An agent is just an LLM in a loop with tools. These are the patterns real 2026 agent products are built from.
Between "chatbot" and "fully autonomous" is a spectrum. Start every build at ask permission before risky actions, and earn autonomy with tests + guardrails.
If each step is 95% reliable, a 20-step task finishes correctly only ~36% of the time (0.9520). So: shorten chains, recover instead of cascading, and checkpoint progress.
Run a task on a schedule or until a goal is met — "keep improving X until it passes." Cap the steps so it can't run away.
Ask several AI "seats" (e.g. a builder, a skeptic, a safety reviewer) the same question, then synthesize. Diverse views catch what one model misses.
When one agent delegates to another, write a contract: objective, output format, boundaries, budget, what to do when blocked. Vague delegation is the #1 failure.
Does it observe real results (not just plan)? Is there a step cap? Does a failed step retry or re-plan instead of poisoning the run? Can you stop it and see what it did? If yes to all — you can let it off the leash a little.