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🤖 AI & Agentic AIBeginner LevelEvidence-Based Skill Profile

Hands-on Mastery in AI Automation

Master practical AI-powered automation workflows from absolute beginner to production scale: event triggers, webhooks, REST APIs, cognitive nodes, and autonomous enterprise systems with your dedicated AI Agent coach.

34 Modules • 5 Production Projects
34 Modules
AI FearFilter Faculty

What You Will Learn

Master the foundations of event-driven automation, comparing conveyor belt workflows against manual human bottlenecks.
Connect third-party SaaS applications via REST APIs, webhooks, cryptographic HMAC-SHA256 signatures, and idempotency deduplication.
Build cognitive AI nodes for intent classification, structured Pydantic extraction, and Map-Reduce document summarization.
Orchestrate complex multi-step Directed Acyclic Graphs (DAGs) with parallel execution, durable state, and conditional branching.
Configure Human-in-the-Loop (HITL) approval gates in Slack and email with automated SLA timeout escalation.
Build production resilience using dead-letter queues (DLQs), exponential backoff retries, and third-party API circuit breakers.
Instrument pipelines with structured JSON logging, correlation IDs, and Prometheus RED telemetry metrics.
Complete 5 production portfolio projects and pass the comprehensive Module 34 Capstone Challenge evaluated by the AI Agent.

Curriculum & Weekly Roadmap

34 Structured Modules

Module 1 — Introduction to AI Automation

  • What is AI Automation?
  • The Evolution from Manual to Autonomous Workflows
  • Core Components of an AI Automation Stack

Module 2 — What Is Automation?

  • Defining Automation in Software Engineering
  • Deterministic vs Probabilistic Processes
  • The Business Value of Automated Operations

Module 3 — AI vs Traditional Automation

  • Rules-Based Automation vs Cognitive AI
  • Handling Unstructured Data at Scale
  • When to Use Code vs When to Use AI

Module 4 — AI Automation Use Cases

  • Customer Support & Ticket Triage Automation
  • Document & Financial Invoice Extraction
  • Marketing Content Pipelines & Personalization

Module 5 — Understanding AI Models and APIs

  • Foundations of Foundation Models & LLMs
  • API Request-Response Anatomy in AI Services
  • Understanding Tokenization and Context Windows

Module 6 — Prompts as Automation Instructions

  • System Instructions as Operational SOPs
  • Structured JSON Output Enforcement
  • Few-Shot Prompting for Workflow Consistency

Module 7 — Workflow Thinking

  • Deconstructing Complex Tasks into Micro-Steps
  • Mapping Data Lineage and State Progression
  • Identifying Bottlenecks and Failure Points

Module 8 — Triggers and Actions

  • Understanding Event Triggers in Automations
  • Understanding Workflow Actions & Side Effects
  • Event-Driven Architecture Fundamentals

Module 9 — Inputs, Outputs and Variables

  • Payload Structure and Variable Scoping
  • Data Types and Schema Validation
  • Environment Variables and Secrets Management

Module 10 — Building Your First AI Automation

  • Step-by-Step Architecture of a Starter Pipeline
  • Configuring the AI Reasoning Step
  • Dry-Run Testing and Payload Inspection

Module 11 — Connecting AI with Applications

  • Integration Protocols: REST, GraphQL, and SDKs
  • Authentication Types: API Keys vs OAuth2
  • Handling Rate Limits with Exponential Backoff

Module 12 — API Basics for AI Automation

  • HTTP Request Methods in Automation Pipelines
  • HTTP Status Codes and Error Recognition
  • Headers, Query Parameters, and URL Encoding

Module 13 — Webhooks and Events

  • Webhooks: Reverse APIs for Real-Time Events
  • Verifying Webhook Signatures (HMAC)
  • Idempotency and Duplicate Delivery Protection

Module 14 — Data Transformation in Automations

  • Data Mapping Between Mismatched Schemas
  • Filtering, Sorting, and Deduplicating Lists
  • Parsing JSON, CSV, and XML Formats

Module 15 — Conditional Logic and Decision Making

  • IF/ELSE Branching in Automation Workflows
  • Multi-Way Switch Routers & Guard Conditions
  • Fallback Paths and Default Handlers

Module 16 — AI-Powered Classification

  • Automated Intent Classification
  • Multi-Label Tagging and Priority Scoring
  • Zero-Shot vs Few-Shot Classification Accuracy

Module 17 — AI-Powered Extraction

  • Named Entity and Key-Value Extraction
  • Handling Missing and Ambiguous Data
  • Validating Extracted Data with Regex & Schemas

