AI agents for routine work: how to start without disappointment

An "agent" today means a model with tools attached: a terminal, files, a browser, access to a codebase. That combination does take over routine work — but only when the task is stated concretely. A vague "sort out my project" returns a vague result.

What agents already do well

• drafts of letters, descriptions and documentation based on your materials;
• processing batches of similar files: renaming, normalizing to a template, extracting data;
• small purpose-built scripts, from spreadsheet processing to an API wrapper;
• a first pass over code: duplicates, outdated dependencies, obvious mistakes.

Step 1. One task, one tool

The first run is not "set up an agent for everything" — it is one recurring task that annoys you most. A CLI agent or an editor plugin is enough. Model access is bought separately — that is where an API key from the storefront comes in.

Step 2. The wording

A good task for an agent has three parts: the input, the expected result and the criterion you will check against. "Go through the reports folder and build one CSV with monthly totals; verify the totals against the manual March calculation" works. "Clean up the reports" does not.

Step 3. Check and repeat

You always verify the first result, not the agent. If everything matches, the task becomes permanent. If not, the wording gets refined, and the second run usually works. That is how you get quiet automation that saves hours instead of "magic".

An agent is an intern with infinite patience: routine never tires it, but it needs clear tasks and a human checking the result.

Once the typical tasks are proven, a dedicated model access starts to pay off: packages for Codex, Claude and Kimi are in the storefront catalog.