Agentic Engineering Academy
A sleek educational platform for video-led lessons and practical labs on agentic engineering.
Train judgment, not just output.
The course is organized like a serious program rather than a content feed. Each chapter introduces a durable operating model, then routes the learner into a lab where the concept becomes a document, prompt, or safety policy.
Clear progression, visible lesson units, and practical application after every idea.
Mode
Content-first
Delivery
Video + text
Practice
Prompt workbench
Foundations of Agentic Engineering
Build a durable mental model for agent loops, orchestration, context design, and execution boundaries.
Agentic Systems 101
Understand the moving parts of an agentic system and why control surfaces matter more than model hype.
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From Context Windows to Context Systems
Move beyond token limits and design context as a curated system with retrieval, compression, and intent cues.
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Review Loops and Guardrails
Learn where to add human review, machine checks, and explicit stop points before an agent causes expensive mistakes.
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Setting Up Your Own Assistant with OpenClaw
Go from zero to a working personal AI assistant using OpenClaw — configured to your needs, running on your terms.
What Is OpenClaw and Why It Matters
Understand what OpenClaw is, how it differs from chatbots, and why having your own configurable assistant changes your workflow.
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Installing and Configuring Your First Assistant
Walk through installing OpenClaw, connecting it to a model provider, and running your first assisted task.
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Customizing Behavior with System Prompts and Tools
Shape how your assistant thinks and acts by writing system prompts, connecting tools, and setting behavioral boundaries.
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Using Coding Assistants Efficiently
Get more from coding assistants by mastering skills, feedback loops, and the habits that separate productive users from frustrated ones.
Working with Coding Assistants: Beyond Autocomplete
Shift your mental model from autocomplete to collaboration — and learn what coding assistants are actually good at.
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Skills, Hooks, and Extending Your Assistant
Learn how skills and hooks turn a generic coding assistant into a specialized tool that fits your team's workflow.
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Feedback Loops That Make Your Assistant Better Over Time
Build systematic feedback loops so your coding assistant improves with use instead of repeating the same mistakes.
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Architecture for Agentic Engineers
Design systems that agents can work within — clean interfaces, predictable state, and separation of concerns that scales.
Designing for Agents: Separation of Concerns
Structure your codebase so agents can reason about isolated pieces instead of needing to understand everything at once.
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Tool Design and API Surfaces Agents Can Use
Build APIs and tool interfaces that agents can discover, understand, and call correctly — without guessing.
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State Management and Orchestration Patterns
Manage state in systems where humans and agents both operate — without losing track of who changed what and why.
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Securely Launching to the Public
Ship agentic applications to real users without exposing your system to prompt injection, data leaks, or runaway costs.
Threat Modeling for Agentic Applications
Identify the unique attack surfaces of agentic applications — from prompt injection to data exfiltration — before they become incidents.
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Authentication, Authorization, and Access Control
Design access control that works when agents act on behalf of users — where identity, permissions, and delegation all get complicated.
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Monitoring, Incident Response, and Rollback
Build the operational infrastructure to detect problems, respond to incidents, and roll back agent actions when things go wrong.
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Scaling
Take agentic systems from prototype to production — managing cost, reliability, and complexity as usage grows.
From Prototype to Production Workloads
Identify what breaks when agentic systems move from demo to real traffic — and how to prepare before it happens.
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Cost Management and Optimization
Keep agentic system costs predictable and sustainable without sacrificing quality — through smart model selection, caching, and token budgets.
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Multi-Agent Systems and Team Coordination
Design systems where multiple agents work together on complex tasks — with clear roles, communication protocols, and conflict resolution.
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