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Build your first AI agent

By Ashwin Chettiar Updated 15 Jun 2026 1 min read
Smartphone are dying and AI agents are taking over

Honest answer: your first AI agent should do one small, boring thing reliably — not run your business. Strip away the demos and an agent is a language model in a loop, calling tools and checking its own work. The magic is real, but so is the failure rate, and planning for the failures is the whole job.

What an agent actually is

Three parts, nothing more:

  • A model that decides what to do next.
  • Tools it can call — search, a calculator, an API, your database.
  • A loop that runs until the task is done or a limit is hit.

Everything else — memory, planning, “reasoning” — is built on top of that loop.

Start with something unglamorous

Good first agents:

  • Summarise your unread emails into a daily digest.
  • Turn a messy spreadsheet into a clean one.
  • Watch an RSS feed and flag what matters to you.

Bad first agents: anything that spends money, sends messages on your behalf, or touches production without review.

Where it will break

  • Tool errors. APIs fail. Your agent must handle that, not loop forever.
  • Cost. Loops multiply token usage fast. Set hard limits.
  • Confidence. Models state wrong answers with total certainty. Add a verification step.

The honest takeaway

A working agent is 20% clever prompting and 80% unglamorous error handling. Build the boring version, watch where it fails, and only then add capability. That’s not the demo — but it’s the part that actually ships.

Frequently asked questions

What is an AI agent, in simple terms?

A language model running in a loop that can call tools — search, a calculator, an API, your database — and check its own work until the task is done or a limit is hit.

What should my first AI agent do?

Something small, boring and useful — summarise unread emails, tidy a spreadsheet, or flag items in an RSS feed. Avoid anything that spends money or posts on your behalf.

Why do AI agents fail?

Usually unhandled tool errors, runaway loops that burn through tokens, and the model stating wrong answers with total confidence. Set hard limits and add a verification step.

Ashwin Chettiar — Founder of TechSober
Ashwin Chettiar
Founder of TechSober

Ashwin Chettiar is the founder of TechSober, covering tech, gadgets and AI automation — and building AI agents and automations for businesses.

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