AI agents are everywhere right now, but many teams struggle to turn the idea into something that actually ships. The promise is appealing: give a system a goal, expose a set of tools, and let it plan and act on its own. In practice, agents are easy to prototype and hard to make reliable, safe, and cost-effective. Developers are left asking what really makes something an agent, how much autonomy is too much, and how these systems differ from the LLM-based tools they already use.

This talk focuses on the practical mechanics of building AI agents. We’ll look at how agents are structured, how tool use works in real systems, and how protocols like MCP shape agent architectures. We’ll also show how agent-based designs reduce custom orchestration code by shifting control flow from hand-written pipelines to reusable tools and declarative goals. Along the way, we’ll discuss concrete implementation choices, including model selection tradeoffs, tool boundaries, memory strategies, and evaluation approaches like task completion and cost tracking. Rather than treating agents as a binary, we’ll frame them as a set of patterns and tradeoffs developers can apply intentionally, helping you decide when an agent is the right abstraction and when simpler automation will deliver more value.

I have given this talk previously at dev up 2025 and IndyPy2025