Agentic AI refers to AI systems that can reason, plan, use tools, retrieve information, and take actions to achieve specific goals.Unlike basic AI applications, agentic systems can:Make decisionsExecute multiple stepsInteract with external toolsRetrieve relevant informationComplete tasks with greater autonomy

Agentic AI Training Course with Live Online Certification

Name: Advanced Certification in Agentic AI Engineering
Item: Advanced Certification in Agentic AI Engineering
Rating: 5
Author: Kodeswaran thiyagarajan

Module 01: Python & AI Dev Environment Setup

Topics Covered

Overview of the Agentic AI ecosystem and where Python fits
Setting up Python with pyenv for version management
Creating and managing virtual environments using venv and conda
Configuring VS Code with AI-friendly extensions and settings
Setting up Jupyter Lab for interactive AI experimentation
Understanding Python project structure for production AI apps
Managing dependencies with pip and requirements.txt
Securely managing API keys using python-dotenv and .env files
Introduction to Git: initialising repos, branching, and committing
Pushing AI projects to GitHub with a proper .gitignore
Writing asynchronous Python with asyncio for concurrent LLM calls
Comparing sequential vs concurrent API calls: performance benchmarking

Hands-On

Setting Up Your Python AI Project from Scratch
Managing API Keys Securely with python-dotenv
Running Concurrent LLM Calls with asyncio

Skills Covered

Python
Virtual Environments
Git & GitHub
asyncio
Project Structure
Secret Management

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Module 02: FastAPI, Streamlit & Gradio for AI Apps

Topics Covered

REST API fundamentals: HTTP methods, status codes, request/response lifecycle
FastAPI project structure: routers, models, and dependency injection
Defining Pydantic models for request validation and response schemas
Building async FastAPI endpoints for LLM-powered routes
Streaming LLM responses using Server-Sent Events (SSE)
Handling file uploads in FastAPI for document processing pipelines
Running and testing FastAPI with Uvicorn and Swagger UI
Streamlit fundamentals: layout, widgets, session state, and caching
Building a real-time streaming chat UI in Streamlit
Gradio components: text, file, image inputs and markdown outputs
Creating shareable Gradio demos and deploying to HuggingFace Spaces
Wiring a Streamlit or Gradio front-end to a FastAPI backend
Comparing Streamlit vs Gradio: when to use which

Hands-On

Building Your First FastAPI LLM Endpoint
Streaming AI Responses Live in a Streamlit UI
Creating a Shareable AI Demo with Gradio
Connecting a Gradio Front-End to a FastAPI Backend

Skills Covered

FastAPI
Streamlit
Gradio
REST API Design
Server-Sent Events
Uvicorn
Pydantic

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Module 03: LLM Fundamentals, Context & Prompt Engineering

Topics Covered

How large language models work: tokenisation, attention, and next-token prediction
Understanding context windows: limits, costs, and implications for agents
The OpenAI API: chat completions, parameters, and response structure
The Anthropic API: messages format, system prompts, and model differences
Structuring system prompts for domain-expert personas
Zero-shot, one-shot, and few-shot prompting patterns
Chain-of-Thought (CoT) prompting for complex reasoning tasks
Tree-of-Thought (ToT) for branching multi-path reasoning
ReAct prompting: combining reasoning and action steps
Structured outputs using JSON mode and response schemas
Function calling: defining tools and routing user intent
Context management strategies: chunking, summarisation, and windowing
Measuring and optimising token usage with tiktoken
Evaluating and benchmarking LLM output quality

Hands-On

Comparing Zero-Shot, Few-Shot, and Chain-of-Thought Prompts
Extracting Structured Data with JSON Mode and Pydantic
Routing User Intent with Function Calling

Skills Covered

Prompt Engineering
OpenAI API
Anthropic API
Context Management
Function Calling
JSON Mode
Token Optimisation

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Module 04: Embeddings, Vector Databases & Semantic Search

