The term agentic workflows is being used frequently, but often without clear explanation. To understand what they are, it helps to break AI systems into four progressive layers.
This article explains:
- The difference between AI, AI automation, AI agents, and agentic workflows
- What “self-healing” actually means
- How to build a simple agentic workflow using Google Anti-Gravity
TL;DR
Agentic workflows: Build, run, and repair workflows autonomously.
AI: Generates responses but requires manual input.
AI automation: Connects tools automatically but breaks when conditions change.
AI agents: Add reasoning and memory to automation.
The four layers of AI systems
Understanding agentic workflows requires a structured breakdown.
AI stack layers
Build
Deploy
Create
Workflow Kit (in progress)
AI Stack Guide (coming soon)
Layer 1: Artificial Intelligence
Basic AI systems are trained on large public datasets. You send a prompt, and the system responds.
Example:
- Copy an email from your inbox
- Paste it into an AI tool
- Generate a reply
- Manually send the reply
This works, but everything is manual. No systems are connected automatically.
Limitation: 100% human-operated.
Layer 2: AI Automation
AI automation connects tools together.
Example using platforms like:
- Make.com
- n8n
Instead of copying and pasting emails, a workflow can:
- Detect incoming email
- Send content to an AI model
- Generate a response
- Automatically reply
This removes manual effort.
Limitation:
If anything changes in the workflow, it breaks.
You must debug it manually.
Layer 3: AI Agents
AI agents introduce reasoning and memory.
An AI agent:
- Has a “brain” (reasoning capability)
- Has memory (conversation context)
- Has instructions (system prompts)
- Has tool access (Gmail, Google Sheets, CRM, etc.)
Example:
You tell the agent, “Send an email to…”
The agent:
- Realizes it needs … email
- Searches Google Contacts
- Retrieves the email
- Sends the message
- Confirms completion
This is orchestration, selecting the right tool for the task.
Limitation:
You still have to build and configure the system.
Layer 4: Agentic Workflows
Agentic workflows go one level further.
Instead of:
- You building the automation
- You debugging the automation
An agentic workflow:
- Builds the workflow for you
- Runs the workflow
- Fixes issues
- Updates the underlying logic
This is called self-healing.
What “self-healing” actually means
Consider three scenarios:
AI automation
If step three fails, the workflow stops.
You must fix it.
AI agent
If step three fails, it retries and completes the task.
But the error may happen again tomorrow.
Agentic workflow
If step three fails repeatedly, it modifies the underlying logic so the error does not happen again.
That is the core difference.
Building a simple agentic workflow with Google Anti-Gravity
Google Anti Gravity provides a local agentic development environment where you describe workflows in natural language and the system builds them.
Example use case: scrape coffee shops in Toronto
Goal:
- Scrape Google Maps listings
- Visit each website
- Extract email addresses
- Stop once two valid emails are found
- Save results to CSV
This is tedious manually. It requires:
- Searching listings
- Opening websites
- Finding contact pages
- Extracting email addresses
Instead, the entire process is described in natural language.
How the interface works
Anti-Gravity has three main panes:
- Left pane: File explorer (like Google Drive)
- Center pane: File contents (CSV, scripts, markdown)
- Right pane: Agent (your AI developer)
You act as project lead.
The agent acts as your developer.
The prompt
The workflow instruction:
- Scrape two coffee shops in Toronto
- Extract contact emails
- If no email is found, continue searching
- Save to CSV
- Do not open a browser
The agent:
- Generates Python scripts
- Installs dependencies
- Runs the workflow
- Fixes formatting issues
- Updates logic automatically
No coding knowledge is required.
Self-healing in action
During execution:
- The agent detects malformed email extraction
- Updates parsing logic
- Re-runs the script
- Produces corrected output
Results appear in a CSV file:
- Business name
- Website
If an issue appears again, the system modifies the code permanently.
Making workflows reusable
After building the workflow, it can be saved as a reusable task:
Example task name:
find local businesses and extract contact emails
Now, instead of rebuilding it, you can simply say:
“Run the workflow for coffee shops in Vancouver.”
The agent references the stored blueprint and executes it.
Multiple workflows can be saved and called dynamically.
Recommended stack:
- n8n – Advanced automation (self-hosted power)
- Make – Automation backbone (visual builder)
- Wispr Flow – Voice productivity (3x faster writing)
- Google Anti-Gravity – Agentic workflows
- Modal – Cloud hosting
Why this matters
Agentic workflows reduce:
- Manual automation building
- Manual debugging
- Repeated error correction
- Rigid automation structures
They introduce:
- Natural language control
- Dynamic orchestration
- Self-healing logic
- Reusable workflow blueprints
Limitations
Anti-Gravity is probabilistic.
Different runs may generate slightly different implementations.
Outputs typically converge on the same result, but the internal code may vary.
Final perspective
Agentic workflows represent a shift from:
- Tool configuration
to - System design through language
Instead of building every node manually, you describe the outcome.
The system builds, tests, fixes, and improves itself.
That is the transition from AI tools to AI systems
Try make
FAQ
Do you need coding skills to use Anti-Gravity?
No. The system generates and manages the code.
Is it deterministic?
No. It is probabilistic, like other large language models.
Can multiple workflows exist simultaneously?
Yes. They can be stored, referenced, and reused.