An agentic workflow is a system where AI agents work autonomously to accomplish goals—making decisions, using tools, and adapting to changing conditions without step-by-step human guidance. Unlike simple automations that follow rigid if-then rules, agentic workflows can handle ambiguity and multi-step reasoning.
This tutorial shows you how to design agentic workflows that actually work, using patterns that scale from simple scheduled tasks to complex multi-agent systems.
What makes a workflow "agentic"
Traditional automation: "When I receive an email with an attachment, save it to Dropbox."
Agentic workflow: "Monitor my inbox for client feedback. Summarize what they want changed, update my project tracker, draft a response acknowledging their points, and flag anything that needs my review before sending."
The difference is autonomy and judgment. An agentic workflow can:
Interpret context — Understand what "client feedback" means across different email formats
Make decisions — Determine which items need human review vs. automatic handling
Use tools flexibly — Read emails, update databases, draft text, send notifications
Handle exceptions — Escalate when something doesn't fit expected patterns
The building blocks
Every agentic workflow has four components:
1. Trigger
What starts the workflow. Common triggers:
Scheduled — Run daily at 9am, every Monday, etc.
Event-driven — New email arrives, file uploaded, webhook received
Manual — User requests execution
On Zo Computer, scheduled triggers are built into the Agents feature. You write an instruction, set a schedule, and the agent runs automatically.
2. Context gathering
The agent's first job is understanding the current state. This might mean:
Reading recent emails or messages
Checking a database or spreadsheet
Fetching data from APIs
Reviewing files in a folder
Good agentic workflows gather context before acting. An agent that processes "today's emails" needs to actually read today's emails, not assume what they contain.
3. Decision and action
The core of the workflow: the agent decides what to do and does it. This is where natural language instructions matter. Compare:
Weak instruction: "Process new leads"
Strong instruction: "For each new lead in the CRM: research their company using web search, write a 2-sentence personalized opener, and add it to their contact notes. Skip leads from companies with fewer than 50 employees."
Specific instructions produce consistent results. Vague instructions produce inconsistent results.
4. Output and handoff
How the workflow delivers results:
Direct delivery — Email, SMS, or Slack message to you
File output — Create or update a document
System update — Modify a database, CRM, or project tracker
Handoff to another agent — Chain workflows together
Design patterns
Pattern 1: The daily digest
The simplest agentic workflow. Runs on a schedule, gathers information, summarizes it, delivers to you.
Example: Morning briefing agent
Trigger: Every weekday at 7am
Instruction: Check my Google Calendar for today's meetings. For each meeting, find the attendees' LinkedIn profiles and summarize their recent activity. Check my email for anything urgent. Send me a briefing via SMS with: today's schedule, key attendee context, and any emails needing immediate attention.
This works because it has a clear trigger, bounded context (today only), specific actions (calendar + email + LinkedIn), and defined output (SMS briefing).
Pattern 2: The monitor-and-respond
Continuously (or frequently) checks for conditions and acts when they're met.
Example: Competitive intelligence agent
Trigger: Every 6 hours
Instruction: Search for news mentions of [competitor names]. If there's anything significant (funding, product launch, leadership change, partnership), add a summary to my Research/competitive-intel.md file with the date and source links. Only add genuinely new information—check the file first to avoid duplicates.
The key here is the conditional action: only do something when there's something worth doing.
Pattern 3: The processor
Handles items in a queue or batch.
Example: Content repurposing agent
Trigger: Weekly on Sunday
Instruction: Check my Articles folder for any new saved webpages this week. For each article: generate 3 potential tweet threads that summarize the key insights, save them to Drafts/tweet-threads.md with the source article linked.
Processor workflows scale naturally—they do the same thing to each item, whether there's one or a hundred.
Pattern 4: The multi-agent chain
Complex workflows where multiple agents hand off to each other.
Example: Research → Draft → Review pipeline
Agent 1 (Research): When I save a new topic to Research/topics.txt, research it thoroughly and save findings to Research/[topic].md
Agent 2 (Draft): Daily at noon, check Research/ for any .md files without a corresponding draft in Drafts/. For each, write a blog post draft.
Agent 3 (Review): Daily at 6pm, review any new drafts, check facts against sources, flag issues, and if clean, move to Ready/.
Each agent has a single responsibility and clear inputs/outputs. They coordinate through the file system rather than direct communication.
Common mistakes
Over-automation: Trying to automate judgment calls that actually need human input. Start with clear-cut cases and expand gradually.
Vague instructions: "Handle my email" will produce random results. "Flag emails from clients that mention deadlines, and draft responses for routine questions" is actionable.
No feedback loop: You need to see what your agents are doing. Build in logging—have agents note what they did and why.
Ignoring edge cases: What happens when the API is down? When there's no new data? When something unexpected appears? Good instructions include fallback behavior.
Getting started on Zo
The fastest way to try agentic workflows:
Go to Agents in your Zo
Create an agent with a simple daily digest instruction
Set it to run once daily
Review the output and refine the instruction
Start simple. A useful workflow that runs reliably beats an ambitious one that fails unpredictably. Once your simple workflows work, compose them into more complex systems.
For more on specific implementations, see the tutorials on automating tasks with AI and building AI agents without coding.