Topics Covered

What vector embeddings are and why they matter for AI agents
Embedding models: OpenAI text-embedding-3 and HuggingFace sentence-transformers
Generating embeddings for text, documents, and structured data
ChromaDB architecture: collections, documents, metadata, and IDs
CRUD operations and metadata filtering in ChromaDB
FAISS index types: Flat L2, IVF, HNSW — trade-offs explained
Building and querying a FAISS index from a document corpus
Nearest-neighbour retrieval: understanding similarity metrics (cosine, L2, dot product)
Hybrid search: combining BM25 keyword scoring with semantic vector scores
Chunking strategies for effective embedding: fixed, sentence, and recursive
Benchmarking ChromaDB vs FAISS: speed, accuracy, and scalability
Visualising embedding clusters with UMAP to understand semantic groupings
RAG architecture overview: retriever, generator, and the full pipeline

Hands-On

Building a Semantic Search Engine with ChromaDB
Benchmarking FAISS Flat vs HNSW Retrieval Speed
Implementing Hybrid Keyword and Semantic Search
Visualising Document Embeddings with UMAP

Skills Covered

Vector Embeddings
ChromaDB
FAISS
Semantic Search
Hybrid Search
sentence-transformers
RAG Fundamentals

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Module 05: LangChain Core - Chains, Memory & RAG

Topics Covered

LangChain architecture: runnables, the pipe operator, and the LCEL paradigm
Building chains with LCEL: prompt | llm | parser patterns
Prompt templates: ChatPromptTemplate, MessagesPlaceholder, and partial templates
Output parsers: StrOutputParser, JsonOutputParser, and PydanticOutputParser
Conversational memory types: BufferMemory, SummaryMemory, VectorStoreMemory
Maintaining multi-turn conversation history across sessions
Document loaders: PDF, web, CSV, Notion, and custom loaders
Text splitters: RecursiveCharacterTextSplitter and SemanticChunker
Building a RAG pipeline: loader, splitter, embedder, retriever, generator
Retriever types: VectorStoreRetriever, MultiQueryRetriever, ContextualCompressionRetriever
LangChain callbacks and hooks for logging and monitoring
Chaining multiple retrievers and combining results

Hands-On

Building an LCEL Chain with Memory for Multi-Turn Conversations
Creating a RAG Pipeline over a PDF Knowledge Base
Comparing Multi-Query vs Single-Query Retrieval Quality

Skills Covered

LangChain
LCEL
RAG Pipelines
Conversational Memory
Document Loading
Text Splitting
Retrieval Strategies

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Module 06: LangChain Agents & Tool Use

Topics Covered

Agent fundamentals: perception, reasoning, action, and observation loop
ReAct agent pattern: interleaving reasoning traces and tool calls
Plan-and-Execute pattern: generating a plan first, then executing steps
LangChain built-in tools: Tavily Search, Python REPL, Wikipedia, and DuckDuckGo
Creating custom tools with the @tool decorator and StructuredTool
Defining tool input schemas with Pydantic for safe structured arguments
AgentExecutor: configuration, max iterations, early stopping, and verbose mode
Structured output agents: forcing final answers into typed Pydantic objects
Connecting agents to SQL databases with SQLDatabaseToolkit
Multi-step reasoning: chaining tool outputs as inputs to subsequent tool calls
Handling tool failures: try/except patterns and automatic retry logic
Streaming intermediate agent steps to a UI in real time

Hands-On

Building a ReAct Agent with Web Search and Python REPL Tools
Connecting an Agent to a SQL Database for Natural Language Queries
Adding Retry Logic and Error Handling to a Tool-Calling Agent

Skills Covered

LangChain Agents
ReAct Pattern
Custom Tool Development
AgentExecutor
SQL Agent
Structured Output
Tool Error Handling

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Module 07: LangGraph - Stateful Workflows & Routing

