I've been tinkering with agentic systems for a while now, and this post nails some key pain points that hit close to home. The emphasis on splitting context and designing tight feedback loops feels spot on—I've seen agents go off the rails without them, hallucinating solutions because the prompt was too bloated or the validation was half-baked. It's like building a machine where every part needs to click just right, or else you're debugging forever.

What really resonates is the bit about frustrating behaviors signaling deeper system issues, not just model quirks. In my own experiments, I've had agents stubbornly ignore tools because I forgot to expose the right APIs, and it made me rethink how we treat these as "intelligent" when they're really just following our flawed setups. It pushes us toward more robust orchestration, where humans handle the high-level intentions and AI fills in the execution gaps seamlessly.

This ties into broader ideas on how AI interfaces will evolve as models get smarter. I extrapolate more of this thinking and dive deeper into human–AI interfaces on my blog if anyone’s interested in checking it out: https://henriquegodoy.com/blog/stream-of-consciousness