Like many developers, I started building LLM agents by stringing together API calls and hoping for the best. It worked, for a while. My agents could browse the web, execute code, and call APIs. They could decompose tasks into sub-steps.
Then I hit a wall.
Every morning, I would wake up to logs of failures I'd seen the day before. The same buggy code modifications. The same incorrect API parameters. The same flawed reasoning paths — repeated, session after session, as if the agent had learned nothing. Because it hadn't. Every conversation was a fresh start.
I spent months trying to fix this. I tried prompt engineering. I tried better tool definitions. I tried chaining. Nothing worked, because the problem wasn't in any single interaction — it was in how we build agents. We were building them as functions, when they should be built as living systems.
What GBase Is