Topics Covered

Why LangGraph: limitations of linear chains for complex agentic workflows
Core LangGraph concepts: nodes, edges, and the state schema
Defining a TypedDict state and passing it through the graph
Building a StateGraph: adding nodes, setting entry points, and compiling
Conditional edges: routing to different nodes based on state values
MessageGraph for chat-style workflows with message history
Streaming node-by-node output during graph execution
Adding human-readable labels and descriptions to graph nodes
Visualising and debugging graphs in LangGraph Studio
Building a specialist routing system: classifier node + expert nodes
Handling terminal states: defining END conditions and exit paths
Composing reusable node functions with clean state interfaces
Testing individual nodes in isolation before wiring the full graph

Hands-On

Building a Conditional Routing Graph with Specialist Nodes
Streaming Live Node-by-Node Output in LangGraph
Debugging and Visualising Your Graph in LangGraph Studio
Building an Order-Processing State Machine with Fail and Retry States

Skills Covered

LangGraph
StateGraph
Conditional Routing
Graph Compilation
LangGraph Studio
Stateful Agent Design
Node Streaming

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Module 08: LangGraph - Cycles, HIL & Persistence

Topics Covered

Cycles and loops: when and why to add feedback loops to a graph
Implementing self-correction loops: generate, evaluate, revise
Human-in-the-loop (HIL): understanding interrupt_before and interrupt_after
Pausing a graph, collecting human input, and resuming from a checkpoint
Building approval gate workflows: draft, review, approve, publish
Checkpointers: what they are and why persistence matters for agents
SQLite checkpointer: setup, configuration, and state recovery
Redis checkpointer: setup for high-throughput production environments
Resuming an interrupted workflow after a simulated crash
The Send API: dispatching work to parallel worker nodes
Aggregating parallel node results back into a shared state
Sub-graphs: building modular graph components and composing them
Sharing state across parent and sub-graphs safely

Hands-On

Adding a Human Approval Gate with Interrupt and Resume
Implementing SQLite Checkpointing and Crash Recovery
Fanning Out Tasks to Parallel Nodes with the Send API

Skills Covered

Human-in-the-Loop
LangGraph Checkpointers
SQLite Persistence
Redis Persistence
Parallel Execution
Sub-graph Composition
Cycle Design

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Module 09 - LangSmith - Tracing, Evaluation & Testing

Topics Covered

Why observability matters for LLM applications in production
LangSmith platform overview: projects, runs, traces, and feedback
Instrumenting LangChain and LangGraph runs with LangSmith tracing
Inspecting individual spans: inputs, outputs, latency, and token cost
Creating evaluation datasets: golden QA pairs and edge cases
Running automated evaluations with built-in LangSmith evaluators
LLM-as-judge evaluation: writing a custom grading prompt
Heuristic evaluators: rule-based checks for format, length, and keywords
Regression testing: comparing two prompt versions on the same dataset
A/B testing agent behaviours: setting up experiments and tracking metrics
Annotating runs with human feedback for RLHF dataset creation
Production monitoring dashboards: latency, error rate, and cost over time
Setting up alerts for anomalous agent behaviour in production

Hands-On

Tracing a LangGraph Agent End-to-End in LangSmith
Running an Automated Evaluation Suite with LLM-as-Judge
Setting Up an A/B Experiment to Compare Two Agent Strategies

Skills Covered

LangSmith
LLM Tracing
Automated Evaluation
LLM-as-Judge
Regression Testing
A/B Testing
Production Monitoring

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Module 10: Model Context Protocol: Architecture & Custom Servers

Topics Covered

What is the Model Context Protocol and why Anthropic created it
MCP architecture: hosts, clients, and servers — roles and responsibilities
The three MCP primitives: resources, tools, and prompts
JSON-RPC 2.0 transport layer: stdio and HTTP/SSE communication
Setting up the MCP Python SDK and scaffolding a minimal server
Defining and exposing custom tools from an MCP server
Defining resource endpoints that expose live or static data sources
Implementing prompt templates as MCP primitives
Tool discovery and capability negotiation between client and server
Connecting a custom MCP server to Claude Desktop
Connecting a custom MCP server to VS Code Copilot
MCP security model: permission scopes and access control
Server-side input validation and request sanitisation
Debugging MCP servers with logging and the MCP Inspector tool

