We’re at a crossroads in AI development, where Large Language Models (LLMs) like GPT-4 can plan, fetch data, make decisions, and even write production-grade code. Yet, most so-called ‘AI agents’ still rely on rigid pipelines, chained prompts, and hacky orchestration. Where is the actual autonomy?
I’ve tried various AI agent platforms, but they all seem to hit the same ceiling: either the agent mindlessly follows instructions or the ‘think-act-observe’ loop falls apart when context shifts slightly. It’s clear that we’re building agent frameworks, but not yet building true agents.
Autonomy isn’t just about running a loop and grabbing coffee. It means the agent chooses what to do next, declines tasks it deems irrelevant or risky, asks for help from humans or other agents, and evolves strategy based on past experience. Right now, most of that still lives in whitepapers and demos, not production.
So, is it truly possible to build fully autonomous agents in 2025, even in narrow domains? Or are we just dressing up LLM orchestration and calling it autonomy? Share your thoughts, cases, failures, and architectures. Let’s have a real discussion about the state of autonomous AI agents.