Agentic AI Learning Path · New 2026

From the LLM
that responds
to the agent that acts

4 progressive modules · No technical prerequisites

A logical sequence designed to take any employee or manager to a complete understanding of Agentic AI and its governance — starting from LLM basics, through responsible use, and crossing the threshold into the agentic paradigm.

C2 · Understanding AI C3 · Responsible GenAI C4 · Agentic AI ✦ M7 · Architecture & Governance
Start the Learning Path ↓
Agentic AI Learning Path
4
Modules
2 d.
Total Duration
0
Prerequisites
N1→N2
Levels
Target Audience
Business managers, employees, IT and compliance teams seeking to understand Agentic AI without a technical background.
C2 · Understanding AI ½ d.
C3 · Responsible GenAI ½ d.
C4 · Agentic AI ½ d. New
M7 · Agentic Architecture 1 d.
Why this learning path

A sequence designed — not assembled

Each module prepares the next. C2 establishes what an LLM is. C3 teaches how to use it safely. C4 crosses the breakthrough into the agentic paradigm — non-technical, decision and governance oriented. M7 moves to architecture and deployment for advanced teams.

C2
Step 1 · LLM Basics ½ day Prerequisites: none
Understanding AI
What an LLM is, its 3 fundamental limitations, concrete organizational use cases — the foundations before going further.
2h30 – 3h ▾
YOU ARE HERE
C2 ← you are here → C3 → C4 → M7
Detailed Program
0h00 – 0h50 What AI is (and is not)
  • AI in 3 minutes: machine learning, models, training data
  • AI landscape: ML, Deep Learning, NLP, GenAI, Agentic — no code
  • 8 misconceptions debunked: what the media exaggerates, what is real
  • Narrow AI vs General AI: where do we really stand in 2026?
0h50 – 1h40 Hallucinations, bias, real limitations
  • Why AI gets it wrong — the hallucination mechanism explained simply
  • Algorithmic bias: where it comes from, how it manifests, who is exposed
  • Opacity and black box: what we can and cannot know about AI decisions
  • Workshop: identify a hallucination in 5 real AI outputs
1h40 – 2h30 Use cases & EU AI Act
  • AI use case mapping in your sector: scoring, NLP, vision, GenAI
  • EU AI Act: high-risk systems, what it changes for your organization
  • The right posture: neither fear nor naivety — critical questioning
Prerequisites
No technical prerequisites — accessible to all
Deliverables & what you take away
  • 🗺️
    Map of the 4 AI Families
    Visual reference: Supervised ML, LLM/GenAI, Agentic AI, Specialized AI — positioning, uses, maturity.
  • ⚠️
    Hallucination Detection Grid
    5 warning signals to identify a dubious AI output before using it in a professional context.
  • 📜
    EU AI Act Positioning
    Factsheet: which systems are high-risk in your sector, which obligations apply from 2025–2026.
Key concepts established here
Fundamental
LLM
Large Language Model — predicts the next token. The foundation of all GenAI.
Fundamental
Hallucination
False but confident output. Reversible — but dangerous if undetected.
Regulatory
EU AI Act
European regulation in effect 2025. High risk = transparency and governance obligations.
Key for C4
LLM ≠ Agent
An LLM responds. An agent acts. This distinction is the core of C4.
📖
Self-Learning Module Available
Self Learning — Understanding AI
→
▼
understanding everyday use
C3
Step 2 · Everyday Use ½ day Prerequisites: C2 recommended
Responsible GenAI
Using generative AI with discernment: CARE method, 5 risks, Shadow AI, usage policy — how to leverage GenAI without putting the organization at risk.
2h30 – 3h ▾
YOU ARE HERE
C2 ✓ → C3 ← you are here → C4 → M7
Detailed Program
0h00 – 0h50 Shadow AI & CARE method
  • Shadow AI: why 60% of enterprise GenAI usage is undeclared
  • CARE method: Context · Action · Result · Example — structuring an effective prompt
  • 5 iteration techniques: rephrasing, constraint, role, format, negative example
  • Case studies: 3 real business use cases rebuilt with CARE
0h50 – 1h40 5 GenAI risks to know
  • R1 Critical — Exfiltration of confidential data to the LLM operator
