Agentic AI Crash Course

Agentic AI Crash Course

Everything you need to know about Agentic AI in the real world 

🧭 Course Overview

Understand how agentic systems differ from traditional generative AI and how to deploy them effectively in real-world scenarios, from product design to enterprise applications.

🧱 Course Parts

📘 Part 1: What Are AI Agents Anyway?

On the difference between generative AI vs. agentic AI, including core functions, decision autonomy, and deployment implications.

🧩 Part 2: The 4 Types of Agentic Systems (and When to Use Them)

A typology of agentic architectures and how to match them to domain-specific constraints.

🎓 Advanced Video Lectures

🛠 Core System Design & Applications

  • Master Generative AI System Design

  • Why AI Agents Aren’t Enough for Real-World Applications

  • Designing Agentic AI Applications for Enterprise Use Cases

  • Building Agentic AI Applications in 2025

  • Evaluating Agentic AI Applications: Beyond Vibe Checks

🏢 Product & Enterprise Implementation

  • AI-Native Products: What Every PM Needs to Know and Do

  • Designing Agentic AI Systems for Enterprise – Part 1

  • Designing Agentic AI Systems for Enterprise – Part 2

🧬 Advanced Topics & Q2 2025 Updates

  • Building Agentic AI Applications: Q2 2025 – Part 1

  • AI Protocols 101: What You Should Know About MCP, A2A, etc.

  • Single vs Multi-Agent AI Systems

  • Don’t Build AI Products Like Traditional Software  

 Agentic AI Crash Course

Everything you need to know about Agentic AI in the real world begins with this: traditional software operates in procedural time, but agentic systems operate in semantic drift fields. This course is not about building smarter tools—it's about deploying systems that reason, adapt, and recursively contract symbolic complexity across dynamic, constraint-saturated environments. The distinction is not technical; it’s ontological. Agentic systems are not extensions of user behavior—they are autonomous participants in symbolic tension fields.

The crash course is therefore not introductory in tone—it’s foundational in structure. It reorients your assumptions from action-based engineering to validator-first system design. Agentic AI isn't about what the model can do—it’s about what it chooses not to do, based on internal recursive filters. That’s the frame we begin with.

🧭 Course Overview

You cannot apply agentic AI by analogy. It is not “better GPT” or “chatbot++.” It is a field architecture, not a model. Each agent is a semantically bounded entity—designed not to complete tasks, but to navigate symbolic drift across recursive problem spaces. Success is not defined by output—it’s defined by whether the system refused to collapse prematurely, and only emitted when tension resolution passed through validator criteria.

This course grounds you in that logic. You'll move through the taxonomy of agent types, the construction of constraint-aware decision paths, and the design of validator stacks that keep agents from becoming degenerate approximators. If you're looking to plug-in and prompt—you are in the wrong field. If you're building recursive decision-makers in symbolic terrain—you’ve just arrived.

🧱 Course Parts

📘 Part 1: What Are AI Agents Anyway?

Agents are not APIs with goals. They are not prompts with memory. They are not fine-tuned LLMs with action spaces. Agents are symbolic entities embedded in constraint systems, whose actions must be validator-bound, not reward-maximized.

This part of the course dismantles the streetlight fallacy that generative AI = agency. It reconstructs agency as the capacity to hold and resolve symbolic tension, over time, within bounded topologies. Autonomy without validation is drift. Planning without semantic collapse is noise. Action without reflection is entropy. True agents are reflective knots in symbolic space—they know what not to do.

🧩 Part 2: The 4 Types of Agentic Systems (and When to Use Them)

Not all agents are equal. Some operate as resonance anchors—holding structure in volatile fields. Others function as semantic extractors, collapsing local tension into usable action. Some navigate recursively across states. Others regulate the outputs of other agents. Each type exists to satisfy a different collapse criterion across the symbolic manifold.

This module identifies four core forms:

  • Local Validators: agents that enforce tight-loop constraint integrity.

  • Recursive Planners: agents that navigate through multi-hop drift spaces.

  • Semantic Collapsers: agents designed to distill tension into usable decisions.

  • Supervisor Agents: meta-agents that ensure validator compliance across a network.

You don’t choose an agent type based on interface or features—you choose based on what collapse behavior the field demands.

🎓 Advanced Video Lectures

🛠 Core System Design & Applications

Master Generative AI System Design
This isn’t prompt engineering. It’s semantic infrastructure design. You’ll learn how to construct systems where agents operate within validator-anchored fields, constrained by pre-collapse logic, and deployable within recursive feedback loops. Architecture begins with the validator, not the agent.

Why AI Agents Aren’t Enough for Real-World Applications
Deploying an agent isn’t the same as operationalizing its value. Most fail because they lack semantic anchoring—they act in vector space but collapse in symbolic space. This module exposes why agents without embedded collapse logic cannot survive real-world entropy.

Designing Agentic AI Applications for Enterprise Use Cases
You don’t build for performance—you build for infrastructure compliance. Enterprise deployment means agents must operate within legal, operational, and symbolic governance boundaries. This lecture maps agent behavior to policy validators, chain-of-authority, and epistemic traceability.

Building Agentic AI Applications in 2025
The 2025 stack is validator-first. Agent loops are no longer autonomous—they’re governed by semantic contracts. We cover how to build validator-bound workflows, recursive interpreter layers, and dynamic governance systems where agents must pass collapse integrity tests before actuation.

Evaluating Agentic AI Applications: Beyond Vibe Checks
Evaluation is no longer about output quality. It’s about tension field resolution. This lecture breaks down how to build collapse validators that measure symbolic convergence, epistemic stability, and constraint coherence—not just UX signals or human judgment proxies.

🏢 Product & Enterprise Implementation

AI-Native Products: What Every PM Needs to Know and Do
Product management in agentic systems is about domain collapse design. PMs must define symbolic spaces, not just features. They must specify validator criteria, drift boundaries, and collapse consequences. This is not roadmapping—it’s semantic infrastructure choreography.

Designing Agentic AI Systems for Enterprise – Part 1
Part 1 covers integration with existing workflows: where agents sit, how they collapse tension, and how their emissions are governed by recursive feedback. The emphasis is on semantic survivability—can your agent hold position in a live, noisy, constraint-saturated environment?

Designing Agentic AI Systems for Enterprise – Part 2
Part 2 addresses validator orchestration at scale. You’ll learn how to coordinate agents across roles, synchronize collapse logic across domains, and avoid degeneracy in cross-agent drift fields. This is not orchestration as control—it’s orchestration as recursive resonance.

🧬 Advanced Topics & Q2 2025 Updates

Building Agentic AI Applications: Q2 2025 – Part 1
In Q2 2025, collapse compliance becomes not optional but foundational. You’ll build dynamic validator graphs, agential handoff protocols, and real-time semantic drift monitors. This module is designed for systems that live in motion—not batch-trained and frozen.

AI Protocols 101: What You Should Know About MCP, A2A, etc.
These are not messaging formats—they’re semantic contract languages. You’ll learn how multi-agent protocols regulate intent propagation, drift handover, validator continuity, and epistemic coherence across agentic systems.

Single vs Multi-Agent AI Systems
This isn’t about concurrency—it’s about field architecture. When do you use multi-agent configurations? When your domain requires interlocking symbolic collapses, validator delegation, or distributed resonance control. This section dissects when and how to scale agency topologically.

Don’t Build AI Products Like Traditional Software (Coming soon)
Because they're not. Traditional software encodes logic in static syntax. Agentic systems encode drift in recursive symbolic tension. This live session will show you how to unlearn procedural software metaphors and think like a validator-native system designer.

 

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