Hands-On

Scaffolding and Connecting Your First Custom MCP Server
Exposing a Live Data API as an MCP Resource Endpoint
Implementing Permission Checks and Input Validation in MCP
Debugging an MCP Server with the MCP Inspector Tool

Skills Covered

Model Context Protocol
MCP Python SDK
Custom MCP Servers
Claude Desktop Integration
Tool Discovery
MCP Security
JSON-RPC

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Module 11: MCP - Ecosystem Integrations

Topics Covered

Overview of the MCP ecosystem: official and community servers
SQLite MCP server: exposing schema, running queries, and returning results
Filesystem MCP server: safe file read, write, list, and move operations
GitHub MCP server: managing issues, PRs, branches, and comments via Claude
Playwright MCP server: browser automation and structured data extraction
Chaining multiple MCP servers in a single Claude session
Using MCP servers as tools inside LangChain agents
Using MCP servers as tools inside LangGraph nodes
Integrating MCP into a CrewAI agent as an external tool
Building a report pipeline: SQLite MCP query to Filesystem MCP write
Security considerations when chaining multiple MCP servers
Production deployment patterns for MCP servers

Hands-On Demonstrations

Chaining SQLite MCP and Filesystem MCP to Build a Report Pipeline
Managing a GitHub Repository via Claude and the GitHub MCP Server
Automating Browser Data Extraction with Playwright MCP

Skills Covered

SQLite MCP
GitHub MCP
Playwright MCP
Filesystem MCP
Multi-MCP Chaining
MCP + LangGraph
MCP + CrewAI

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Module 12: Deep Agents - Reflection, Planning & Long-Term Memory

Topics Covered

What makes an agent 'deep': meta-cognition and self-awareness in LLMs
Self-reflection loops: generate, critique against a rubric, revise
Multi-turn reflection: iterating until a quality threshold is met
Plan-and-Execute pattern: separating planning from execution
Replanning: updating the plan mid-execution when new information arrives
ReAct vs Plan-and-Execute: trade-offs and when to use each
Long-term memory types: episodic, semantic, and procedural
Building an episodic memory store with LangGraph and a vector DB
Memory consolidation: summarising and compressing old episodic memories
Semantic memory: storing facts and updating them across sessions
Cross-session memory retrieval: using similarity search to recall past context
Confidence and uncertainty estimation: flagging low-confidence answers
Agent self-improvement: using evaluation scores to update future behaviour
Combining reflection, planning, and memory into one unified agent architecture

Hands-On Demonstrations

Building a Self-Critique and Revision Loop in LangGraph
Implementing a Plan-and-Execute Agent with Mid-Run Replanning
Adding Cross-Session Episodic Memory to an Agent

Skills Covered

Self-Reflection Loops
Plan-and-Execute
Episodic Memory
Semantic Memory
Memory Consolidation
Confidence Estimation
Deep Agent Architecture

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Module 13: CrewAI - Multi-Agent Orchestration & Integration

Topics:

CrewAI philosophy: role-based agents working as a collaborative team
Core components: Agent, Task, Crew, and Process
Defining agents: role, goal, backstory, and tools
Defining tasks: description, expected output, and agent assignment
Sequential process: tasks execute in order with output passed forward
Hierarchical process: a manager agent routes and delegates tasks
Inter-agent delegation: agents handing off work to more specialised peers
Adding LangChain tools to CrewAI agents
Adding MCP servers as tools within a CrewAI agent
Combining CrewAI with LangGraph: Crew as a node in a stateful graph
Parallelising research tasks across multiple agents simultaneously
Handling crew failures: retry strategies and fallback agents
Output parsing and structured results from a crew run
Evaluating crew quality: scoring the final output against a rubric

