What is Agentic AI?
Agentic AI refers to AI systems composed of autonomous agents that can plan, use tools, call external APIs, and work in orchestrated parallel toward a complex goal — going beyond single-prompt question answering to execute multi-step workflows where each agent specialises in a discrete task and the outputs are synthesised into a structured result requiring minimal human intervention.
The term "agentic AI" distinguishes modern multi-agent AI architectures from earlier, simpler applications of large language models. A chat assistant responds to a prompt and produces a text output. An agentic AI system receives an objective, decomposes it into subtasks, dispatches those subtasks to specialised agents or tools, processes the results, and synthesises a final output — often without requiring human intervention at each intermediate step.
The key characteristics of agentic AI systems are: (1) autonomy — agents act on the environment rather than just responding to queries; (2) tool use — agents can call external APIs, databases, and services; (3) parallelism — multiple agents work simultaneously on different aspects of the same objective; (4) orchestration — a supervisor or routing layer coordinates agent tasks and manages dependencies; (5) memory and context — agents can reference prior outputs within the same workflow; and (6) human-in-the-loop gates — critical decisions require human review before action.
In the insurance domain, agentic AI is particularly well-suited to underwriting workflows because underwriting is inherently multi-step and multi-source. A single submission requires: document parsing, geocoding, third-party data lookups (flood, wind, credit), compliance checks, pricing analysis, treaty monitoring, and memo generation. These tasks are independent enough to run in parallel but must be synthesised into a coherent decision output — precisely the pattern that agentic architectures are designed to handle.
The performance advantage of agentic AI over single-model AI in insurance comes from both parallelism (multiple tasks running simultaneously rather than sequentially) and specialisation (each agent is optimised for its specific task — the flood agent uses different context and tooling than the pricing agent). A well-designed agentic pipeline can compress a 45-minute manual workflow into under 30 seconds without sacrificing — and often improving — the quality of the analysis.
Challenges of agentic AI include managing reliability across complex pipelines (individual agent failures can cascade), ensuring auditability of automated decisions, and calibrating the human-in-the-loop gates so that automation saves time without removing necessary human judgment.
Orb is Vortic's agentic AI underwriting platform — nine specialist agents running in parallel on every submission. The document parser handles structured extraction; the flood, wind, and hazard agents handle geographic risk data; the compliance and financial agents handle sanctions and credit; the treaty agent handles aggregation monitoring; and the memo writer synthesises all outputs into a structured decision document. Each agent is independently optimised and can be updated without disrupting the others — giving Vortic the ability to add new agents (SOV mapping, policy comparison, claims FNOL) on a quarterly release cycle.
Frequently asked questions
How is agentic AI different from a simple AI chatbot?
A chatbot processes a single prompt and returns a response within one inference call. Agentic AI orchestrates multiple specialised agents, each of which may call external tools, APIs, or databases, and whose outputs are synthesised by a coordinator. The result is a system that can complete complex, multi-step workflows — like underwriting a property submission — rather than just answering isolated questions.
What is a human-in-the-loop gate in agentic AI?
A human-in-the-loop gate is a checkpoint where the agentic system pauses and requires human review or approval before proceeding. In underwriting, this typically occurs at the bind decision: the AI completes all analysis and produces a recommendation, but the underwriter must explicitly approve before coverage is bound. This preserves human accountability for coverage decisions while automating the labour-intensive analysis.
Is agentic AI reliable enough for insurance underwriting?
Modern agentic AI systems designed for insurance include multiple reliability layers: source citations in every output, confidence indicators for data extractions, fallback logic when external APIs are unavailable, and structured output formats that make hallucination easier to detect. Combined with human-in-the-loop review at the bind decision, agentic AI in underwriting provides both speed and the audit trail required for regulatory compliance.