Put a Small Agent Plan Together

You don't need to build an autonomous company to understand AI agents. Start with one small job that would feel genuinely helpful if it happened with less manual effort.

Imagine you'd like a weekly roundup of events related to a hobby. The goal is simple: find three relevant events and prepare a short note with dates and links.

Work backwards. The agent needs a source for current information, a way to select relevant events and somewhere to put the draft. Then decide when it should run, what it should do if there are no matches and whether anything needs your approval.

You can draw that as a few boxes: trigger, gather information, decide what fits, prepare output, review. You might realise the job is better as a fixed workflow than as an agent. That's a useful design decision, not a failure of imagination.

By the end, you should be able to describe a small AI-powered process that might genuinely improve your life. Later in the course, you'll learn how to combine tools and turn an idea like this into something real.

Let's design a small agent without writing code. Suppose you help organise a neighbourhood group and spend time every week turning messages into a short update. You'd like an assistant to collect the approved items, organise them and prepare a document that you can edit before sharing.

Begin with the outcome. Not 'build an AI agent', but 'every Friday, prepare a draft update containing this week's approved announcements and dates'. That sentence gives the project a purpose and a recognisable finish line. The technology comes afterwards.

Next identify the sources. Perhaps approved announcements live in a shared folder and the dates are maintained in a calendar. Those are the inputs. A connected system needs permission to read them. If the folder contains other unrelated documents, give the assistant access only to the material needed for this job where the tools allow it.

Now outline the stages. First retrieve new approved announcements. Then identify dates and remove duplicates. Next organise items under a small set of headings. Finally create an editable draft. The process should pause for your review before anything is shared externally. You've now described a useful workflow with an explicit handover.

Think about exceptions. What if an announcement has no date? What if two submissions disagree about the venue? What if the calendar connection fails? You don't need to design a perfect system on day one, but you should decide whether the assistant may omit uncertain material, flag it for review or stop the process. A good system makes uncertainty visible rather than guessing.

Choose one success test. Run the workflow with a small set of fictional announcements and see whether it creates the expected document with the right dates. Then add a duplicate and a missing date to see how it behaves. That is much more informative than judging the system by how articulate its explanation sounds.

You might discover that a simple scheduled template and a manual upload deliver most of the benefit. That's perfectly acceptable. The purpose isn't to use the most autonomous technology available. It's to remove unnecessary work with a process you can supervise, improve and understand.

Only once the small version works should you consider extra capabilities, such as collecting updates automatically or preparing a draft email. Each added connection changes the system's permissions and possible failure points. Grow the workflow because a real need justifies it, not because an AI demonstration made autonomy look effortless.

You can make the first version entirely manual. Collect five fictional announcements, provide them to an assistant and ask it to produce the draft. Inspect the result and refine your instructions. Once that works, try connecting the document tool so the draft is created in the right place. Only then consider scheduling the process.

Each stage answers a different question. The first tests whether the AI can interpret and organise the content. The second tests whether a connected tool can create the artefact reliably. The third tests whether the recurring workflow behaves sensibly without you starting it each time.

That sequence gives you evidence of progress. Rather than claiming you've 'built an agent' because it can describe a plan, you can point to a working draft, an authorised tool action and a repeatable process. Real capability is demonstrated through what the system consistently accomplishes.