By Seth Earley, Founder & CEO, Earley Information Science
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Published: June 13, 2025
Last Updated: October 7, 2026 | Version 1.0
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Who This Is For: C-suite executives, VP/Directors of Digital Transformation, and AI/ML leaders navigating the transition from LLM-based tools to agentic AI systems. Also valuable for technology strategists and enterprise architects evaluating agentic deployment.
Prerequisites: Basic familiarity with large language models and enterprise AI initiatives.
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This article draws on a roundtable discussion with AI and search practitioners Jeff Evernham of Chapsvision, Olivier Têtu of Coveo, Patrick Hoeffel of Perficient, and Phillip Ryan of GlassLeopard Technologies, who participated in the Earley Information Science seven-part series on Search and AI.
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Something significant is happening beneath the surface of the AI conversation, and it is not the headline capabilities that tend to dominate coverage. The more consequential shift is architectural: enterprises are moving from large language models as standalone tools toward agentic systems that orchestrate tasks, coordinate across multiple systems, and act with a degree of autonomy that earlier AI implementations did not possess.
For most organizations, this transition is arriving faster than their strategic planning anticipated. Understanding what it means in practical terms — what agentic systems can do, where they fall short, and what organizational conditions determine whether they succeed — is now a prerequisite for executive decision-making, not a topic that can be safely delegated to technology teams.
What Makes Agentic AI Different
Traditional large language models respond to prompts. They generate text, summarize documents, draft communications, and answer questions. The interaction is fundamentally reactive: a human initiates, the model responds, the exchange ends. This is genuinely useful, but it is also inherently limited. The model does not initiate, does not remember across sessions by default, and does not act on systems beyond producing text.
Agentic systems operate differently. They are designed to pursue goals across multiple steps, coordinating between data sources, APIs, software systems, and in some configurations, other agents. Jeff Evernham of Chapsvision observed in our discussion that AI is becoming less something people consciously go to and more the invisible infrastructure woven through everything an organization does. That shift in how AI is experienced — from tool to infrastructure — is what agentic architecture enables.
Patrick Hoeffel of Perficient offered a useful analogy: agentic AI is to traditional LLMs what microservices were to monolithic applications. Just as software architects decomposed large applications into smaller, independently deployable services, agentic AI decomposes complex tasks into specialized agents that can be composed and orchestrated as the work requires. The interface is natural language; the underlying architecture is modular and dynamic.
What This Means for Enterprise Operations
The business implications are substantial. Agentic AI creates the possibility of automating not just discrete tasks but connected workflows — sequences of decisions and actions that previously required human coordination at each handoff. Customer support interactions that route, escalate, and resolve across multiple systems. Software development workflows where agents suggest, test, and refine code in parallel. Knowledge management processes where agents curate, validate, and surface information without manual intervention at each step.
Phillip Ryan of GlassLeopard Technologies described how agentic tools have transformed development workflows in practice, with multiple agents collaborating in real time to accelerate work that previously required sequential human effort. The productivity gains in bounded, well-defined domains are real.
But the transition introduces complexity that organizations need to plan for honestly. Agentic systems that operate autonomously across multiple steps and systems also fail in more consequential ways than systems that merely respond to prompts. The scope of potential error is broader, the traceability of that error is harder, and the governance required to manage it is more demanding.
Four Realities Executives Should Plan For
Data quality determines everything. This is not a new insight, but agentic AI makes it more consequential than ever. Phillip Ryan put it directly: search and retrieval are not query-time problems, they are data management problems. Agentic systems can orchestrate across datasets, but they cannot compensate for the quality of the data they draw on. Every failure mode in earlier AI implementations that traced back to poor information architecture, inconsistent taxonomy, or ungoverned metadata will resurface in agentic deployments — at greater speed and scale. Organizations that have deferred foundational data work will find it impossible to avoid any longer.
Pattern matching is not reasoning. Olivier Têtu of Coveo raised a concern that practitioners encounter consistently: users develop an inflated sense of AI capability based on fluent, confident outputs. Current models, including those powering agentic systems, are fundamentally pattern-matchers. They produce plausible continuations of language based on statistical relationships in training data. When operating in well-structured domains with high-quality inputs, this produces impressive results. When operating outside those conditions, it produces hallucinations — outputs that are fluent and confident but factually incorrect. Agentic systems inherit this limitation and must be carefully scoped, grounded in authoritative knowledge sources, and monitored to prevent unintended consequences from propagating across automated workflows.
Generic solutions will not create competitive advantage. Têtu also observed that differentiation in agentic AI will not come from access to foundation models, which are broadly available, but from how organizations configure, customize, and integrate those models against their specific operational realities. Every enterprise will need agentic configurations tailored to their data, their workflows, and their customer expectations. The organizations that build that customization capability — developing the orchestration layers, the domain-specific training, and the integration architecture that reflects how their business actually works — will pull ahead of those that rely on off-the-shelf implementations.
