01 / ANATOMYRecognize the five components first
Agent = model + context + tools + loop + constraints. A minimal agent stores state around a goal, lets the model decide a next move, calls a tool, reads the result and continues. Its five parts are the goal and finish condition; state about what is known, done, missing and awaiting confirmation; controlled tools; a loop; and constraints from permission, budget, confirmation and verification.
02 / TRACEA reliable run leaves a readable trajectory
For “find the latest contract in a folder and summarize risks”, observe the file list, decide the newest version, read it, extract clauses, compare signature date and version, then return risks with source pages. If anything fails, the trace must locate the failure instead of ending with “sorry”. Retain observation, decision and reason, tool and parameters, actual feedback, then verification of whether to continue or stop.
03 / CHOICENot every task deserves an agent
A fixed process with certain input and output is usually cheaper and more stable as a normal program or workflow. An agent’s autonomous loop has value only when the path must change according to intermediate results. Do not add uncertainty just to make a deterministic process sound intelligent.
04 / TRY ITTry the system difference without building it
A travel plan generated once is not the same as a system that checks weather and trains, adjusts for budget, saves an itinerary and waits for confirmation before payment. The difference is not a longer answer but a task divided into observation, action, feedback and correction. The site’s interaction lab puts one task in different settings with context, retrieval or guardrails removed so you can see where process and result diverge, without calling a real model.
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