In the light-speed evolution of artificial intelligence, we are experiencing a critical inflection point. The landscape is shifting rapidly from large language models (LLMs) as standalone tools to a new era of agentic systems — AI that doesn’t just generate answers, but actively orchestrates tasks, adapts dynamically, and interacts autonomously with other systems.
In a recent discussion among AI practitioners and strategists, it became clear that while hype continues to dominate headlines, a quieter, more substantial transformation is underway. For executives and senior technology leaders, understanding this transition, and preparing for it, will be key to ensuring that AI initiatives deliver real business value rather than becoming costly science projects.
“AI is no longer something you go to and use. It’s becoming the invisible fabric woven into everything we do,” remarked Jeff Evernham, Chief Product Officer of Sinequa, a division of Chapsvision, underscoring a central theme of the conversation.
Beyond LLMs: The Rise of Agentic AI
Traditional LLMs — think GPT-4 or Claude — have captivated enterprises with their ability to generate human-like text and streamline processes from summarizing documents to drafting emails. However, their capabilities have been largely passive. They respond to prompts but do not initiate actions or handle complex workflows.
Agentic AI changes that.
As Olivier Têtu, Senior Product Manager at Commerce AI for Coveo noted, “It’s not just about retrieving information anymore; it’s about orchestration — enabling AI to act as an intelligent intermediary between users and systems.”
Agentic systems are designed to operate with autonomy, interacting not only with data but with other software, APIs, and even other agents. They can dynamically adjust workflows, troubleshoot issues, and coordinate multiple steps to accomplish goals without requiring human micro-management.
This evolution mirrors a broader pattern we’ve seen in technology before. Much like the shift from monolithic applications to microservices, we’re now decomposing intelligence into modular, task-specific agents that can be composed and orchestrated as needed.
“Think of it as microservices for human tasks, but with a natural language interface,” as Patrick Hoeffel, Senior Technical Architect for Perficient aptly summarized.
Why This Matters for Enterprises
For C-Suite leaders, the implications are profound.
Agentic AI offers the potential to automate not just discrete tasks but entire business processes. It moves AI from being a passive assistant to an active collaborator reducing time-to-decision, improving service levels, and driving productivity gains across departments.
Yet, this transition is not without challenges.
1. Data Quality Remains Paramount
One recurring theme in the conversation was the critical importance of data. Not surprising, but worth repeating.
Everything we do with AI is compensating for decades of poor data management. Without high-quality, well-organized information, even the most sophisticated agent will struggle.
Phillip Ryan, Principal Consultant at GlassLeopard Technologies, reinforced this point: “Search is not just a query-time problem; it’s a data management problem.” While agentic systems can orchestrate across datasets; they cannot fix underlying data quality issues. Enterprises must still invest in foundational data, i.e., information architecture, taxonomy development, and metadata management, to ensure that AI initiatives are grounded in reliable, structured information.
2. Agentic AI Is Not a Panacea
While agentic AI represents a leap forward, it’s important to temper expectations.
“People still have a false sense of expertise when interacting with LLMs,” warned Têtu. Despite their impressive outputs, today’s models are not reasoning engines; they are fundamentally pattern-matchers. This can lead to hallucinations — plausible but incorrect results — especially when LLMs operate outside their trained domains or are fed poor-quality inputs.
Agentic systems, while more dynamic, inherit this limitation. They can automate tasks but must be carefully scoped and monitored to avoid unintended consequences. Fine-tuning models, grounding them in authoritative knowledge sources, and implementing strong governance will be critical.
3. Customization Will Be the Norm
Another insight that emerged is the highly contextual nature of agentic AI applications.
“Every business is going to need its own specialized agent, like a Menards agent or a Bass Pro Shops agent, tailored to their data, workflows, and customer expectations,” explained Têtu.
Generic solutions will not be enough. Enterprises must be prepared to invest in customized orchestration layers that reflect their unique operational realities. Off-the-shelf LLMs may provide a starting point, but differentiation will come from how organizations build, tune, and integrate their agentic ecosystems.
