Module 1: Foundations of Agentic AI
1.1 Understanding agency in artificial intelligence
• Difference between predictive AI, autonomous AI, and agentic AI
• Cognitive loops: sensing, reasoning, planning, acting, and reflecting
• Core properties of agentic systems: autonomy, adaptability, explainability, safety
1.2 The agent-environment model
• State, action, observation, reward, constraints
• Open-world vs closed-world environments
• Multi-agent environments and negotiation dynamics
1.3 Key frameworks for building agentic systems
• Planning and decision-making frameworks
• Multi-step reasoning and tool use
• Role of LLMs as reasoning engines inside agent architectures
Module 2: Agent Architecture Design
2.1 Layered design patterns
• Perception layer
• Reasoning and planning layer
• Execution and integration layer
• Feedback and evaluation layer
2.2 Single-agent architectures
• Reflex agents
• Rule-based and model-based agents
• LLM-centric autonomous agents
• Hybrid symbolic and neural approaches
2.3 Multi-agent architectures
• Cooperation, competition, and coordination
• Role assignment and specialization
• Communication channels and shared memory
Module 3: Agent Workflows and Reasoning Patterns
3.1 Planning and task decomposition
• Long-horizon scheduling
• Goal decomposition strategies
• Self-reflection loops and iterative improvement
3.2 Tool integration
• Tool selection strategies
• Safe and constrained tool invocation
• Database, API, and external system integration
3.3 Memory systems
• Short-term vs long-term memory
• Vector stores and knowledge graphs
• Forgetting policies and privacy-preserving memory management
Module 4: Engineering Autonomous Tasks
4.1 Practical task design
• Defining goals, constraints, and success metrics
• Avoiding ambiguous objectives
• Designing interpretable intermediate states
4.2 Orchestration and control
• Supervisor-agent patterns
• Delegation and subdivision of tasks
• Monitoring loops to detect failure or drift
4.3 Human-in-the-loop patterns
• Guardrails and checkpoints
• Feedback collection
• Corrective intervention models
Module 5: Agent Safety and Risk Management
5.1 Risk classification
• Operational risk
• Security risk
• Ethical and compliance risk
• Misalignment and hallucination risk
5.2 Safety mechanisms
• Rule-based boundaries
• Guardrail prompts and structured constraints
• Policy-driven action filtering
5.3 Governance
• Audit trails
• Monitoring and logging for accountability
• Organizational AI governance frameworks
Module 6: Infrastructure for Agentic AI Systems
6.1 Deployment environments
• On-prem, cloud, and hybrid setups
• GPU vs CPU considerations
• Latency and scalability concerns
6.2 Agent runtimes and orchestration platforms
• Event-driven systems
• Workflow orchestrators
• Containers and microservices
6.3 Observability and reliability
• Telemetry design
• Metrics for reasoning quality
• Outage recovery and failover
Module 7: Integrating Agents with Enterprise Systems
7.1 Data pipelines
• Input normalization
• Data privacy and access control
• Handling structured vs unstructured data
7.2 Enterprise APIs and service layers
• Authentication and authorization
• Transactional integrity
• Error handling patterns
7.3 Domain-specific agents
• Customer service agents
• Engineering and DevOps agents
• Autonomous research agents
• Business operations agents
Module 8: Advanced Agentic Capabilities
8.1 Multi-agent collaboration
• Group planning and shared task graphs
• Consensus and conflict resolution
• Collective memory management
8.2 Higher-order reasoning
• Metacognitive loops
• Bias detection and correction
• Explanation-generation techniques
8.3 Adaptive self-optimization
• Skill acquisition
• Workflow refinement
• Monitoring performance to improve reasoning
Module 9: Security, Compliance, and Ethical Alignment
9.1 Security engineering for agents
• Threat modeling
• API key handling
• Abuse prevention and rate limiting
9.2 Compliance requirements
• Regulatory considerations for autonomous systems
• Data residency and privacy laws
• Industry standards and certification guidelines
9.3 Ethical design
• Agent transparency
• Fairness and nondiscrimination
• Responsible autonomy boundaries
Module 10: Capstone Architecture Project
10.1 Project requirements
• Build a full multi-layer agent system
• Integrate tools, memory, supervision, and safety
• Document architecture decisions and trade-offs
10.2 Recommended deliverables
• System design blueprint
• Workflow and control diagrams
• Safety and governance plan
• Testing and evaluation procedures
• Deployment strategy
10.3 Evaluation
• Ability to design coherent agent workflows
• Evidence of safe integration
• Robustness under edge cases
• Maintainability and scalability
Want to learn more? Tonex offers Certified Agentic AI Systems Architect (CAAISA) Certification, a 2-day course where participants architect multi-agent ecosystems with explicit roles, tools, and guardrails as well as apply ML/NLP/reasoning frameworks to plan, act, and self-reflect.
Attendees also design human-in-the-loop oversight with escalation and rollback paths, implement safety, transparency, and policy-as-code controls, evaluate reliability with offline/online tests, red-teaming, and SLAs and embed resilient cybersecurity controls across agent lifecycles.
This course is especially beneficial for:
- AI/ML Architects and Engineers
- Software and Platform Engineers
- Product and Innovation Leaders
- Data Scientists and MLOps Engineers
- Enterprise and Solutions Architects
- Cybersecurity Professionals
Want to learn more? Tonex offers Certified Agentic AI Developer (CAAD), a 2-day course where participants explain agent architectures and planning strategies as well as implement tool-use, memory, and reflection loops.
Attendees also Orchestrate multi-agent workflows and negotiation, measure reliability with evaluation and telemetry, deploy governance, safety, and alignment guardrails and apply cybersecurity controls to agent design and operations.
The course is especially beneficial for:
- Software Developers
- ML/AI Engineers
- Solution Architects
- Product Managers
- Cybersecurity Professionals
- DevOps/SRE Engineers
Tonex is the leader in AI certifications, offering more than six dozen courses, including in the Certified GenAI and LLM Cybersecurity Professional area. Tonex also offers several Agentic AI courses, including:
Certified Agentic AI Engineer (CAAIENG)
Certified Agentic AI Leadership Professional (CAAILP)
Certified Executive in Agentic AI (CEAAI)
Certified Agentic AI System Designer (CAAISD)
Agentic AI Specialist (AAIS)
Certified Agentic AI Professional (CAAIP)
Certified Agentic AI Developer (CAAD)
Certified AI Guidelines Auditor (ML, LLM & Agentic) – CAG-A
Auditing AI Guidelines & Frameworks (ML, LLM, Agentic) Essentials
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.
