TL;DR: This article explains how agentic workflows—powered by tools like Google’s Anti-Gravity IDE and the DOE (Directive, Orchestration, Execution) framework—enable businesses to build autonomous AI systems that can perform complex, revenue-generating tasks like lead scraping, email follow-ups, and self-improvement, all without coding experience.
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📹 Watch the Complete Video Tutorial
📺 Title: the n8n killer? AGENTIC WORKFLOWS: Full Beginner’s Guide
⏱️ Duration: 6743
👤 Channel: Nick Saraev
🎯 Topic: N8N Killer Agentic
💡 This comprehensive article is based on the tutorial above. Watch the video for visual demonstrations and detailed explanations.
If you’ve been hearing buzz about “agentic workflows” but thought they were just hype with little real-world value—think again. The technology has matured to the point where agentic workflows are now a practical, reliable, and highly profitable tool for automating business operations. In fact, they’re poised to “run the entire economy,” according to Nick, a seasoned entrepreneur who has scaled two agencies to over $160,000 in combined revenue and now leads Maker School, an AI automation community generating nearly $300,000 in monthly profit.
This comprehensive guide distills every insight, technique, example, and framework from Nick’s full course on agentic workflows—designed not for theoretical exploration, but for immediate, revenue-generating action. You’ll learn how to build autonomous AI systems that scrape leads, enrich data, draft proposals, send follow-up emails, and even self-anneal (self-correct and improve over time)—all from a simple text prompt.
No coding experience? No problem. Using Google’s Anti-Gravity IDE and a novel framework called DOE (Directive, Orchestration, Execution), you’ll create your own “AI employee” that operates with enterprise-grade reliability. Let’s dive in.
Why Agentic Workflows Are the Future of Business Automation
Until recently, the term “agent” was synonymous with AI hype—promising revolutionary change but delivering inconsistent, unreliable results. But today, the underlying technology has advanced enough that agentic workflows are not just viable—they’re essential.
Unlike traditional automation tools (like Make.com or n8n), agentic systems don’t just follow rigid, pre-built paths. They reason, plan, execute, verify, and adapt—mimicking the decision-making of a human employee. And when built correctly, they operate with a level of reliability most assume is impossible with current AI.
Crucially, these aren’t experimental toys. Nick uses them daily in his businesses to:
- Generate qualified leads via outbound cold email
- Run inbound dental marketing campaigns ($2M/year)
- Automate post-call proposal generation
- Save dozens of hours per week in manual labor
But there’s a catch: raw LLMs (Large Language Models) are stochastic—they produce probabilistic, non-deterministic outputs. Ask the same model to scrape LinkedIn leads five times, and you might get five different results—hallucinations, errors, or refusals. For hobbyists, a 50% success rate might be acceptable. For businesses? Even a 1% failure rate can cost 100% of a client relationship.
Introducing the DOE Framework: Directive, Orchestration, Execution
To solve the reliability problem, Nick developed the DOE framework—a three-layer architecture that separates concerns and forces deterministic behavior from inherently probabilistic AI models.
This structure mirrors real-world organizations:
- Directive Layer = Management (SOPs, high-level instructions)
- Orchestration Layer = Mid-level employee (reasoning, task planning)
- Execution Layer = Tools & scripts (deterministic actions)
Layer 1: The Directive Layer (Your AI’s “SOP Manual”)
This layer contains Standard Operating Procedures (SOPs) written in simple, structured text (typically Markdown). These are high-level instructions that define:
- What the task is
- Success criteria
- Guardrails and constraints
Example: Instead of telling an AI “scrape 200 realtors,” your directive might say:
“Assemble a test batch of 25 prospects. If ≥85% are verified realtors in the U.S., proceed to full scrape of 200. If not, adjust filters and retry until criteria are met.”
These directives live in a folder called directives/ in the Anti-Gravity IDE.
Layer 2: The Orchestration Layer (Your AI “Employee”)
This is where the AI agent resides. It doesn’t perform tasks directly. Instead, it:
- Reads the directive
- Chooses an action
- Executes it via the execution layer
- Evaluates the results
This “reasoning loop” allows the agent to function like a mid-level manager—interpreting instructions, making decisions, and delegating work.
Layer 3: The Execution Layer (Deterministic Tooling)
Here’s the secret to reliability: the AI doesn’t do the work—it writes code that does.
Instead of asking an LLM to “scrape LinkedIn,” the orchestrator generates a Python script (scrape.py) that performs the task deterministically. This script lives in the execution/ folder. Why? Because:
- Code is deterministic (same input → same output)
- AI is excellent at generating code
- Execution errors are easier to debug and fix
This approach reduces compounding error rates. A 5-step workflow with 90% success per step would normally have only a 59% overall success rate (0.9⁵ = 0.59). With DOE, each step becomes a verified script—pushing reliability toward 100%.
Real-World Example 1: Autonomous Lead Generation in 15 Minutes
Nick demonstrates a production-grade workflow that scrapes, enriches, and personalizes leads—all triggered by a single message: “Scrape 200 realtors in the United States.”
Step-by-Step Breakdown of the Workflow
| Step | Action | DOE Layer | Outcome |
|---|---|---|---|
| 1 | User inputs: “Scrape 200 realtors in the United States” | Orchestration | Agent reads directive scrape_leads.md |
| 2 | Agent creates test batch of 25 prospects | Execution | Runs scrape.py via Apify |
| 3 | Verifies ≥85% are target realtors | Orchestration | If yes → proceed; if no → adjust filters and retry |
| 4 | Scrapes full 200 leads | Execution | Populates dataset |
| 5 | Enriches 178 → 193 email addresses | Execution | Uses third-party enrichment service |
| 6 | Generates “casual company names” | Execution | Converts “Bal Serzac Group of AB and Co-Realtors” → “Baliserac Group” |
| 7 | Outputs formatted summary + Google Sheet link | Orchestration | User receives deliverable in minutes |
Result: A fully enriched, personalized lead list—ready for cold email—generated with zero manual intervention. Nick built this entire system in under 15 minutes.
