Agentic AI Foundations

Concept & Motivation

The AI conversation has moved beyond chatbots. The next wave — already here — is agentic: AI systems that don’t just answer questions but take actions, use tools, reason through multi-step problems, and operate with varying degrees of autonomy.

But the gap between the demos and the reality is enormous. Most organizations are not ready — not because the technology isn’t there, but because they don’t understand what “agentic” actually means, where it works, and where it creates expensive failures.

This course closes that gap. Not by teaching you to build agents, but by giving you the conceptual foundation to evaluate, commission, and govern them.

What You’ll Learn

What makes an agent “agentic” — the reasoning loop (observe → plan → act → reflect), tool use, memory, and autonomy levels. The technical architecture explained for decision-makers
The agent landscape — Claude (Anthropic), GPT (OpenAI), Gemini (Google), open-source (LLaMA, Mistral), and local models (Ollama). What differentiates them and when it matters
Agent frameworks — Claude Code, LangChain, CrewAI, AutoGen, and custom implementations. What each solves, their trade-offs, and how to evaluate which fits your use case
Where agents create value — document processing, code generation, research synthesis, customer service escalation, compliance checking, data pipeline orchestration. Real patterns, not hypotheticals
Where agents fail — hallucination in action chains, tool misuse, infinite loops, confidently wrong reasoning. The failure modes that demos never show
Autonomy spectrum — from fully supervised (human approves every action) to fully autonomous (agent runs overnight). How to choose the right level for your risk tolerance
Build vs. buy — when to use off-the-shelf agent platforms, when to build custom, and when to wait

Who This Is For

Executives and directors evaluating AI strategy and vendor proposals
Innovation and digital transformation leads scoping agentic AI pilots
IT and architecture teams assessing integration requirements and security implications
Product managers exploring agent-powered features in their products
Anyone making decisions about AI adoption who needs substance beyond the buzzwords

No technical background required. The course teaches the concepts at the level where you can make informed decisions and ask the right questions.

Format & Duration

1.5-day seminar (on-site or hybrid). Day 1 covers the conceptual foundations with live demonstrations of agent systems. Half-day 2 is a structured evaluation workshop: participants assess an agentic AI proposal for their own organization using a readiness scorecard.

What Makes This Course Different

Most “AI agent” training either teaches you to code agents (wrong audience) or gives you marketing-level overviews (too shallow to be useful). This course sits in the gap: deep enough to understand the technical architecture, practical enough to make business decisions.

The examples come from real consulting engagements — not sanitized case studies. You’ll see agents that worked brilliantly and agents that failed expensively, with honest analysis of why.

Q & A

Learn more about what we do

A chatbot answers questions. An agent takes actions. An AI agent can reason about a goal, break it into steps, use tools (databases, APIs, file systems), verify its own output, and iterate until the task is done. This course teaches you to understand and evaluate that difference — and to recognize when a vendor is selling a chatbot dressed up as an agent.

No. AI agents are built on top of large language models, but you don't need to understand how those models are trained. What you need is a clear mental model of what agents can and cannot do, how they reason, and where they fail. That's exactly what this course provides.

Especially then. This course helps you skip the hype phase and start with a clear understanding of what's real, what's coming, and what's marketing. You'll leave with a framework for evaluating where agentic AI fits in your organization — or whether it does at all right now.

Yes. We demonstrate live agent systems — including Claude's agentic framework and our own AgentForge platform — so you see real reasoning chains, tool use, and failure modes. No coding required; you observe, evaluate, and critique.