The best AI agent isn’t one that runs forever. It’s one you can kill at any moment and resume without losing the truth. If progress lives only in chat history, the system is fragile. Plans, decisions, artifacts, and checkpoints should survive the model and the sandbox.

An AI agent saying “done” is not evidence. “Tests passed” needs the command, scope, output, and exit code. “Deployed” needs a real runtime check. The more work we delegate, the more verification becomes the bottleneck.

Persistent agent memory sounds useful... until stale decisions, duplicates, contradictions, and expired context pile up. Short-lived specialists are easy to reset. Long-lived project memory slowly rots. The hard part isn’t storage. It’s maintenance.

“What’s the best model?” is usually the wrong question for agent systems. My orchestrator and reviewer need judgment. Explorers need speed. Workers need reliable tool use at a sane cost. I’m routing models by failure cost, not leaderboard rank. How are you assigning yours? #AI #Dev #Vibe #Code

AI agents are not making focus obsolete. They are changing where focus happens. I’m testing a workflow where each project keeps persistent context, while temporary specialists handle focused work. The hard part is no longer only execution. It’s orchestration and verification.