Certification in Agentic AI Design and Development
32-hour training covering complete agentic AI lifecycle
Secure AI agents for multi-step business workflows
Agent architecture, ReAct, tools, RAG, memory & API integration
LangChain, LangGraph, CrewAI, Google ADK, n8n, MCP & A2A
Multi-agent workflows, evaluation test suites & debugging skills
Enterprise agent simulation, automation sandbox & capstone project
Flexible classroom & live online learning options
4.9/54.9/5Overview
What you will learn:
- Identify suitable agentic AI use cases and define appropriate levels of autonomy
- Design bounded agent architectures, tools, states, and controls
- Develop reliable prompts, structured outputs, and model-selection strategies
- Build custom tools, function-calling workflows, and secure API integrations
- Implement RAG pipelines using embeddings, vector search, and memory flows
- Build and orchestrate multi-agent workflows using LangChain, LangGraph, CrewAI, MCP, and A2A
- Evaluate AI agents using test suites, debugging techniques, security controls, and governance practices
- Plan deployment with observability, cost control, and the capstone project
Curriculum
Agent versus chatbot distinctions
Agent versus RPA comparison
Levels of autonomy
Agentic capabilities and limits
Use-case selection criteria
Bounded business task framing
AI Integration
- Analyze use-case descriptions with AI to classify suitable agentic opportunities.
- Support requirement framing by generating bounded task definitions and risk assumptions.
Activities/Case Study
- Exercise: explore agent use cases.
- Case study: chatbot versus agent.
- Simulation: identify autonomy levels.
Goals, environment, inputs, outputs
State and failure modes
ReAct reasoning loops
Planning, reflection, routing
Orchestrator worker patterns
Workflow versus autonomous agents
AI Integration
- Analyze task flows with AI to propose suitable agent architecture patterns.
- Support design reviews by generating failure modes and control checkpoints.
Activities/Case Study
- Hands-on: map agent boundaries.
- Case study: architecture trade-offs.
- Simulation: choose design patterns.
System prompt design
Prompt and context strategy
Structured outputs design
Model selection criteria
Latency and cost trade-offs
Reliable instruction handling
AI Integration
- Analyze prompt variants with AI to improve consistency and output structure.
- Support model selection using AI-generated comparisons of cost and capability.
Activities/Case Study
- Hands-on: refine system prompts.
- Case study: output reliability.
- Simulation: compare model choices.
Custom tool design
Function calling workflows
API integration patterns
Input validation controls
Least-privilege tool access
Tool misuse prevention
AI Integration
- Analyze API specifications with AI to draft tool schemas and parameters.
- Support secure tool design by flagging excessive permissions and unsafe inputs.
Activities/Case Study
- Hands-on: build custom tools.
- Case study: API integration.
- Simulation: validate tool permissions.
RAG pipeline basics
Embeddings and vector search
Short-term memory design
Long-term memory strategies
Context management methods
Memory poisoning risks
AI Integration
- Analyze retrieval outputs with AI to improve grounding and context relevance.
- Support memory design by identifying persistence risks and context gaps.
Activities/Case Study
- Hands-on: configure memory flows.
- Case study: long-task reliability.
- Simulation: debug context drift.
LangChain and LangGraph overview
CrewAI workflow concepts
Google ADK foundations
Low-code agents with n8n
Code-first versus low-code
Framework selection criteria
AI Integration
- Analyze framework options with AI to match delivery constraints and complexity.
- Support workflow assembly by generating starter logic for orchestration steps.
Activities/Case Study
- Hands-on: compare framework flows.
- Case study: platform selection.
- Simulation: build orchestration logic.
Roles and delegation models
Coordination and shared state
Failure handling strategies
MCP integration concepts
A2A communication patterns
AGENTS.md convention usage
AI Integration
- Analyze agent responsibilities with AI to improve delegation and coordination logic.
- Support protocol planning by generating secure interaction flows for connected agents.
Activities/Case Study
- Hands-on: design agent roles.
- Case study: protocol integration.
- Simulation: coordinate agent handoffs.
Agent performance metrics
Test-suite design methods
LLM-as-judge evaluation
Long-horizon task testing
Debugging failed runs
Iterative improvement cycles
AI Integration
- Analyze run traces with AI to detect failure patterns and weak steps.
- Support evaluation design by generating test cases and scoring rubrics.
Activities/Case Study
- Hands-on: create test suites.
- Case study: failed agent runs.
- Simulation: score task success.
Prompt injection defenses
Tool misuse controls
Context poisoning prevention
OWASP agentic risks overview
Bias, privacy, transparency
Human oversight checkpoints
AI Integration
- Analyze threat scenarios with AI to identify likely attack paths and controls.
- Support governance planning by drafting oversight checkpoints and accountability notes.
Activities/Case Study
- Hands-on: assess security risks.
- Case study: governance failures.
- Role-play: approve risky actions.
Observability and logging design
Cost and latency control
Scaling and enterprise integration
Capstone build and test
Design note preparation
ROI business case presentation
AI Integration
- Analyze operational logs with AI to surface cost, latency, and reliability issues.
- Support capstone delivery by generating presentation drafts and business case summaries.
Activities/Case Study
- Hands-on: finalize capstone agent.
- Case study: production rollout.
- Role-play: present deployment plan.
Participants act as an internal AI engineering team tasked with designing an agentic solution for a high-volume enterprise service workflow.
The scenario involves requests that require tool access, policy checks, knowledge retrieval, approvals, and status updates across connected systems.
Teams must decide whether a single-agent, workflow-based, or multi-agent design is most appropriate.
They also need to define memory boundaries, tool permissions, escalation points, and failure handling before implementation begins.
Working in online breakout teams, participants design the architecture, build a bounded prototype, and test it against realistic task scenarios and edge cases.
They evaluate task success, tool-call accuracy, and failure patterns while applying security and governance controls.
Each team then presents its deployment approach, observability plan, and business value case.
The outcome is a practical view of how agentic AI can automate complex workflows without losing control, accountability, or operational clarity.
Participants work in a simulated online environment that mirrors common enterprise integration conditions for agentic AI delivery.
They design automated workflows using relevant tools from the course, including agent frameworks, APIs, retrieval components, and orchestration logic.
The sandbox shows where delays, context loss, weak permissions, and poor handoffs can disrupt outcomes.
This gives participants a safe setting to streamline task execution, improve reliability, and test production-minded design choices before wider deployment.
Using generative AI and automation methods, participants create prompts, tool schemas, memory flows, evaluation checks, and workflow logic for a bounded business task.
They produce a working agent prototype, test scenarios, design notes, and a concise business case.
Governance remains visible throughout through approval points, logging expectations, and least-privilege controls.
The result is a hands-on build experience that balances speed, quality, security, and enterprise readiness.