Course Overview
This curriculum prepares learners to design, build, evaluate, and deploy agentic AI systems that operate autonomously, reason over tasks, coordinate tools, and deliver reliable outcomes. The program emphasizes theory, hands-on practice, safety, and real-world deployment patterns.
Module 1: Foundations of Agentic AI
1.1 What Agentic AI Is
• Autonomous decision-making systems capable of reasoning, planning, and executing tasks without continuous human prompting.
• Distinct from standard LLM applications due to persistent memory, tool use, environment interaction, and goal-directed behavior.
1.2 Core Components
• Reasoning engine (LLM or hybrid model).
• Planning subsystem.
• Tool interface layer (APIs, functions, external systems).
• Memory architecture (episodic, semantic, vector stores, logs).
• Safety and guardrails.
• Execution loop.
1.3 Architectural Patterns
• Loop-based agent.
• Graph-based agent.
• Multi-agent orchestration.
• Event-driven and workflow-infused agents.
1.4 Typical Use Cases
• Autonomous research and analysis.
• Software creation, debugging, and deployment.
• Business automation and integration.
• Autonomous operations monitoring.
• Data pipeline orchestration.
Module 2: LLM Reasoning for Agentic Systems
2.1 Chain-of-Thought and Structured Reasoning
• Techniques for stable thought decomposition.
• Task breakdown, intermediate steps, and verification.
2.2 Planning Techniques
• Forward planning versus backward planning.
• Hierarchical task planning.
• Replanning and adaptive loops.
2.3 Error Handling
• Recognizing hallucinations.
• Self-critique loops.
• Redundant reasoning passes.
2.4 Reinforcement of Stable Behavior
• Prompt scaffolding.
• System-level goals and constraints.
• State-based reasoning.
Module 3: Memory and Knowledge Management
3.1 Types of Memory
• Long-term vector memories.
• Short-term context memory.
• Procedural memory for routines.
• Tool-based or database-backed memory.
3.2 Building a Memory Layer
• Document ingestion.
• Embedding pipelines.
• Retrieval ranking.
3.3 Memory Safety
• Avoiding harmful growth of memory.
• Forgetting strategies.
• Contextual relevance filtering.
Module 4: Tool Use and Environment Interaction
4.1 Tool Abstractions
• Function calling.
• API orchestration.
• Database queries and updates.
• Code execution.
4.2 Tool Design Principles
• Deterministic outputs.
• Clear error messages.
• Bounded action spaces.
• Idempotent design for safe retries.
4.3 Executing Complex Workflows
• Multi-step plans requiring multiple tools.
• Persistent state management.
• Observability and instrumentation.
Module 5: Multi-Agent Systems
5.1 Agent Roles
• Coordinator agents.
• Specialist agents.
• Critic or evaluator agents.
5.2 Communication Patterns
• Message passing.
• Shared memory boards.
• Token-bounded dialogue loops.
5.3 Multi-Agent Safety
• Conflict resolution.
• Role permissions and isolation.
• Voting and consensus mechanisms.
Module 6: System Safety and Governance
6.1 Safety Principles
• Predictability, controllability, and transparency.
• Guardrails for instructions and behavior.
• Action-level authorization.
6.2 Human-in-the-Loop Patterns
• Approval points.
• Arbitration of high-risk actions.
• Oversight of autonomous tasks.
6.3 Red Teaming Agentic Systems
• Prompt-based testing.
• Tool misuse scenarios.
• Escalation and fallback plans.
Module 7: Engineering and Deployment
7.1 Agent Development Lifecycle
• Requirement gathering.
• Architecture design.
• Implementation and iteration.
• Evaluation and benchmarking.
7.2 Infrastructure
• Hosting models (cloud, containerized, serverless).
• Vector database selection and tuning.
• Logging, monitoring, and metric dashboards.
7.3 Continuous Improvement
• Feedback loops.
• Updating models and tools.
• Versioning strategies for agent behavior.
Module 8: Evaluation and Benchmarking
8.1 Agent Evaluation Methods
• Task success rate.
• Reasoning coherence.
• Tool-execution accuracy.
• Error recovery.
8.2 Benchmark Types
• Realistic, scenario-based tasks.
• Automated regression tests.
• Human review scoring.
Module 9: Capstone Project
Learners design, build, and evaluate a fully functioning agentic system. The project must include:
• Clear system goal definition.
• Multi-step planning.
• At least one external tool or API.
• A memory layer.
• Safety controls and fallback logic.
• Evaluation metrics and documentation.
Examples of project themes:
• An autonomous customer-support triage agent.
• A self-correcting data pipeline manager.
• A research automation agent that summarizes, compares, and verifies findings.
Module 10: Exam Preparation and Certification Guidance
10.1 Review Topics
• Agent architecture.
• Reasoning and planning.
• Tool integration.
• Memory engineering.
• Safety mechanisms.
• Evaluation techniques.
10.2 Practice Tasks
• Designing prompts for agents.
• Building minimal agents from scratch.
• Simulating debugging and failure recovery.
10.3 Certification Strategy
• Master fundamental concepts.
• Practice implementing end-to-end agents.
• Prepare small demonstrations or reproducible examples.
• Document workflows clearly and concisely.
Want to learn more? Tonex offers Certified Agentic AI Engineer (CAAIENG), a 2-day course where participants apply reflex, model based, and learning based agent architectures to real use cases as well as engineer memory, planning, and goal reasoning loops for robust autonomy.
Attendees also orchestrate multi agent collaboration with LangChain, AutoGen, and CrewAI, design human agent teaming patterns with oversight and intervention points, implement trust, explainability, and safety mechanisms for accountable systems,evaluate performance, cost, and reliability with reproducible metrics, strengthen cybersecurity of agent pipelines, data flows, and tool integrations.
This course is especially beneficial for:
- AI Engineers and Developers
- Data Scientists and MLOps Engineers
- Software Architects and Product Leaders
- Cybersecurity Professionals
- QA and Reliability Engineers
- Innovation and R&D Teams
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.
