Building with AI Agents: What Actually Works in Practice
Hands-on lessons from building agentic AI systems that plan, use tools, and recover from errors.
This week I went all-in on building a proper agentic AI system — one that could plan a multi-step task, call external tools, check its own output, and recover when something went wrong. I had tinkered with agents before in short experiments, but this time I wanted to push it to something resembling production-level reliability.
What I quickly ran into is that agents are deceptively hard. The model could plan reasonably well in isolation, but reliable execution across five or six sequential steps was a completely different challenge. Adding a dedicated planning step at the start — where the model outlined its intended actions before executing any of them — cut down failure rates noticeably. Memory was the other big issue: without tracking what actions had already been completed, the agent would occasionally loop or lose context midway.
My final takeaway this week is that building reliable AI agents is really a systems engineering problem as much as it is an AI problem. Good tool abstractions, state management, and graceful error recovery matter just as much as the underlying model's intelligence. There is still a lot of rough edges in this space, but I can already see where it is heading, and it is exciting.
