence, an agentic workflow uses AI agents that perceive context, plan a course of action, execute across tools and systems, and adapt when the situation changes. For insurance operations specifically, where complexity is the norm and the cost of errors is high, that capability represents a meaningful shift in what automation can cover.

What are agentic workflows?

An agentic workflow is a sequence of tasks executed by one or more AI agents that perceive inputs, plan what to do next, use tools to act, and revise their approach based on the findings. Unlike a traditional automation pipeline with a fixed sequence of steps, an agentic workflow evaluates the situation at each stage and decides the appropriate action.

The key distinction is autonomy. A rules-based system does what it was trained to do. An agentic system does what the situation requires, within the boundaries you set. In an FNOL intake workflow, an agentic system recognises that the coverage type requires an additional document, requests it, checks it on receipt, and routes the complete file to the right adjuster without a predefined step for each action having been coded in advance.

Why insurers are adopting agentic systems

Managing insurance operations requires running high document volumes through complex business rules and fragmented systems, all while meeting strict regulatory timelines. In the past, that meant automation handled easy cases, with humans dealing with everything else. As volume picks up, there just aren't enough easy cases to keep this model from breaking.

Carriers are deploying agentic workflows now because AI can actually execute multi-step tasks across real systems instead of just generating text. LLMs can read a policy document and extract the relevant coverage triggers. Tool use lets the same agent query the claims management system, check the policy record, and update the adjuster notes without human input. Guardrails make those actions safe enough to run in a regulated environment.

Agentic workflows vs. traditional automation

Rules-based automation requires every possible input to be anticipated in advance. Unrecognized form fields, ambiguous coverage questions, and overlooked jurisdictional rules all cause the system to fail. When these unexpected scenarios occur, the workflow breaks and routes the task to a human. In insurance, these edge cases happen often, while bringing the biggest regulatory and financial risks.

RPA executes steps. An agentic workflow plans them. An agentic system handles ambiguity by reading the surrounding context and executing the most probable next step. It protects the process by automatically routing the case to a human reviewer whenever its confidence falls below a specific threshold. It can handle a coverage question that requires cross-referencing the policy document against the reported loss details and the applicable state regulation, returning a structured answer with source citations rather than routing to a senior adjuster as a default.

The scope difference is also architectural: RPA bots connect to one system at a time, while an agentic workflow reads from one system, writes to another, and triggers a process in a third within a single execution.

How are agentic workflows different from non-agentic workflows?

A non-agentic workflow executes a fixed sequence of steps defined while building it. Each step has a defined input, a defined output, and a defined next step. The workflow handles every case the same way, regardless of context, and breaks when an input falls outside that architecture. A human must handle the exception.

An agentic workflow evaluates the situation at each step and determines the appropriate action from the available options. It can read an ambiguous input, identify the most likely interpretation, take the appropriate action, and flag uncertainty for review. It can adjust its plan mid-execution if new information changes what the next step should be. The result is automation that covers complex, conditional, multi-system workflows without requiring every possible path to be coded in advance.

Core components of an agentic workflow

What lies beneath the efficient agentic workflows? What capabilities and core components should an AI contain to provide end-to-end insurance automation? The core architecture starts with AI agents, LLMS, integrations, and agent memory, ensuring they all follow guardrails and policies. 

AI agents

An AI agent is the execution unit in an agentic workflow. It takes an input, uses whatever tools and context it has access to, and produces an output or takes an action. In a multi-agent system, different agents handle different parts of the workflow: one agent might handle document ingestion and classification, another handles coverage verification, and a third composes the customer-facing communication.

Large language models (LLMs)

LLMs provide the reasoning capability that makes agents flexible. Rather than matching inputs to predefined rules, an LLM-powered agent reads the input, understands it in context, and determines the appropriate action. For insurance-specific tasks, this includes reading unstructured claim narratives, interpreting policy language, and recognising legal demand patterns in correspondence.

Tools and integrations

Tools are the mechanisms through which agents take action in external systems. A tool might be an API call to a claims management system, a database query against a policy record, a document extraction function, or a communication channel for sending a policyholder acknowledgment. The range of tools an agent can access determines what it can accomplish.

Agent memory

Memory gives agents the ability to operate coherently across a multi-step workflow. Short-term memory holds the context of the current interaction: what has been extracted so far, what decisions have been made, and what the current state of the claim file is. Longer-term memory lets agents draw on prior interactions with the same policyholder or patterns from similar past cases.