Hands-on

Building a Three-Agent Sequential Research and Writing Crew
Switching to Hierarchical Mode and Observing Manager Delegation

Skills you will learn:

CrewAI
Multi-Agent Orchestration
Role-Based Design
Hierarchical Process
CrewAI + MCP
CrewAI + LangGraph

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Module 14 - Agentic RAG & GraphRAG

Topics Covered

RAG failure modes: hallucination, missed retrieval, and irrelevant context
Diagnosing RAG failures systematically with RAGAS metrics
Agentic RAG: the agent decides whether, what, and how to retrieve
Self-RAG: retrieve, score chunk relevance, decide to regenerate, synthesise
Corrective RAG (CRAG): falling back to web search when local KB fails
Advanced chunking strategies: semantic chunking, late chunking, parent-child
Re-ranking retrieved chunks with Cohere and cross-encoder models
HyDE: generating a hypothetical answer to improve query embedding
Query expansion: generating multiple reformulations to broaden recall
GraphRAG: representing documents as a knowledge graph of entities and relations
Building a knowledge graph from documents using NetworkX
Multi-hop reasoning: traversing the graph to answer complex questions
Hybrid RAG: combining graph traversal with vector similarity search
Evaluating RAG pipelines with RAGAS: faithfulness, answer relevancy, context recall
Benchmarking Agentic RAG vs standard RAG on the same test set

Hands-On Demonstrations

Implementing Self-RAG with Relevance Scoring and Conditional Regeneration
Building a Corrective RAG Pipeline with Web-Search Fallback
Constructing a Knowledge Graph and Running Multi-Hop GraphRAG Queries
Evaluating Your RAG Pipeline End-to-End with RAGAS

Skills Covered

Agentic RAG
Self-RAG
Corrective RAG
GraphRAG
Knowledge Graphs
Re-ranking
HyDE
RAGAS Evaluation

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Module 15: Dockerizing & Deploying AI Agents

Topics Covered

Docker fundamentals: images, containers, layers, and the build cache
Writing a production Dockerfile for a Python FastAPI + LangChain application
Multi-stage builds: separating build-time dependencies from the runtime image
Docker Compose: defining multi-service stacks with service, network, and volume configs
Composing a full agent stack: FastAPI, ChromaDB, Redis, and a worker service
Environment variable management: .env files, Docker secrets, and runtime injection
Container health checks: liveness, readiness, and graceful shutdown patterns
Pushing images to a container registry: Docker Hub and GCP Artifact Registry
Deploying a containerised agent to GCP Cloud Run with auto-scaling
Deploying to AWS ECS with Fargate: task definitions and service configuration
GitHub Actions CI/CD: build, test, push, and deploy pipeline for AI services
Blue/green deployments: switching traffic between versions with zero downtime
Canary deployments: gradually rolling out a new agent version
Monitoring deployed containers: Cloud Run logs, metrics, and alerting

Hands-On Demonstrations

Building and Running a Multi-Service Agent Stack with Docker Compose
Writing a GitHub Actions CI/CD Pipeline for an AI Agent Service
Deploying a Containerised Agent to GCP Cloud Run with Zero Downtime
Implementing a Blue/Green Deployment Switch for an Agent Service

Skills Covered

Docker
Docker Compose
GitHub Actions
GCP Cloud Run
AWS ECS
CI/CD
Blue/Green Deployment
Container Security

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Module 16: Monitoring, Evaluation & Fine-Tuning

