The pragmatic guide to AI agents in the enterprise

This post first appeared in The Analytics Engineering Roundup.

What does it mean to be agentic? Is there a spectrum of agency?

In this episode of The Analytics Engineering Podcast, Tristan Handy talks to Sean Falconer, senior director of AI strategy at Confluent, about AI agents. They discuss what truly makes software "agentic," where agents are successfully being deployed, and how to conceptualize and build agents within enterprise infrastructure.

Sean shares practical ideas about the changing trends in AI, the role of basic models, and why agents may be better for businesses than for consumers. This episode will give you a clear, practical idea of how AI agents can change businesses, instead of being a vague marketing buzzword.

Please reach out at podcast@dbtlabs.com for questions, comments, and guest suggestions.

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Key takeaways

Sean, can you give us the TLDR on your career and what you're working on today?

Sean Falconer: I've always worked at the intersection of data, engineering, and AI. From academia studying computer science, into industry as a founder, then to Google, I worked on conversational systems and privacy/security in AI. Currently, at Confluent, I'm leading our AI product strategy, balancing both technical and go-to-market roles.

You moved from being deeply technical into marketing and sales. What drove that transition?

I was forced into it as a founder. Initially uncomfortable, but it taught me huge respect for marketing and sales. I had to learn by making many mistakes, eventually building out entire marketing and sales functions. I realized how challenging and critical these roles are.

You were at Google before ChatGPT launched. Did you foresee the transformative nature of these technologies?

Honestly, no. Having seen earlier disappointments in conversational AI (like Microsoft's Alice), I was skeptical initially, even as ChatGPT emerged. It wasn’t obvious we'd soon experience this revolution.

You’ve written about three waves of AI. Can you describe these?

Yes. Wave one was predictive AI, traditional ML models trained for specific tasks like fraud or spam detection—effective but rigid. Wave two introduced generative AI, or foundation models, trained on vast general datasets, flexible but lacking specific business context. The third wave, agentic AI, involves AI systems that can reason, dynamically choose tasks, gather information, and perform actions as a more complete software system.

Do foundation models replace traditional ML methods?

Sometimes they can, but it doesn’t always make sense. An LLM might do sentiment analysis well enough, but a traditional model may be more efficient and cheaper. Think of using an LLM as cutting steak with a chainsaw—possible, but unnecessary.

Let's clarify "agents." What makes software truly agentic?

It’s software that can dynamically decide its own control flow: choosing tasks, workflows, and gathering context as needed. Realistically, current enterprise agents have limited agency to ensure reliability. They're mostly workflow automations rather than fully autonomous systems.

You mentioned a spectrum of agency. Is this similar to autonomy in self-driving cars?

Exactly. Highly autonomous agents are appealing but not practical yet. Most enterprise success stories involve structured workflows with clearly defined boundaries.

Why have agents taken off more in enterprises than consumer apps?

Enterprises have many well-defined, high-value tasks perfect for automation. Consumer scenarios demanding high agency—like planning complex trips—are still too unreliable. Enterprises can benefit significantly even from limited agentic capability.

Is an agent just a microservice?

In many ways, yes. An agent functions like a microservice with extra capabilities (using LLMs for decisions). Deployment considerations like state management and long-running tasks differ slightly, but fundamentally it’s similar.

What tools and frameworks help build effective agents?

Start with frontier models like GPT-4 or Claude. Frameworks include LangChain, Microsoft Autogen, and CrewAI. But for real-world deployment, treat it as rigorous software engineering with observability, scalability, and robustness in mind.

Are organizational barriers bigger than technical challenges?

Yes. AI efforts are often mistakenly tasked to data science teams rather than cross-functional software teams. Successful companies create dedicated teams blending software engineering skills and data expertise to build reliable agentic systems.

What pitfalls should teams avoid?

Avoid building monolithic agents. Break systems into smaller, well-defined units in a multi-agent architecture. Use event-driven frameworks to avoid rigid, hard-to-maintain dependencies.

Chapters

• [00:00] Introduction: What's all the hype about agents?
• [01:10] Meet Sean Falconer: A journey from engineer to AI strategist
• [04:10] Learning marketing as an engineer-founder
• [05:50] Inside Google's AI efforts before ChatGPT
• [09:00] What does it mean to run AI strategy?
• [10:45] Three waves of AI: Predictive, Generative, and Agentic
• [16:30] Will foundation models replace traditional ML?
• [18:30] Defining agents clearly: Beyond the buzzword
• [22:00] The spectrum of agency: From controlled workflows to open-ended tasks
• [25:30] Why agents fit better in enterprises than consumer apps
• [28:00] Agents as microservices: A practical view
• [35:00] What tech stack is needed to build effective agents?
• [37:50] Organizational challenges in adopting agents
• [39:30] Models that are favorites for developers
• [43:30] Why software engineers are best placed to build agents
• [46:00] The technical stumbling blocks in building agents
• [48:00] Concluding thoughts: Beyond POCs to production agents