Real-World Example 2: Post-Call Proposal & Follow-Up Automation
Another revenue-generating workflow: turning a sales call into a polished proposal and follow-up email—automatically.
How It Works
- User types: “Create a proposal” (no context needed)
- Agent loads directive
create_proposal.md - User provides brief company details (or pastes call transcript)
- Agent:
- Researches company context
- Expands high-level notes (e.g., “wants $45K savings”)
- Generates Pandoc-formatted proposal
- Fills yellow-highlighted fields in a pre-built template
- Sends follow-up email via MCP (Model Context Protocol) tool
The final proposal includes:
- Customized problem statements (“Burning $50K/year on Apollo outreach”)
- Value-driven solutions
- Clear investment breakdown (Month 1, 2, 3+ pricing)
Again, this system was built in ~15 minutes using high-level instructions and the DOE framework.
Why Traditional Automation Tools Fall Short
Platforms like Make.com, Zapier, or n8n are procedural—they follow fixed paths. This creates three major limitations:
| Traditional Automation (n8n, Make.com) | Agentic Workflows (DOE Framework) |
|---|---|
| Rigid, linear workflows | Dynamic, adaptive reasoning |
| Requires manual updates for changes | Self-anneals and improves over time |
| Limited to pre-connected apps | Generates custom code for any task |
| No built-in validation or correction | Verifies outputs against success criteria |
In short: traditional tools automate tasks. Agentic workflows automate thinking.
Building Your First Agentic Workflow: The Anti-Gravity IDE Setup
Nick uses Anti-Gravity, Google’s agentic development platform. The IDE has three panels:
- Left Panel (Explorer): File system with
directives/andexecution/folders - Middle Panel: Agent management tools (e.g., “Edit coding,” “Line code with agent”)
- Right Panel: Chat interface to communicate with your agent
Creating Your First Directive
To build the lead scraper:
- Create
directives/scrape_leads.md - Write high-level instructions:
1. Scrape 25 test leads from U.S. realtors 2. Verify ≥85% are in target market 3. If yes, scrape 200 leads; if no, adjust filters and retry 4. Enrich emails using [Service X] 5. Generate casual company names using [Logic Y] 6. Output to Google Sheet + summary message
- Create
execution/scrape.py(or let the agent generate it)
That’s it. Now you can trigger the entire workflow with one message.
Self-Annealing: The Secret to Long-Term Reliability
“Self-annealing” is the process by which agentic workflows maintain and improve themselves over time. Here’s how it works:
- Agent attempts a task (e.g., scraping leads)
- It evaluates results against success criteria (e.g., 85% target accuracy)
- If criteria aren’t met, it modifies its approach (e.g., changes filters)
- It repeats until successful
- Successful scripts are saved and reused
Over time, your agent builds a library of reliable, battle-tested scripts—becoming more efficient and accurate with each use.
Choosing the Right AI Model for Agentic Workflows
You don’t need the most cutting-edge model. Nick successfully uses:
- Claude Sonnet 4.5 (for proposal generation)
- Gemini 3 (recommended for general tasks)
Key insight: Architecture matters more than model size. A well-structured DOE system with a mid-tier model outperforms a poorly structured system with GPT-4.
Avoiding Costly Business Failures: The Reliability Imperative
In business, AI errors aren’t just inconvenient—they’re catastrophic. Examples:
- Sending the wrong invoice → lost client
- Scraping incorrect leads → wasted ad spend
- Generating inaccurate proposals → damaged credibility
The DOE framework mitigates this by:
- Isolating stochastic reasoning (orchestration) from deterministic execution
- Validating outputs at every stage
- Allowing human review of generated scripts
Tools & Technologies Mentioned
- Anti-Gravity IDE: Google’s agentic development environment
- Apify: Web scraping service used in execution layer
- Pandoc: Document conversion tool for proposal formatting
- MCP (Model Context Protocol): Tool for email integration
- Google Sheets: Output destination for lead data
- Fireflies: Call transcription service (for input data)
Getting Started: Your Action Plan
- Install Anti-Gravity IDE (or equivalent agentic platform)
- Create your first directive (e.g., “Scrape 10 local dentists”)
- Define success criteria (e.g., “Must have phone number and website”)
- Let the agent generate an execution script
- Review, test, and refine
- Scale to revenue-generating workflows (lead gen, proposals, follow-ups)
Why This Isn’t Just Another AI Course
Nick designed this course with one goal: help you make real money. Unlike theoretical tutorials, every example shown:
- Runs in production
- Generates actual revenue
- Saves dozens of hours weekly
- Uses battle-tested frameworks (DOE)
There’s no fluff—just actionable systems you can replicate in 15 minutes.
Final Thoughts: Your AI Business Operating System
By the end of this process, you’ll have an agentic business operating system—a self-improving AI employee that handles your most economically valuable work through a simple text box.
Imagine:
- Typing “Find 50 SaaS companies using HubSpot” → getting a verified lead list in 10 minutes
- Saying “Draft Q3 content calendar” → receiving a fully researched plan
- Requesting “Analyze last month’s ad spend” → getting actionable insights
This isn’t science fiction. It’s the new baseline for high-leverage entrepreneurs. And with the DOE framework, it’s finally reliable enough for real business use.
