How to Design an Agentic Workflow

If you’re searching for agentic AI vs generative AI, you’re usually trying to answer a practical question: how do I get an AI system to actually do multi-step work on my behalf, reliably, using tools and real-world context?

On Zo Computer, an agentic workflow is a way to structure that work so your AI can:

• follow a clear objective

• use tools (web browsing, files, integrations)

• keep state (so it doesn’t forget what happened last run)

• run on a schedule (so it keeps working)

• fail safely (so it doesn’t spam you or break things)

This tutorial walks through a design process you can reuse for almost any automation.

Agentic AI vs generative AI (in one minute)

Generative AI is the “one-shot” style: you give a prompt, it gives an answer. It’s great for drafting text, summarizing, brainstorming, and doing isolated reasoning.

Agentic AI is “goal + loop”: you give an objective, and the system takes actions to reach that objective—calling tools, checking intermediate results, and deciding what to do next.

A clean rule of thumb:

• If you can solve it with one prompt, it’s generative.

• If it needs a sequence of actions (often with state across days), it’s agentic.

Zo’s Agents are built for that second category. (Docs: https://docs.zocomputer.com/agents)

Step 0: Pick a workflow that has a crisp definition of “done”

Bad agent goals:

• “help me with my life”

• “improve my productivity”

Good agent goals:

• “Every weekday at 8am, email me a 5-bullet briefing: today’s meetings, top 3 emails to reply to, and 2 risks.”

• “Every hour, check a specific webpage for a specific change, and text me only if it changed.”

• “Every Sunday, summarize my week’s Git commits and draft a changelog in a file.”

The sharper the “done” condition, the less your agent will drift.

Step 1: Decide the input(s) and output(s)

Write this down explicitly before you build anything.

Inputs (examples):

• one or more URLs to read

• a folder to scan

• your calendar for the next 24 hours

• a spreadsheet to update

Outputs (examples):

• a file in your workspace (best for durable state)

• an email digest (best for things you’ll reference)

• an SMS alert (best when you need to react)

If you’re unsure, default to “write a file” first. It makes testing easier.

Step 2: Make the agent’s actions deterministic

An agentic workflow should read like an operating procedure.

Instead of:

• “monitor this site and tell me what’s new”

Write:

1. Open the URL.

2. Extract only the section under heading “Release notes”.

3. Compare it to the last saved value in release_notes_last.txt.

4. If it changed, save the new value and send me a 5-bullet summary with quotes.

5. If it did not change, do nothing.

This is the difference between “AI as a vibe” and “AI as a system.”

For web workflows, Zo has two browsing tools:

• Fast extraction: https://docs.zocomputer.com/tools/read-webpage

• Full browser rendering + screenshot: https://docs.zocomputer.com/tools/view-webpage

Step 3: Add state (memory) using files

Most “agents that don’t work” fail because they have no memory.

On Zo, you usually want two files:

• a state file: what happened last time (timestamps, last seen values, counters)

• a working output: the latest report/draft

Example state layout:

• state.json

• latest

State makes your agent:

• idempotent (running twice doesn’t double-notify)

• stable across restarts

• debuggable (you can inspect what it “believes”)

Step 4: Build guardrails (so it fails safely)

Guardrails are what make an agent safe to run unattended.

Use these patterns:

• Notification thresholds: “Only text me if there’s a real change.”

• Rate limits: “Never send more than 1 SMS per run; if there are 10 changes, summarize.”

• Fallback behavior: “If a webpage fails to load, log the error to a file and try again next run.”

• No destructive actions by default: start read-only; only later add “delete/modify” steps.

If you do need the agent to change things (delete emails, move files, create calendar events), require a confirmation step (like writing a proposed plan to a file) before any destructive action.

Step 5: Test as a one-off run, then schedule it

A good workflow development loop:

1. Run it once and inspect outputs.

2. Tighten instructions until it’s boring.

3. Only then schedule it.

If you want an end-to-end example you can copy, see:

• https://www.zo.computer/tutorials/how-to-automate-tasks-with-ai

• https://www.zo.computer/tutorials/how-to-build-ai-agents-without-coding

A complete example: “change monitor” agent

Here’s a concrete spec you can paste into an Agent instruction and adapt:

Every hour:

1. Read https://example.com/release-notes and extract only the release notes text.

2. Read release_notes_last.txt if it exists.

3. If the release notes are unchanged, do nothing.

4. If they changed:

   • overwrite release_notes_last.txt with the new text

   • write release_notes_diff summarizing what changed (max 10 bullets, include 2–4 short quotes)

   • send me a short SMS with the top 3 changes.

5. If anything errors, append a one-line error with timestamp to errors.log and exit.

The key is that every run is safe and repeatable.

Summary

Designing an agentic workflow is mostly about structure:

• define “done”

• make inputs/outputs explicit

• write deterministic steps

• store state in files

• add guardrails

• test once, then schedule

Once you have one workflow working, it becomes a template. Most of the time, you’re just swapping in different inputs (URLs/files/apps) and different outputs (file/email/SMS).