EXPLORE
Find the right problems
Understand agent capabilities, value, constraints, autonomy and adoption decisions.
EXPLORE • IMPLEMENT • SCALE
Agentic AI Academy
Engineering trustworthy AI agents from learning to production.
A professional, open-source learning and engineering platform for building AI agents that are not only capable, but also measurable, secure, observable, governable and production-ready.
By Hendar Mawan, PhD
Hendar Mawan : AI Engineering Leader
AI Engineering · AI Architecture · Agentic AI · Secure AI · Edge AI · AI Governance
01
Why Agentic AI
Agentic AI is a systems-engineering discipline, not a prompting trick.
IMPLEMENT
Build bounded capability
Engineer agents with tools, memory, RAG, orchestration, evaluation and safeguards.
SCALE
Earn production trust
Operate agents with security, observability, governance and enterprise architecture.
02
14-Module Curriculum
From first principles to enterprise deployment and AI leadership.
01
Exploring Agentic AI
Opportunity, autonomy, constraints and organizational value.
02
Python for Agentic AI
The Python skills used directly in agent engineering.
03
LLM & Agent Foundations
Structured outputs, planning, verification and model selection.
04
Single-Agent Engineering
Bounded loops, state, tools, retries and stopping conditions.
05
Tools, APIs & MCP
Function calling, permissions, APIs and tool boundaries.
06
RAG, Knowledge & Memory
Retrieval, embeddings, citations and durable state.
07
Multi-Agent Systems
Delegation, routing, supervision and shared state.
08
Agent Evaluation
Task success, correctness, groundedness, safety, latency and cost.
09
Security & Safety
Least privilege, policy gates, sandboxing, approval and audit.
10
Production Engineering
Deployment, reliability, model routing, CI/CD and operations.
11
Observability & Operations
Tracing, metrics, trajectories, cost and failure analysis.
12
Governance, Risk & Scaling
Risk registers, deployment gates, incidents and adoption.
13
Enterprise Architecture
Identity, model/tool gateways, policy, knowledge and audit.
14
Leadership & Strategy
Portfolio prioritization, ROI/TCO and transformation roadmaps.
03
Learning Architecture
A complete pathway from beginner to professional Agentic AI engineer and architect.
BEGINNER→PYTHON→AI FOUNDATIONS→AGENTS→TOOLS→MEMORY→RAG→MULTI-AGENT→EVALUATION→SECURITY→PRODUCTION→OBSERVABILITY→GOVERNANCE→ARCHITECTURE→AI LEADERSHIP
Complete Beginner · 12–16 weeks
Python Developer · 8–10 weeks
AI/ML Engineer · 8 weeks
AI Product Manager · 6 weeks
AI Architect / Technical Leader · 8 weeks
Researcher · architecture + evaluation focus
04
Engineering Proof
The Academy is designed as code, curriculum and production reference—not just tutorials.
10 Enterprise Case Studies
Customer support, software engineering, cybersecurity, finance, HR, sales, healthcare, manufacturing and public sector.
10 Progressive Projects
From a Python Agent Tool Chest through secure agents, multi-agent teams and an enterprise capstone.
11 Runnable Examples
Framework-neutral examples for loops, tools, memory, RAG, guardrails, evaluation, multi-agent and observability.
Evaluation Framework
Task success, correctness, groundedness, tool accuracy, safety, latency, token usage and estimated cost.
Security by Design
Least privilege, policy enforcement, approvals, sandbox boundaries, budgets, validation and audit logging.
Production Engineering
Docker, CI/CD, tests, health checks, configuration, logging, retries, timeouts and operational controls.
05
Live Engineering Stack
Experience it. Benchmark it. Operate it.
Experience the agent
Hugging Face Playground
Run bounded agent scenarios, inspect RAG behavior, prompt-injection defenses, policy decisions and evaluation signals.
Benchmark the agent
Evaluation & Security Benchmark
48 expert-authored synthetic cases across task success, tool routing, RAG groundedness, prompt injection, unsafe actions, policy, multi-agent coordination and regressions.
Inspect, evaluate and operate
Streamlit Engineering Lab
Run the benchmark, inspect traces and evidence, compare regressions, review security failures, enforce release gates and export evaluation reports.
Public proof chain:
Curriculum → implementation → evaluation & security dataset → interactive playground → engineering lab → release-gate evidence.
06
Flagship Capstone
Responsible Enterprise Research Agent
The capstone integrates planning, policy enforcement, bounded tool use, knowledge retrieval, memory, evaluation, human approval, cost controls, failure recovery and auditability into a single enterprise-oriented reference system.
USER→CONTROLLER→PLANNER→POLICY ENGINE→TOOL GATEWAY→RESEARCH TOOLS→RAG→EVALUATION→HUMAN APPROVAL→REPORT→AUDIT
07
Professional Outcomes
Build portfolio evidence for engineering, architecture, security and AI leadership roles.
Agentic AI EngineerProduction AI EngineerAI ArchitectTrustworthy AI EngineerAI Security EngineerAI Engineering Leader
Open Source · Engineering First
Learn it. Build it. Benchmark it. Evaluate it. Secure it. Operate it.
GitHub is the canonical source for curriculum, runnable code, labs, tests, security patterns and enterprise architecture. Hugging Face provides the live Agentic AI Playground and the public evaluation & security benchmark. The Streamlit Engineering Lab is the operational layer for traces, RAG evidence, security failures, regression comparison, latency/cost signals, release gates and downloadable evaluation reports.