Guardrails and policy controls

Guardrails are what make agentic systems deployable in regulated environments. They define what an agent can and cannot do: which systems it can write to, which decisions require human approval, which outputs must be logged for audit, and which actions are prohibited regardless of what the LLM reasons are appropriate. In insurance, guardrails encode compliance requirements: a claims agent cannot issue a determination exceeding the adjuster's authority level.

How agentic workflows work in practice

Knowing the core of the agentic workflows helps us understand how it all works in practice. In general, when implementing AI workflows in insurance automation, it follows four steps: planning, acting, and reflecting, and adapting.

Plan

When an input arrives, the agent reads it and produces a task plan. That plan determines the sequence of steps required to handle the request, the tools needed at each step, and the conditions under which the plan should branch or escalate.

Act

With a plan in place, the agent executes each step using its available tools. It calls the policy admin system to verify coverage, writes the structured claim data to the claims management system, and triggers the acknowledgment workflow. Each action produces an output that feeds the next step. Confidence scores and validation checks run alongside execution, catching data quality issues before they propagate downstream.

Reflect and adapt

After each action, the agent evaluates whether the outcome matched expectations and whether the remaining plan still makes sense. If a coverage verification returns ambiguous results, the agent routes to a specialist agent configured for that coverage type rather than continuing with a low-confidence determination. If new information arrives mid-workflow, the agent incorporates it and adjusts the plan accordingly. This reflection step is what distinguishes an agentic workflow from a sequential automation pipeline.

Agentic workflow use cases in insurance

How do agentic workflows work in practice? What steps do they follow? The basic workflow follows FNOL intake, processing, triage, and resolution. Still, these steps come with more activities in between.

First Notice of Loss intake and claim setup

FNOL intake runs end-to-end without manual intervention on standard cases. The agent authenticates the policyholder, verifies coverage against the policy record, captures structured incident details, flags any special handling requirements, routes to the right adjuster based on claim type and jurisdiction, and sends the acknowledgment.

Policy servicing and endorsement processing

The policy servicing and endorsement processing step helps identify challenges and differences. Mid-term policy changes, endorsement requests, and coverage adjustments require authentication, eligibility verification, rule checking against state filing requirements, system updates, and policyholder confirmation. An agentic workflow handles each of those steps in sequence, routing to human review only when the change triggers an exception outside the agent's authority level. For standard changes, the policyholder receives a confirmed endorsement without the request ever sitting in a queue.

Underwriting triage and document ingestion

Incoming submissions contain a mix of structured forms, narrative descriptions, prior loss runs, and supporting documentation. An agentic workflow reads each component, extracts the relevant risk data, checks for referral triggers like material changes and coverage anomalies, and routes to the right underwriter with a populated triage summary. Underwriters see a completed risk profile and the flagged issues rather than a document stack requiring manual extraction.

Billing inquiry resolution and reinstatement

Billing contacts cluster around payment failures, lapse events, and reinstatement requests. Each scenario requires a different handling path, and some carry compliance obligations that cannot be skipped. An agentic workflow identifies the billing scenario, applies the appropriate handling logic, and completes the resolution.

How Notch deploys agentic workflows end to end

Notch deploys specialised agents operating under a governing orchestration layer, with deterministic validation at each step and a unified audit trail from first contact through resolution. Each agent handles a defined scope: document ingestion, coverage verification, adjuster routing, and policyholder communication. The orchestrator manages handoffs, enforces the policy controls, and routes exceptions to human review when agent confidence falls below the defined threshold.

The integration layer connects to the carrier's existing core systems: claims management, policy admin, and CRM. Deployment does not require the carrier to replace infrastructure; it requires connecting the agent layer to the systems already in place. For standard workflows, deployment runs to production within 90 days. Notch's commercial model reflects that commitment: no payment until the platform reaches 30% autonomous resolution, and outcome-based pricing that ties cost to actual end-to-end resolution rather than interaction volume.

Conclusion

Insurance operations have enough volume, complexity, and regulatory risk that the manual review layer required by current automation is expensive to maintain. Agentic workflows reduce that layer by handling the cases that rule-based automation cannot: the multi-system workflows, the conditionally complex routing decisions, and the documents that do not fit a predefined template. Carriers deploying this architecture are not doing it to eliminate their operations teams. They are doing it to concentrate those teams on the cases that require their judgment, while the agentic layer handles the volume that does not.