Brent Laster opened his session by grounding the current wave of AI in a broader timeline. From early machine learning to conversational models like ChatGPT, he explained that we’re now entering the “agent era,” where large language models (LLMs) are extended to interact with real-world tools, APIs, and even other models. This shift, Brent argued, positions AI agents as the new applications, with the potential to offload routine work and unlock new efficiencies in software and beyond.
To demystify how AI agents function, Brent shared a practical breakdown: Agents use LLMs as a kind of “brain” that processes tasks through a workflow of reasoning, action, and observation. These systems analyze a prompt, decide on a course of action (like calling an API), and reflect on results before responding. This thought-action-observation pattern is an example of what gives agents “agency,” enabling them to operate with autonomy in structured environments like developer tools, customer support systems, or even flight booking assistants.
Brent highlighted a key takeaway for developers: Agents aren’t just chatbots with extra steps. They combine reasoning, memory, decision-making, and multi-step coordination to complete complex tasks. Whether it’s calling APIs, chaining prompts, routing to specialized models, or even critiquing output with evaluator models, agents provide a framework for integrating AI into real workflows, not just demos!
Throughout the talk, Brent stressed that understanding the core design patterns of agents is essential for developers who want to build or extend them responsibly. From orchestration to multi-agent collaboration, frameworks like Autogen, CrewAI, and LangChain offer a starting point, but developers still need to grasp how these components work together. His advice? Start small, focus on use cases, and keep your human-in-the-loop practices in place as you scale.
Agents are more than chatbots – AI agents use LLMs as reasoning engines, integrating real-world tools, memory, and decision-making workflows to complete complex tasks.
Reason, act, observe – At the core of agent design is a repeatable loop that evaluates each step, enabling more accurate, flexible, and autonomous outcomes.
Developers need to understand the patterns – Tools like Autogen can help, but a working knowledge of agent types, prompting strategies, and control structures is key to building useful systems.
Conclusion
Brent’s session offered developers a clear path into a complex topic. AI agents are reshaping how LLMs are applied, moving beyond conversations into dynamic workflows. For teams exploring how to integrate agents into real-world software, the message is clear: Know the patterns, understand the tools, and stay curious about how LLMs can think, act, and adapt—just like the systems we aim to build.