  • R2 Critical — Unverified output presented as factual
  • R3 High — RAG on unclassified data, indirect leakage
  • R4 Moderate — Prompt injection from an external document
  • R5 Moderate — Dependency and loss of critical competence
1h40 – 2h30 Usage policy & scope audit
  • The 6 components of an internal AI policy: scope, classification, tools, validation, traceability, sanctions
  • What you should never send to an external LLM — the 3-question rule
  • Final exercise: audit 3 GenAI uses in your scope (approved / needs guardrails / prohibited)
Prerequisites
C2 · Understanding AI (recommended, not mandatory)
Deliverables & what you take away
  • 📝
    Personal Prompt Library
    3 to 5 reusable prompts built during the workshop, structured with CARE, adapted to your business context.
  • 🚦
    GenAI Risk Matrix
    Classification of your GenAI uses in 3 columns: approved · needs guardrails · prohibited.
  • 📜
    Responsible Usage Charter
    Individual AI usage charter template, adaptable to your organization's internal policy.
The 5 risks — criticality levels
💀
R1–R2 Critical — Data leakage + unverified output. The two most frequent and most costly mistakes.
⚠️
R3 High — RAG on unclassified sensitive data. Invisible but real risk as soon as you connect an LLM to internal files.
🎭
R4 Prompt injection — C3 introduces this risk. C4 deepens it in the agentic context where it becomes critical because the agent acts.
📖
Self-Learning Module Available
Self Learning — Responsible GenAI
→
▼
crossing the breakthrough — non-technical bridge
NEW MODULE · 2026
C4
Step 3 · Bridge to the Agentic ½ day ✦ New 2026 Prerequisites: C2 + C3
Introduction to Agentic AI
The LLM → Agent breakthrough explained without code. The 4 gears, use cases by department, the 3 specific risks, the 5 questions to ask — the essential module before M7.
2h30 – 3h ▾
YOU ARE HERE
C2 ✓ → C3 ✓ → C4 ← you are here → M7
Detailed Program
0h00 – 0h45 The LLM → Agent breakthrough
  • Comparison table: mode of action, system access, memory, autonomy, error type, required supervision
  • The operational analogy: consultant who advises vs employee who acts
  • Why your current GenAI policies do not cover agents
  • ORBii 2026 definition: what an AI agent is in practice
0h45 – 1h30 The 4 gears & business impacts
  • Gear 1: Brain — planning, goal decomposition, self-correction
  • Gear 2: Hands — access to APIs, CRM, databases, IS
  • Gear 3: Memory — persistence between sessions, long-term context
  • Gear 4: Cooperation — orchestrator + specialized agents
  • Before/After table by department: IT, Compliance, Legal, Finance, HR, Dev
1h30 – 2h30 Risks, questions & assessment framework
  • R1 Critical — Irreversible action: the agent acts on real systems
  • R2 High — Excessive data access: least privilege principle
  • R3 Moderate — Agentic prompt injection: manipulation via read documents
  • The 5 questions to ask before any agentic deployment in the organization
  • Workshop A: LLM or Agent? — classifying 4 real situations
  • Workshop B: Agentic potential scoring on 15 points (processes in your scope)
Prerequisites for C4
C2 · Understanding AI — LLM basics essential
C3 · Responsible GenAI — risk and AI policy concepts
C4 is the prerequisite for M7 · Agentic Architecture & Governance
Deliverables & what you take away
  • ⚡
    Agentic Potential Scorecard (/15)
    15-point scoring: volume, rules, data, measurable outcome, risk. To hand to the IT department for project evaluation.
  • ❓
    5-Question HITL Checklist
    The 5 essential questions to ask before any agentic deployment — A5 format quick reference.
  • 🎯
    Use Case Qualification Sheet
    Template to describe, evaluate and submit an agentic use case identified in your business scope.
The 3 agentic-specific risks
⚡
R1 Critical — Irreversible action — An LLM hallucination produces a false text (reversible). An agent hallucination triggers a real action: email sent, file modified, order placed.
🔍