ROI must be defined before deployment, not after. Ryan identified a pressure point that many technology leaders will recognize: the imperative to deploy AI is sometimes stronger than the imperative to derive value from it. This is a significant risk at the organizational level. Agentic systems that are deployed without clear success criteria — specific, measurable outcomes tied to defined business processes — are expensive and hard to evaluate. Traditional KPIs may not capture the performance of multi-step, probabilistic workflows. Organizations need measurement frameworks designed for agentic systems before those systems go into production, not improvised afterward when stakeholders are already questioning the investment.
A Risk Worth Naming: Agent Sprawl
One risk that received significant attention in our discussion deserves direct mention for C-suite leaders. As agentic capabilities become more accessible, organizations will accumulate specialized agents rapidly — for specific functions, specific data domains, specific customer workflows. Some organizations are already managing thousands of custom GPTs and specialized configurations.
Without intentional organizational frameworks for cataloging, governing, and retiring agents, this proliferation creates a new version of a familiar problem: the data sprawl of earlier enterprise technology cycles, now replicated at the level of AI agents. Discovering which agent is authoritative for a given task, ensuring agents are not operating with conflicting assumptions about the same business concepts, and maintaining consistency across an expanding agentic ecosystem are governance challenges that need to be addressed before the ecosystem grows large enough to make them intractable.
The Organizational Posture That Works
The organizations positioned to benefit from agentic AI share a set of characteristics that have nothing to do with which models or platforms they have selected. They have invested in information architecture as foundational infrastructure rather than a supporting concern. They define business outcomes before deploying technology and build measurement frameworks that reflect how agentic systems actually operate. They approach customization as a strategic capability rather than an implementation detail. And they treat governance as an enabler of scale rather than an obstacle to speed.
Agentic AI is becoming the interface layer between human intent and machine execution. For enterprises that build the right foundations, the potential is substantial. For those that do not, the complexity will compound faster than the value.
Frequently Asked Questions
What is the practical difference between a large language model and an agentic AI system?
A large language model responds to prompts — it generates text based on a user's input and the conversation ends. An agentic system pursues goals across multiple steps, coordinating between data sources, APIs, software systems, and other agents to accomplish tasks without requiring human intervention at each stage. The difference is between a system that responds and a system that acts. Agentic systems can initiate workflows, make sequential decisions, and operate autonomously within defined parameters.
Why does data quality matter more for agentic AI than for standard LLM deployments?
Standard LLM deployments that produce a wrong answer create a bounded, visible failure. Agentic systems that operate on incorrect or poorly structured data can propagate that error across multiple automated steps before anyone detects the problem. The scope of failure is broader and the traceability is harder. Every data quality problem that organizations have managed to work around in earlier AI implementations — inconsistent taxonomy, ungoverned metadata, fragmented content — becomes more consequential in agentic deployments because the system acts on that data rather than simply reporting from it.
What is agent sprawl and why should C-suite leaders care about it?
Agent sprawl is the accumulation of specialized AI agents across an organization without intentional governance frameworks to manage them. As agentic capabilities become more accessible, organizations rapidly build agents for specific functions, data domains, and customer workflows. Without clear cataloging, authority hierarchies, and retirement processes, it becomes difficult to determine which agent is authoritative for a given task, ensure agents share consistent assumptions about business concepts, and maintain coherent outputs across an expanding ecosystem. Organizations that do not address this proactively will replicate the data sprawl problem of earlier technology cycles at the agent level.
Why won't off-the-shelf agentic AI solutions deliver competitive advantage?
Foundation models are broadly available to all organizations, which means access to the same underlying capabilities does not differentiate. Competitive advantage in agentic AI comes from the orchestration layers, domain-specific configurations, and integration architecture that reflect an organization's unique operational realities — its data, its workflows, its customer expectations. Generic implementations provide a starting point. Differentiation comes from how organizations build, customize, and integrate their agentic ecosystems against their specific context.
What governance structures do agentic AI systems require?
Agentic systems require governance across three dimensions. First, content and data governance that ensures the information agents act on is accurate, current, and well-structured. Second, agent governance that catalogs what agents exist, what they are authorized to do, and how conflicts between agents are resolved. Third, outcome governance that defines success criteria, monitors performance against those criteria, and provides mechanisms for detecting, diagnosing, and correcting errors when they occur. Organizations that deploy agentic systems without these structures in place will find that complexity compounds faster than value.
How should C-suite leaders define ROI for agentic AI before deployment?
ROI for agentic AI should be defined at the level of specific business processes rather than at the level of technology capabilities. The relevant questions are: which process will this agent support, what does that process cost today, what outcome would constitute measurable improvement, and how will we know when we have achieved it? For customer support, relevant metrics might include first-contact resolution rate or handle time. For knowledge management, relevant metrics might include time-to-answer or content accuracy rates. Measurement frameworks need to account for multi-step, probabilistic workflows rather than relying on traditional KPIs designed for deterministic systems.
This article was originally published on CustomerThink.