Designing Your First Agent: A Beginner-Friendly Agent Design Canvas

Designing Your First Agent

Today's outcome: Complete an Agent Design Canvas for a study assistant and test how it handles one unexpected situation.

This is Day 5 of AI Agents in 30 Days. It combines the ideas from the first four days into one practical agent design.

Design the decisions before the code

An agent design should explain more than what the model says. It should describe the goal the system pursues, the information it receives, the tools it may use, the decisions it may make, and the situations where a person must take control.

OpenAI's practical guide describes the basic agent foundation as a model, tools, and instructions, with clearly defined guardrails around tool use and workflow execution. It also recommends starting with a single agent and adding complexity only when needed. That gives us a useful principle for Day 5: start small, make the boundaries explicit, and test the edge cases before building more.

Source: OpenAI - A practical guide to building agents

The Agent Design Canvas

Use these six fields to turn a vague agent idea into a design you can explain and test.

The model is not the whole design. The same model can behave very differently depending on the context, tools, instructions, and permissions surrounding it.

Practical example: the learner misses Tuesday

Suppose the learner planned a database lesson for Tuesday but did not complete it.

A controlled study assistant can follow this reasoning:

1. Observe that Tuesday's lesson is incomplete.

2. Read the remaining plan and the learner's available calendar slots.

3. Find an open slot later in the same week.

4. Move the unfinished lesson only if that action is within its allowed decisions.

5. Preserve the original deadline and required topics.

6. Ask the learner if meeting the plan would require deleting a topic or extending the deadline.

7. Check that the revised schedule is realistic before stopping.

The important design choice is not simply that the agent can edit a plan. It is that the agent knows which edits are reversible and low risk, and which changes need human approval.

A simple decision boundary

This boundary keeps the agent helpful without pretending that every decision should be autonomous.

Try this today

Choose one small agent idea, such as a study assistant, meeting-preparation assistant, or reading-list organizer.

1. Complete all six fields in the canvas.

2. Write one action the agent may take independently.

3. Write one action that always requires approval.

4. Give the agent one unexpected situation.

5. Check whether your design explains what happens next.

Completed example you can copy

Agent: Weekly Study Assistant

Goal:
Keep my weekly learning plan realistic and complete.

Context:
My learning goal, required topics, progress, deadlines, and available study slots.

Tools:
Read the plan, read calendar availability, and propose or apply schedule changes.

Allowed decisions:
Move an unfinished lesson to another free slot within the same week.

Approval rules:
Ask before deleting a topic, changing the final deadline, or adding more weekly hours.

Success check:
Every required topic has a realistic slot, there are no overlaps, and protected constraints remain unchanged.

Test case:
I miss Tuesday's lesson and only one open slot remains on Friday.

Common mistakes

Starting with too many tools

Each additional tool creates another possible action and another failure path. Begin with only the tools the first useful version genuinely needs.

Writing a goal that cannot be checked

"Help me learn better" is difficult to evaluate. "Create a realistic weekly plan covering these five lessons" gives the agent a visible completion condition.

Treating every instruction as permission

An instruction may describe a task without authorizing every possible action. Write decision boundaries separately so sensitive or irreversible changes remain protected.

Testing only the happy path

The design becomes clearer when you test missing information, conflicting constraints, unavailable tools, and actions that need approval.

Quick knowledge check

1. Why should an agent have a success check?

2. What is the difference between an allowed decision and an approval rule?

3. Why should a first agent begin with a small tool set?

4. In the missed-session example, when should the agent stop and ask the learner?

Answers

1. It gives the system a concrete way to judge whether the task is complete.

2. An allowed decision can be made autonomously; an approval rule identifies a decision that must be handed back to a person.

3. Fewer tools reduce complexity, unintended actions, and the number of cases that must be tested.

4. It should ask when recovering the plan requires changing a protected constraint, such as deleting a topic or extending the deadline.

Download the notes

The one-page notes summarize the canvas, the missed-session example, and today's exercise.

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Series navigation

• Previous: Day 4 - Context and Instructions

• Roadmap: AI Agents in 30 Days

• Next: Day 6 - Development Setup and APIs (link after publication)