R2 High — Excessive data access — An uncontrolled agent can read salaries, confidential notes, personal data — and use them without flagging. Data classification is an absolute prerequisite.
🎭
R3 Agentic prompt injection — Malicious instructions hidden in a document read by the agent, causing it to execute unauthorized actions. An invisible and novel risk.
What C4 does NOT cover → addressed in M7
  • Architecture of the 7 layers of an industrial agentic system
  • 4 patterns: sequential, parallel, loop, orchestrator
  • MCP, RAG, RBAC, observability — the complete technical stack
  • Agent Policy, Agent Catalog, HITL, formalized Audit Trail
  • Deployment roadmap: avoiding the 40% failure rate
📖
Self-Learning Module Available
Self Learning — Introduction to Agentic AI
→
▼
architecture & governance — Advanced level
M7
Step 4 · Architecture & Governance 1 day Advanced Level Prerequisites: C2 + C3 + C4
Agentic AI — Architecture, Governance & Deployment
The 7 layers of an agentic system, the 4 patterns, Agent Policy, MCP Registry, Tech Radar — for IT teams, architects, and decision-makers deploying agents in production.
6h – 7h ▾
MANDATORY PREREQUISITES FOR M7
C2 · Understanding AI — LLM basics and model types
C3 · Responsible GenAI — risks, classification, AI policy
C4 · Introduction to Agentic AI — breakthrough, gears, specific risks
Detailed Program
0h00 – 1h30 Architecture of the 7 layers
  • Layer 1: Objective & constraints — the agent's mission
  • Layer 2: Planning — ReAct decomposition, Chain-of-Thought
  • Layer 3: Memory — short-term (context) vs long-term (vector store)
  • Layer 4: Tools & MCP — Model Context Protocol, permissions
  • Layers 5–7: Orchestration, Observability, Governance
1h30 – 3h00 4 agentic patterns
  • Pattern 1: Sequential — deterministic step pipeline
  • Pattern 2: Parallel — competing specialized agents
  • Pattern 3: Reasoning loop — self-critique, ReAct
  • Pattern 4: Orchestrator + experts — hierarchical multi-agent
  • Pattern selection based on use case and risk level
3h00 – 6h00 Governance & deployment
  • Agent Policy: complete template, legal validation, DPO
  • Agent Catalog: registry, AI Owner, lifecycle
  • HITL architecture: mandatory human validation checkpoints
  • RBAC + Audit Trail + Observability (DORA Art.17)
  • Agentic Tech Radar 2026: LangGraph, CrewAI, AutoGen, MCP, Vertex AI
  • 4-step roadmap: Anchor → Activate → Secure → Scale
Deliverables & what you take away
🏗️
Agentic Architecture Blueprint
7-layer diagram applied to your priority use case, with selected technology stack.
📋
Agent Policy — Complete Template
Complete document ready for governance submission: scope, permissions, HITL, rollback, AI Owner.
📡
Customized Agentic Tech Radar
Key technology positioning based on your existing stack: adopt · evaluate · watch · avoid.
🗺️
12-Month Roadmap
4-step plan with milestones, KPIs, required resources and governance checkpoints to deploy without joining the 40% abandoned.
M7 Target Audience
CIO · Enterprise Architects
Design the agentic infrastructure and data access policy
CDO · Governance Leaders
Define the Agent Policy, registry and supervision processes
IT Managers · Tech Leads
Lead the first pilots and select the appropriate technology stack
📖
Self-Learning Module Available
Self Learning — Agentic AI — Architecture, Governance & Deployment
→
C2
LLM Basics
½ day · Level 1
→
C3
Everyday Use
½ day · Level 1
→
C4
Agentic Bridge
½ day · New ✦
→
M7
Architecture & Gov.
1 day · Advanced Level
Total
2 days
Full Understanding Program Explore C1 · Data Culture & Business + all Level 1 modules
← Hub ORBii.Academy Understanding Program Level 2: Governing →
Tailored · In-Company · 2026
This learning path for your teams?
The Agentic AI learning path C2→C3→C4→M7 is ideal as a kick-off for a business division, an IT team, or an Executive Committee preparing for agentification. Available as 2 intensive days or 4 spaced half-days. Adapted to your sector, your real processes, and your governance challenges.
Request this in-company learning path → Download Program PDF Access a free sample