First Agent Loop in a Hosted Tool

A hosted agent — Claude Code, ChatGPT's agent mode, Cursor — doesn't just answer once; it runs a loop: plan, act, observe, adjust, repeat, until it decides the task is complete or it hits a wall. This is fundamentally different from a chat exchange, where you ask and it answers once. The loop means the agent can course-correct mid-task based on what it observes, which is both the power and the risk.

Understanding the loop changes how you interact with it: instead of crafting one perfect prompt, you're setting up a good starting brief and then supervising a process that unfolds over minutes, not seconds.

Not every task is a good first delegation. Use the BVR test: is it Bounded (confined to a small, well-defined scope — one folder, not your whole drive), Verifiable (you can spot-check the output and know if it's right), and Reversible (if it goes wrong, nothing real is lost)? A task that fails any of these three should wait until you've built more trust and better briefing habits.

A task like building an index of 90 blog post files — read-only, one folder, spot-checkable rows — passes all three. A task like "clean up and reorganize my entire Google Drive" fails all three at once.

Before running any hosted agent on real work, set up guardrails: work in a dedicated workspace folder rather than your live files, use copies rather than originals so mistakes cost nothing, and set the permission mode to "ask" so the agent checks with you before taking consequential actions rather than running fully autonomous on a first task.

This setup takes a few minutes and should feel almost excessive the first time — that's the point. It's much easier to loosen constraints once you've built calibrated trust than to recover from an agent that moved fast on the wrong files.

A good brief for an agent has the same anatomy as a good prompt, but with more emphasis on scope and success criteria: a clear goal, an explicit definition of done, any constraints (what it must not touch or change), and what you want it to log as it works. "Here are 90 exported blog posts as .md files. Build an index.csv: title, date, word count, topic tags from this list, and flag posts mentioning deprecated product names from this list. Modify nothing; plan first" hits all four.

The phrase "plan first" is doing real work here — it forces the agent to surface its intended approach before touching anything, giving you a checkpoint to catch problems before they become actions.

Once the agent starts, your job is to read its plan and its actions as they happen, not to approve every keystroke. Skim the plan for anything unexpected or out of scope, then let it work, periodically checking observed outputs against what you expected. This is a different skill than either fully trusting an agent or hovering over every action — it's targeted attention.

The goal is calibration: over several tasks, you learn which kinds of steps this particular agent tends to get right unsupervised and which ones deserve a closer look every time.

Sometimes the agent's plan includes something you didn't ask for — in one worked example, a plan to build a file index also includes renaming files, which was never requested. The right move is a clear, immediate redirect: "skip renaming, index only," and the agent adjusts and continues. This is different from aborting the whole task, which throws away good progress over a fixable deviation.

Learn to distinguish three responses: redirect (the task is fine, one step needs adjusting), abort (the whole approach is wrong, stop and restart), and let it finish (a minor imperfection you'll fix afterward is cheaper than interrupting). Steering a running agent well is itself a skill that improves with reps.

When the agent finishes, don't just check the output and move on — debrief the run. What did it actually do, step by step? What would you brief differently next time to avoid the intervention you had to make? And critically: does this task recur often enough that it's worth turning into a reusable Skill or instruction file?

Ending a delegation with "write what you did as a reusable instruction file" turns a one-off task into a repeatable asset — the next time you need the same kind of work done, you're not starting the brief from scratch.