4. The ROI Conundrum
Despite the excitement, many enterprises are still struggling to define and measure the ROI of their AI investments.
“Right now, the pressure technical leaders are under is sometimes more about deploying AI than deriving value from it,”Ryan noted candidly.
This is a critical warning for C-level leaders. Simply deploying AI technologies does not guarantee business outcomes. Clear success metrics must be established upfront, such as improving customer satisfaction, reducing call center volume, or accelerating product development cycles.
Furthermore, agentic systems introduce new complexities in monitoring and evaluation. Traditional KPIs may not suffice; enterprises will need more sophisticated measurement frameworks that account for dynamic, multi-step workflows and probabilistic outputs.
Practical Use Cases Emerging
While much of the technology is still maturing, there are several areas where agentic AI is already proving its worth:
Software Development: Ryan described how agentic systems have transformed his coding workflow. Using tools like Replit, multiple agents collaborate in real-time to suggest, test, and refine code dramatically boosting productivity.
Data Preparation: Agentic AI is adaptable for data management tasks like enrichment, validation, and summarization where humans traditionally struggled with scale and consistency.
Customer Support: Intelligent agents can handle complex customer queries, route issues appropriately, and even escalate to human agents when necessary, all while maintaining context and continuity.
Internal Knowledge Management: Organizations can use agentic systems to curate and manage knowledge bases, ensuring that employees have fast, accurate access to the information they need without wading through outdated or irrelevant documents.
The Road Ahead: Cautions and Opportunities
As with any transformative technology, agentic AI will not unfold in a straight line. There will be missteps, overhyped promises, and real operational challenges.
We’re moving into an era where the complexity of the systems we build will exceed human understanding. This raises important questions about transparency, governance, and risk management.
Moreover, the proliferation of GPTs and specialized agents, while powerful, introduces new challenges around discoverability and management. Moderna, a poster child for OpenAI has over 3000 GPTs. How do you find the right GPT among thousands? Without intentional organizational frameworks, enterprises risk replacing their data sprawl with an agent sprawl.
Yet, for those who approach this space thoughtfully with a commitment to solid information foundations, clear business objectives, and disciplined execution, the opportunities are enormous.
AI is not going away. It is becoming the infrastructure of work itself. And agentic systems are poised to be the interface layer that finally bridges the gap between human intent and machine execution.
Key Takeaways for C-Level Leaders:
- Focus on information architecture and data quality. They are the bedrock of successful AI systems.
- Treat agentic AI as a strategic enabler, not a silver bullet.
- Plan for customization. One-size-fits-all solutions will not deliver a competitive advantage.
- Define clear, measurable business outcomes before deploying AI solutions.
- Invest in governance frameworks to manage risk and complexity as agentic ecosystems grow.
The future belongs to enterprises that can seamlessly integrate human and machine intelligence — not as disparate tools, but as orchestrated, dynamic systems that enable new ways of working and new levels of performance.
The shift to agentic AI has begun. Will your organization be ready?
How this article was written
This article is a summary of a discussion with AI and Search experts Patrick Hoeffel, Jeff Evernham of Chapsvision, Olivier Tetu of Coveo, and Phillip Ryan of GlassLeopard Technologies. (Patrick, Jeff, Philip, and Olivier participated in an Earley Information Science 7-part series on Search and AI)
After a freeform hour-long discussion with Patrick, Jeff, Olivier, and Phil, I uploaded the transcript to ChatGPT and provided context for my tone and voice from prior articles and white papers written without the use of LLMs. I then edited and uploaded the resulting article to Claude along with the original transcript asking for additional insights from the discussion (some valuable points were missing). A final round of editing leveraged a custom GPT built by our firm to further align with our value propositions SEO and GEO. Additional expanded iterations were also developed into whitepapers for business and technology leaders. This provides expert insights with a unique voice for each point of view substantiated and enriched. The core is human-generated, and the result is informative and actionable.
– Seth Earley, CEO Earley Information Science