Agents

How to build your first AI agent

An AI agent is not magic software that does everything. It is a loop: understand the goal, inspect the context, use tools, check the result, and ask for approval when the action is sensitive.

Start with one job

The easiest first agent is not a general assistant. It is a narrow helper: summarize unread emails, draft invoice follow-ups, check a website after deployment, create a weekly report, or turn a meeting transcript into tasks.

• Pick a task you already understand.
• Write down the inputs the agent needs.
• Decide what output counts as done.
• Decide which actions need human approval.

Give it tools carefully

Tools are what turn a chatbot into an agent. A tool might read files, search the web, open a browser, send an email, query a database, or create a pull request. The more powerful the tool, the clearer the guardrails need to be.

For a first agent, keep the tool list short. A website-checking agent might only need HTTP fetch, screenshot, and a checklist. An email agent might be allowed to draft replies but not send them without approval.

Add memory only where it helps

Memory should store stable preferences and durable facts, not every stray detail. Good memory says: use Pacific Time, verify production after deploys, prefer short summaries, or keep a list of known project paths.

Bad memory is a messy transcript pile. If the agent has to search through noise every time, memory becomes drag instead of leverage.

Verify the work

Every agent workflow needs a finish line. If it changes a website, fetch the live URL. If it writes code, run the test or open the page. If it drafts an email, show the draft. Verification is what separates a useful agent from a confident autocomplete box.