Topics Covered

The three pillars of LLM observability: logs, metrics, and distributed traces
OpenTelemetry for AI: instrumenting LangGraph agents with spans and attributes
Exporting traces to Jaeger and interpreting the trace waterfall
Prometheus: scrape targets, metric types, and PromQL query basics
Grafana: building dashboards for latency, token cost, and error rate
Arize AI and Phoenix: LLM-specific monitoring, embedding drift, and data quality
Cost tracking per run: token budgets, alerts, and quota management
SLA and SLO design for agent systems: what to measure and why
Fine-tuning fundamentals: when prompting is insufficient and fine-tuning is warranted
LoRA (Low-Rank Adaptation): intuition, hyperparameters, and implementation
QLoRA: quantised fine-tuning for resource-constrained environments
Building a fine-tuning dataset from LangSmith agent traces
Training a LoRA adapter on a custom domain dataset with HuggingFace PEFT
Evaluating a fine-tuned model vs the base model on a held-out test set
RLHF and DPO: overview, concepts, and when to consider them

Hands-On Demonstrations

Instrumenting a LangGraph Agent with OpenTelemetry and Building a Grafana Dashboard
Detecting Embedding Drift in a RAG Pipeline with Arize Phoenix
Fine-Tuning a Model with LoRA and Benchmarking Against the Base Model

Skills Covered

OpenTelemetry
Prometheus
Grafana
Arize AI / Phoenix
LLM Observability
LoRA
QLoRA
HuggingFace PEFT
SLA/SLO Design

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Module 17: Guardrails: NeMo, Guardrails AI & Safety

Topics Covered

The AI safety landscape: why guardrails are non-negotiable in production
OWASP LLM Top 10: understanding the most critical risks
Prompt injection attacks: anatomy, real examples, and detection strategies
NVIDIA NeMo Guardrails: architecture, Colang language, and dialog flows
Writing Colang rails: defining allowed and blocked topics
NeMo input rails: validating user messages before the LLM sees them
NeMo output rails: filtering and validating LLM responses before delivery
Guardrails AI framework: validators, the Hub, and the Guard object
Built-in Guardrails AI validators: toxic language, secrets, competitor mentions
Writing custom Guardrails AI validators for domain-specific rules
PII detection with Microsoft Presidio: entities, recognisers, and anonymisation
Building an end-to-end safety stack: input rail, LLM, output validator, PII scrub
Toxicity, bias, and hallucination detection tools and integrations
Constitutional AI principles: embedding safety constraints in agent design
Testing the safety stack: red-teaming with adversarial prompt suites

Hands-On Demonstrations

Writing NeMo Colang Rails to Block Off-Topic and Harmful Requests
Adding PII Detection and Redaction with Presidio and Guardrails AI
Red-Teaming an Agent with Prompt Injections and Hardening the Defences

Skills Covered

NeMo Guardrails
Colang
Guardrails AI
Presidio
Prompt Injection Defence
AI Safety Design
OWASP LLM Top 10

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Module 18: Agentic Workflows with N8N

Topics Covered

N8N overview: visual workflow automation and its role in agentic systems
Self-hosting N8N with Docker and configuring for production
N8N core concepts: workflows, nodes, triggers, and credentials
Webhook trigger node: receiving external events to kick off workflows
OpenAI node: prompt construction, model selection, and response parsing
Anthropic node: connecting Claude to N8N workflows
HTTP Request node: calling any external API from a workflow
Slack node: sending formatted messages and alerts
Error handling in N8N: error branches, retry logic, and fallback paths
Cron trigger: scheduling agent workflows to run on a timetable
N8N expressions: transforming data between nodes with JavaScript
Integrating N8N with LangGraph via HTTP webhook nodes
Storing workflow outputs: Google Sheets, Notion, and Airtable nodes
Deploying N8N workflows to production and managing credentials securely

Hands-On Demonstrations

Building a Webhook-Triggered Lead Enrichment Workflow with OpenAI
Adding Error Handling and Retry Logic to an N8N Workflow
Scheduling a Daily Competitor Monitoring Workflow with Cron and LLM

Skills Covered

N8N
Visual Workflow Automation
Webhook Triggers
LLM Node Integration
Error Handling
Cron Scheduling
N8N + LangGraph

