Module 1: Foundations of Agentic AI
1.1 What agentic AI means
• Difference between standard AI models and autonomous, goal-driven agents
• Key capabilities: planning, tool use, memory, reflection, multi-step reasoning
• When to use agentic architectures
1.2 Core terminology
• Agents, environments, tasks, objectives
• Policies, actions, observations, feedback
• Reasoning loops, task decomposition, orchestration
1.3 Types of AI agents
• Reactive agents
• Deliberative/planning agents
• Hybrid agents
• Multi-agent systems
1.4 Required background knowledge
• Programming experience (Python recommended)
• Basic machine learning understanding
• Familiarity with APIs and cloud platform
Module 2: Architectures and Framework
2.1 Anatomy of an AI agent
• Reasoning module
• Memory module
• Action and tool interfaces
• Safety, constraints, and guardrails
2.2 Common agent frameworks
• LangChain agents
• AutoGen-style agents
• Open source agent orchestrators (overview and comparison)
2.3 Planning algorithms
• Chain-of-thought reasoning
• Tree-of-thoughts
• Task graphs and workflow planners
• ReAct (reasoning + action) paradigm
2.4 Tool use and integration
• APIs, databases, local tools
• Embedding and vector store integrations
• Retrieval-augmented generation (RAG) as a tool
Module 3: Building an Agent from Scratch
3.1 Problem definition
• How to scope agent tasks
• Hard constraints vs flexible constraints
• Determining success metrics
3.2 Designing prompts and instructions
• System prompts
• Role prompts
• Self-reflection prompts
• Error-recovery instructions
3.3 Creating memory systems
• Short-term and long-term memory
• Episodic vs semantic memory
• Storing and retrieving state
• Memory decay, pruning, context-compression
3.4 Action modules
• API adapters
• Code execution modules
• Web search and retrieval wrappers
• File system and automation tools
3.5 Example: Build a research assistant agent
• Define goal
• Specify reasoning tactics
• Add tools
• Add memory
• Add safety guardrails
Module 4: Multi-Agent System
4.1 Why use multiple agents
• Specialization and division of labor
• Parallelization
• Cross-verification for accuracy
4.2 Roles in multi-agent setups
• Planner agent
• Worker agents
• Judge/critic agents
• Tool-use specialists
4.3 Communication protocols
• Message passing
• Task delegation
• Negotiation and consensus patterns
4.4 Coordination strategies
• Cooperative vs competitive agents
• Supervisory orchestration
• Emergent behavior considerations
4.5 Example: Multi-agent research and coding team
Module 5: Safety, Ethics, and Reliability
5.1 Failure modes in agentic AI
• Infinite loops
• Hallucinations
• Unsafe tool use
• Misaligned objectives
5.2 Guardrail techniques
• Sandbox execution
• Capability restrictions
• Verification agents
• Red-team simulations
5.3 Ethical responsibilities
• Privacy and data handling
• Transparency
• Bias and fairness
• Deployment safety practices
5.4 Human-in-the-loop
• Approval gates
• Oversight workflows
• Checkpointed reasoning
Module 6: Advanced Capabilities
6.1 Self-reflection and self-correction loops
• Critic-assistant patterns
• Replan-when-stuck logic
6.2 Long-horizon planning
• Using planners or decision-making algorithms
• Agent memory graphs
6.3 Autonomous coding agents
• Code writing
• Testing and validation
• Continuous improvement loops
6.4 Agents that operate in real systems
• Email and calendar automation
• CRM and ticketing integration
• Browser-capable agents
• Robotic or IoT interactions
Module 7: Deployment and Scaling
7.1 Packaging an agent
• Configuration files
• Dependency management
• Containerization
7.2 Cloud deployment
• Serverless patterns
• Persistent storage for memory
• Logging and monitoring
7.3 Performance tuning
• Caching
• Parallelism and batching
• Token optimization
7.4 Security considerations
• Secret management
• User authentication
• Permission scopes
Module 8: Capstone Projects
Choose one or more to demonstrate mastery:
• Personal research copilot agent
• Multi-agent data analysis pipeline
• Customer support automation agent
• Autonomous content creation and scheduling agent
• Workflow automation agent for a business process
Each project should include:
• Clear objective and constraints
• Tool-integration plan
• Memory strategy
• Safety and fail-safe design
• Evaluation metrics
Want to learn more? Tonex offers Agentic AI Specialist (AAIS), a 2-day course where participants learn to explain agentic AI patterns and tool-use workflows as well as design compact agent architectures for applied use cases.
Attendees also implement prompts, memory, and planning with measurable KPIs, govern agents with policies, constraints, and human-in-the-loop review, evaluate reliability, cost, and latency with A/B and canary runs, and elevate enterprise cybersecurity by embedding controls into every agent action.
This course is especially beneficial for:
- Product Managers and Solution Architects
- ML/AI Engineers and Data Scientists
- Automation and RPA Engineers
- DevOps / MLOps Practitioners
- Cybersecurity Professionals
- Compliance and Risk Managers
Tonex is the leader in AI certifications, offering more than six dozen courses, including in the Certified GenAI and LLM Cybersecurity Professional area, such as:
Certified AI Compliance Officer (CAICO) certification
Certified AI Electronic Warfare (EW) Analyst (CAIEWS)
Certified GenAI and LLM Cybersecurity Professional (CGLCP) for Professionals
Certified GenAI and LLM Cybersecurity Professional for Data Scientists
Certified GenAl and LLM Cybersecurity Professional for Developers Certification
Additionally, Tonex offers even more specialized AI courses through its Neural Learning Lab (NLL.AI). Check out the certification list here.
For more information, questions, comments, contact us.
