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To further AI understanding and adoption, Lazarus AI is producing a series of articles titled Artificial Intelligence – Insights for Insurance (“AI-II”). This piece introduces agentic AI in practical terms and provides a framework for understanding how it relates to existing AI investments. Additional insights will explore agentic AI in greater depth throughout the year.

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Another AI Concept?

Many leaders are fatigued by the volume of AI terminology. New concepts are often met with skepticism, especially when they appear driven more by marketing than substance. Though agentic AI is not entirely novel, recent announcements from major technology firms have brought it to the forefront. Thus, whether driven by marketing or not, it is now a concept leaders need to understand.

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What Is Agentic AI?

At its core, agentic AI is the orchestration of tools, technologies, and people to execute a broader business process. It is not a single model or capability, but rather a system that coordinates multiple components to achieve an outcome.

In practice, this often includes a network of specialized agents. These agents are designed to perform specific tasks (e.g. document summarization, fraud detection, web search, translation, or data extraction). Each operates within a defined scope, but together they contribute to a larger workflow.

As these agents proliferate, the challenge shifts from capability to coordination. The effectiveness of agentic AI depends less on any single component and more on how those components are orchestrated.

It is important to note, however, that not every system labeled as agentic AI will reflect meaningful orchestration. Just as “AI” has been broadly applied across products, the term “agentic” will likely become overused as well.

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From Applications to Agents

One of the more significant implications of this shift is how software is structured.

Traditional applications (especially those without embedded AI) will gradually lose relevance. Over time, discrete tools will be replaced by systems of agents that handle tasks dynamically rather than through fixed interfaces.

This does not mean software disappears, but it does change how functionality is delivered. Capabilities become modular, distributed, and increasingly autonomous within defined boundaries.

The role of developers will evolve alongside this shift, though not necessarily diminish at the same pace. Designing and managing these systems introduces new complexity rather than eliminating it.

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A Practical Example

Consider a standard insurance use case.

An auto claim includes structured claim data, a handwritten police report, photos, video footage, and audio evidence. It is supported by policy documents, claims guidelines, and internal systems.

An agentic AI system is given a directive: adjudicate the claim, follow all guidelines, identify potential fraud, and return a recommendation. The system is also required to document its reasoning and present its conclusions in a format suitable for review.

Executing this task requires multiple coordinated actions. Documents must be interpreted. Handwriting must be processed. Visual and audio inputs must be analyzed. Policy rules must be applied. Outputs must be structured, explained, and validated.

No single model handles this end-to-end. The outcome is produced through orchestration across multiple agents and systems.

This is the defining characteristic of agentic AI.

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Don’t Lose Sight of What Already Works

Agentic AI does not replace existing AI capabilities, rather it builds on them.

Established approaches (e.g. data extraction, document interpretation, handwriting recognition, summarization) remain critical. These capabilities form the components that agentic systems orchestrate.

As a result, prior AI investments are not made obsolete. In many cases, they become foundational with agentic AI appropriately framed as an extension. Agentic AI will, however, accelerate the decline of legacy systems that cannot integrate into this model. Static, non-adaptive technologies in particular will struggle to remain relevant in workflows driven by coordinated intelligence.

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Summary

Agentic AI is not a distant concept, but already emerging in many practical applications.

Leaders should understand it as a system-level advancement: one that coordinates multiple capabilities to execute meaningful business processes. Ignoring it creates the risk of missing where the next layer of value will be created.

It is important to note that agentic AI does not replace the need for existing AI investments. Mature capabilities will continue to deliver value and remain essential components of these systems.

The shift is not from old to new, but from once isolated capabilities to orchestrated intelligence.

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About Lazarus AI

Lazarus AI develops enterprise-grade AI systems for the insurance industry, public sector, and beyond. Our Applied Intelligence Engine (AIE) enables organizations to eliminate their processing bottlenecks and provides rapid time to value, allowing our customers to compete more effectively with reduced cost, lower risk, and greater speed.