Module 18 — AI-Powered Summarization

  • Extractive vs Abstractive Summarization
  • Map-Reduce Summarization for Long Documents
  • Tailoring Summary Formats for Different Personas

Module 19 — AI-Powered Content Generation

  • Templated Generation with Variable Injection
  • Brand Voice, Tone, and Style Enforcement
  • Automated Quality Checks & Fact Verification

Module 20 — AI-Powered Email and Communication Automation

  • Automated Inbound Email Processing Pipeline
  • Contextual Customer History Retrieval
  • Safe Sending: Drafts vs Auto-Send Guards

Module 21 — Document and File Automation

  • PDF Parsing, OCR, and Table Extraction
  • Automated Document Generation and PDF Assembly
  • Cloud File Storage & Metadata Indexing

Module 22 — Spreadsheet and Database Automation

  • Automating Google Sheets and Excel Workflows
  • Relational Database CRUD via Automation Nodes
  • Bidirectional Synchronization Patterns

Module 23 — Multi-Step AI Workflows

  • Designing Chained Multi-Step Automation DAGs
  • State Management Across Long-Running Workflows
  • Data Aggregation and Fan-In / Fan-Out Patterns

Module 24 — AI Agents vs AI Automation

  • Static Workflows vs Dynamic Autonomous Agents
  • When to Choose Workflows over Autonomous Agents
  • Hybrid Architecture: Workflows with Agentic Nodes

Module 25 — Building Agentic Workflows

  • The ReAct (Reason + Act) Loop in Automations
  • Tool Definition and Schema Binding for Agents
  • Managing Termination Conditions and Loop Limits

Module 26 — Human-in-the-Loop Automation

  • Human-in-the-Loop (HITL) Architecture Patterns
  • Building Approval Workflows (Slack, Email, UI)
  • Handling Timeouts and Escalation Policies

Module 27 — Error Handling and Retry Logic

  • Classifying Transient vs Permanent Errors
  • Dead-Letter Queues (DLQ) and Error Triaging
  • Compensation Transactions and Rollback Logic

Module 28 — Automation Security and Privacy

  • The Principle of Least Privilege in API Scopes
  • Defending Against Prompt Injection in Automation Pipelines
  • Data Privacy, PII Masking, and GDPR Compliance

Module 29 — Testing and Debugging AI Automations

  • Unit Testing Deterministic Workflow Nodes
  • Mocking External APIs and Webhook Payloads
  • Regression Testing AI Prompts Across Versions

Module 30 — Monitoring and Logging

  • Structured Logging and Correlation IDs
  • Workflow Health Metrics: SLA, Latency, and Error Rate
  • Alerting and Anomaly Detection in High-Volume Pipelines

Module 31 — Cost and Token Management

  • Calculating and Optimizing LLM Automation Costs
  • Model Tiering: Small vs Large Models for Micro-Tasks
  • Semantic Caching of Repeated Prompts and Queries

Module 32 — Reliability and Guardrails

  • Implementing Input and Output Guardrails
  • Circuit Breakers for Fragile Third-Party APIs
  • Graceful Degradation and Fallback Strategies

Module 33 — Production-Ready AI Automation Architecture

  • Enterprise Automation Blueprint & Clean Decoupling
  • CI/CD Deployment Pipelines for Automation Workflows
  • Governance, Auditing, and Compliance in Enterprise AI

Module 34 — Build a Real-World AI Automation System

  • Capstone Architecture: Autonomous Enterprise Automation System
  • Implementing Ingestion, Verification, and Routing Nodes
  • Integrating Cognitive AI Extraction and Structured Data Schema
  • Configuring Human-in-the-Loop Approval and Action Dispatchers
  • Deploying Observability, Prometheus Metrics, and DLQ Resiliency
  • Production Readiness Review and Skill Certification Benchmark

Who This Course Is For

Aspiring Automation Engineers, AI Builders, Operations Specialists, Backend Developers, and CS Students aiming to design, build, automate, test, and deploy resilient AI automation pipelines from scratch.

Key Skills Developed:

Workflow Architecture, Event Triggers & ActionsAPI Integrations, Webhooks & Data TransformationCognitive AI Nodes (Classify, Extract, Summarize)Agentic Workflows, Human-in-the-Loop & GuardrailsAutonomous Multi-System Enterprise Pipeline

Course Faculty & Development

AI FearFilter Faculty

AI & Automation Engineering Team

AI FearFilter Academy

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