Agentic AI Certification Training Program

Learning Outcomes:

Understand the evolution from traditional AI to Agentic AI.

Define key concepts: agents, autonomy, goals, and environments.

Differentiate between reactive and proactive agents.

Set up development environment.

Topics covered:

What is Agentic AI vs Traditional AI/ML.

Agent architectures and types.

Core components: perception, reasoning, action.

Use cases across industries.

Tool setup: Python, LangChain, OpenAI API.

Hands-on Lab Activities:

Environment setup and API configuration.

Build a simple reactive chatbot.

Create a basic goal-oriented agent.

Learning Outcomes:

Understand LLM capabilities and limitations for agentic workflows.

Implement prompt engineering for agent behaviour.

Design ReAct (Reasoning + Acting) patterns.

Manage context windows and token limits.

Topics covered:

LLMs are the brain of agents.

Prompt engineering for agents.

Chain-of-Thought (CoT) and ReAct framework.

Context window optimisation.

Conversation summarisation strategies.

Context pruning techniques.

Hands-on Lab Activities:

Design prompts for multi-step reasoning.

Implement the ReAct pattern for problem-solving.

Build a context management system.

Implement conversation summarisation.

Learning Outcomes:

Implement short-term and long-term memory systems.

Design conversation history management.

Use vector databases for semantic memory.

Manage agent state across sessions.

Topics covered:

Memory types: episodic, semantic, procedural.

Conversation buffers and windowing.

Vector databases (Pinecone, ChromaDB).

Embeddings for memory retrieval.

State persistence strategies.

Memory optimisation for cost efficiency.

Hands-on Lab Activities:

Implement a conversation memory buffer.

Build semantic memory with vector DB.

Create an agent with long-term memory.

Optimise memory for token efficiency.

Learning Outcomes:

Connect agents to external tools and APIs.

Implement function calling with structured outputs.

Design tool selection logic.

Validate and parse agent outputs reliably.

Topics covered:

Tool abstraction and interfaces.

OpenAI function calling and JSON mode.

Pydantic models for output validation.

Tool schemas and descriptions.

API integrations.

Output parsing and error handling.

Structured data extraction.

Hands-on Lab Activities:

Create custom tools with structured outputs.

Implement function calling with Pydantic validation.

Build a multi-tool agent with reliable parsing.

Handle malformed outputs and validation errors.

Learning Outcomes:

Implement planning algorithms.

Design task decomposition strategies.

Create goal-oriented agent behaviours.

Optimise planning for efficiency.

Topics covered:

Planning vs replanning strategies.

Task decomposition techniques.

Plan-and-Execute pattern.

ReWOO (Reasoning Without Observation).

Subgoal generation and tracking.

Cost-aware planning.

Hands-on Lab Activities:

Build a task decomposition agent.

Implement Plan-and-Execute workflow.

Create a travel planning agent.

Design a project management assistant.

Learning Outcomes:

Master LangChain and LangGraph for agent development.

Use CrewAI for role-based agents.

Implement workflow orchestration with n8n.

Compare framework capabilities.

Topics covered:

LangChain agents and chains.

LangGraph for cyclic workflows.

CrewAI for role-based agents.

n8n for visual workflow orchestration.

Integrating agents with n8n.

Framework selection criteria.

Hands-on Lab Activities:

Build agents with LangChain.

Create complex workflows with LangGraph.

Design visual agent workflows in n8n.

Build end-to-end automated workflow.

Learning Outcomes:

Implement RAG pipelines for knowledge grounding.

Design document ingestion and chunking strategies.

Integrate vision and document understanding.

Build multi-modal agentic systems.

Topics covered:

RAG architecture and components.

Document loaders and text splitters.

Embedding models and vector stores.

Retrieval strategies (similarity, MMR, hybrid).

Vision-enabled agents (GPT-4V, Claude).

PDF and image processing.

Multi-modal RAG patterns.

Hands-on Lab Activities:

Build a document Q&A agent with RAG.

Create a vision-enabled agent for image analysis.

Build a multi-modal research assistant.

Implement PDF analysis agent.

Learning Outcomes:

Design multi-agent architectures.

Implement agent communication protocols.

Coordinate agent behaviours.

Manage agent roles and responsibilities.

Topics covered:

Multi-agent system patterns.

Communication protocols: message passing, blackboard.

Agent-to-agent communication standards.

Message formats and serialisation.

Coordination strategies.

Hierarchical vs flat architectures.

Role assignment and specialisation.

Hands-on Lab Activities:

Create a two-agent debate system.

Implement standardised message protocol.

Build a hierarchical multi-agent system.

Design an agent communication interface.

Learning Outcomes:

Implement advanced collaboration patterns.

Design supervisor and worker architectures.

Create human-in-the-loop workflows.

Implement agent reflection and critique mechanisms.

Topics covered:

Supervisor-worker pattern.

Agent reflection and self-critique.

Collaborative decision-making.

Human-in-the-loop patterns.

Approval workflows and handoff.

Human feedback integration.

Task routing and delegation.

Hands-on Lab Activities:

Build a code review multi-agent system.

Implement HITL approval workflow.

Create an agent with a human feedback loop.

Design a customer service system with escalation.

Learning Outcomes:

Design comprehensive testing strategies for agents.

Implement evaluation metrics and benchmarks.

Monitor agent behaviour in production.

Debug and troubleshoot agentic workflows.

Topics covered:

Unit testing for agent components.

Integration testing for workflows.

Simulation environments.

Success metrics: accuracy, latency, cost.

LangSmith and LangFuse for tracing.

Performance benchmarking.

A/B testing agents.

Regression testing.

Hands-on Lab Activities:

Write unit tests for agent components.

Set up LangSmith for agent tracing.

Create evaluation dataset and automated tests.

Build a monitoring dashboard with alerts.

Learning Outcomes:

Deploy agents to production environments.

Implement failure recovery and resilience patterns.

Design for scalability and reliability.

Optimize costs and performance.

Implement streaming for real-time interaction.

Topics covered:

API development with FastAPI/Flask.

Containerization with Docker.

Cloud deployment.

Circuit breakers and retry logic.

Graceful degradation strategies.

Streaming responses and WebSockets.

Cost optimisation: caching, model selection.

Rate limiting and quota management.

Hands-on Lab Activities:

Containerise and deploy the agent application.

Implement circuit breakers and resilience patterns.

Build a streaming agent with WebSocket.

Implement cost optimisation strategies.

Learning Outcomes:

Implement comprehensive security measures.

Conduct adversarial testing and red teaming.

Address ethical considerations in agentic AI.

Design guardrails and safety mechanisms.

Build an end-to-end agentic system.

Topics covered:

Security: API keys, data privacy, sandboxing.

Prompt injection prevention.

Adversarial testing and red teaming.

Jailbreaking prevention.

Input sanitisation.

Ethical AI principles and bias mitigation.

Regulatory compliance (GDPR, data retention).

Agent feedback loops and continuous improvement.

Hands-on Lab Activities:

Implement security guardrails and adversarial testing.

Project: Build and deploy a complete multi-agent system with HITL, monitoring, and security (e.g., AI-powered business analyst, automated customer support, research automation system).