Understanding AI Agents: From Hype to Real-World Application

The buzz around AI agents has intensified in recent years, but for many practitioners and businesses, the concept still feels abstract, wrapped in layers of marketing and technical jargon. At the center of this transformation is a simple yet powerful idea: agency. What does it mean to give an AI agent “agency”? And how does this reshape the future of intelligent systems?

In a recent podcast interview, AI expert and author Micheal Lanham shared deep insights into agentic AI — what it is, how it works, and why it matters now more than ever. With decades of experience spanning game development, machine learning in oil and gas, cannabis regulation tech, and modern agtech, Lanham brings a practitioner’s clarity to an often-confused space.

You can listen to the full podcast on Spotify, Apple, and SoundCloud.

Agency Is the Core of Agentic AI

According to Micheal Lanham, the essence of an AI agent comes down to one word: agency. While large language models (LLMs) like GPT-4 can respond to questions and complete tasks, they typically do so in a reactive, stateless manner. AI agents, by contrast, operate with autonomy. They are goal-seeking systems that can plan, decide, act, and iterate — moving beyond single-turn prompts toward solving complex, multi-step problems.

This distinction is critical. When we ask ChatGPT a question, we’re engaging a powerful language engine. But when we give an agent a goal — say, “research and draft a market analysis for renewable energy startups” — we’re giving it the authority and responsibility to decide how to achieve that goal. That’s agency in action.

From Prompt Engineering to Decision-Making

Prompt engineering remains foundational in the development of effective AI agents. As Lanham notes, this practice has evolved beyond clever phrasings or gimmicks. It’s about structuring prompts to guide how an agent should think, reason, and act.

Techniques such as “chain-of-thought” prompting and the ReAct (Reason + Act) framework help guide agents through multi-step tasks. These methods are particularly important when agents are expected to use tools, invoke APIs, or collaborate with other agents. Lanham emphasizes that prompt engineering should be approached with the same rigor as traditional software architecture — well-structured, testable, and iteratively improved.

In fact, he often uses one LLM to generate prompts for another, leveraging the comparative strengths of different models. This kind of cross-model orchestration is becoming increasingly common as practitioners experiment with hybrid stacks.

AI Agents vs. Traditional Automation

What distinguishes AI agents from chatbots, rules-based systems, or robotic process automation (RPA)? According to Lanham, the difference lies in reasoning and adaptability. While RPA follows rigid rules to automate tasks, agents bring dynamic problem-solving into the mix. They reason, evaluate context, and determine which tools to use at which time.

For example, consider an agent tasked with booking travel. Rather than following a fixed script, it might search multiple travel sites, check your past preferences from memory, evaluate options based on constraints, and assemble a personalized itinerary, iterating as needed. This kind of autonomy is what sets agents apart from earlier automation tools.

The Rise of Multi-Agent Systems

Lanham is also bullish on multi-agent systems, where teams of AI agents work collaboratively to solve complex tasks. These agents can be assigned roles — like developer, editor, or QA tester — and pass tasks among themselves in a coordinated way.

Such systems are especially useful for problems that require exploration, iteration, and negotiation — scenarios where the goal is known, but the path to get there isn’t. Multi-agent frameworks like AutoGen and CrewAI are making this possible, though Lanham is quick to note that these are not production-ready solutions for every context. They’re ideal for discovery, creative work, and problem-solving at the edges of complexity.

Memory, Evaluation, and Feedback Loops

Effective agents need memory. Lanham outlines multiple types: short-term, long-term, procedural, and episodic. These memories can be stored semantically via vector databases or in structured formats like relational or graph databases, depending on the use case.

Crucially, agents also need to forget. Forgetting is a feature, not a bug — it allows agents to prioritize recent or relevant memories and avoid cognitive overload.

Evaluation is another pillar of agent development. Lanham advocates for rigorous grounding evaluations, especially when deploying agents in mission-critical environments like agriculture or enterprise support. He uses both human feedback and AI-based evaluators (i.e., agents that judge other agents) to ensure outputs are accurate and aligned with source data.

Tools like Arize Phoenix and LangFuse help trace and inspect agent behavior, while OpenTelemetry and OpenAI’s SDK provide tracing out of the box. These observability tools are key to moving from experimentation to production.

The Role of MCP: Message Control Protocol

A major recent advance in agent systems is the emergence of the Message Control Protocol (MCP), a standard for connecting agents with tools. Rather than hard-coding integrations, MCP allows agents to call external tools — such as Slack, Notion, or PowerPoint — through structured interfaces. This enables a plug-and-play architecture for agents to interact with software ecosystems dynamically.

Lanham highlights platforms like Smithery.ai, which offers thousands of open MCP servers, as a game-changer. But he cautions developers to vet these tools carefully for quality and security.

From Tools to Teammates

Looking ahead, Lanham sees agents transitioning from tools to teammates. But he’s pragmatic: “I don’t think 90% of the population is ready to work alongside digital coworkers,” he notes. Still, the potential is there. With better reasoning, memory, evaluation, and guardrails, agents may soon support — or even lead — workflows across industries.

In his own work, Lanham is already building agents that serve as front-line support for high-stakes environments like agricultural equipment troubleshooting. These agents are backed by robust evaluation systems, memory architecture, and human-in-the-loop feedback.

Final Thoughts

AI agents are no longer science fiction. They’re here, they’re evolving, and they’re becoming increasingly capable. But as Micheal Lanham emphasizes, building real-world agentic systems requires more than hype or experimentation. It demands intentional architecture, trustable evaluation, and a commitment to grounding in user needs.

For those looking to build or adopt agent systems today, Lanham’s work — especially his book AI Agents in Action — is an essential resource. It bridges the gap between foundational concepts and applied practice, offering hands-on examples, real code, and candid insights from the frontlines of AI development.

If you’re exploring the intersection of autonomy, reasoning, and tool integration in AI, Micheal Lanham’s work should be on your radar.