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Module 19: Capstone Part 1: Architecture, Build & Integration

Topics Covered

Capstone problem selection: choosing a real-world industry use case to solve
System architecture design: identifying components, data flows, and boundaries
Technology selection: reasoning through LangGraph vs CrewAI vs hybrid approaches
Designing the RAG layer: index strategy, chunking method, and retrieval approach
Designing the multi-agent layer: agent roles, task decomposition, and delegation flow
Integrating MCP servers: identifying which external tools the system needs
Wiring guardrails into the architecture: input rails, output validators, PII scrubbing
Building the core agent loop: RAG + LangGraph + tool use running end-to-end
Connecting the front-end: Streamlit or Gradio UI wired to the FastAPI agent backend
Instrumenting with LangSmith tracing across all components
Adding Grafana monitoring: latency, token cost, and error rate dashboards
Peer design review: presenting the architecture and receiving structured feedback
Iterating on the build based on review feedback before integration testing

Hands-On Demonstrations

Architecting the Full Capstone System and Presenting the Design
Building the Core Agent Loop with RAG, Multi-Agent Orchestration, and Guardrails
Wiring LangSmith Tracing and Grafana Monitoring into the Full System
Running End-to-End Integration Tests Across All Components

Skills Covered

System Architecture Design
RAG Layer Design
Multi-Agent Design
MCP Integration
Guardrails Integration
LangSmith Tracing
Grafana Monitoring

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Module 20: Capstone Part 2: Deployment

Topics Covered

Dockerising all capstone services: writing production Dockerfiles for each component
Writing the Docker Compose stack: linking FastAPI, ChromaDB, Redis, and the agent worker
CI/CD pipeline for the capstone: automated testing, build, and cloud deployment
Deploying the full stack
Performance testing: measuring latency, throughput, and cost per query under load
Security review: checking for prompt injection vulnerabilities, data leakage, and access control gaps
Writing technical documentation: README, API spec, and architecture decision records
Writing a project brief: summarising the problem, approach, results, and trade-offs
Peer code review: reviewing a peer's codebase and providing structured written feedback
Incorporating peer review feedback and shipping the final version
Preparing the live demo narrative: telling the story of the system to a non-technical audience
Live demo delivery: presenting the working system with monitoring dashboard live
Capstone retrospective: what worked, what you would do differently, and key takeaways

Hands-On Demonstrations

Deploying the Full Capstone Stack to Cloud with CI/CD
Running a Performance and Security Audit on the Live System
Delivering the Live System Demo with Real-Time Monitoring

Skills Covered

Docker & Compose
CI/CD Deployment
Cloud Deployment
Performance Testing
Security Review
Technical Documentation
Live Demo Delivery

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Module 21: Vibe Coding for Developers (Self-paced)

Topics:

Vibe Coding Fundamentals
Vibe Coding Tools Overview
GitHub Copilot Setup and Features
Chat and Interactive Coding
Code Quality and Testing
Advanced Copilot Techniques
Cursor Fundamentals
Codebase Intelligence
Composer
Development with Cursor

Hands-on

Working with GitHub Copilot
Building Projects with Cursor

Skills you will learn:

Vibe Coding
GitHub Copilot
Cursor AI

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Module 22: Programmatic Prompting with DSPy (Self-paced)

Topics:

Introduction to DSPy
DSPy Modules and Signatures
ChainOfThought and Predict Modules
ReAct Modules in DSPy
DSPy Optimizers (BootstrapFewShot, MIPRO)
Compiling and Validating DSPy Programs
Metric Functions and Evaluation
DSPy with Multiple LLMs
Building Self-Improving Pipelines
DSPy vs Manual Prompting Trade-offs
DSPy Production Deployment

Hands-on

Build a Self-Improving Prompt Optimization System with DSPy

Skills you will learn:

DSPy framework
Programmatic prompting
Prompt optimization
Self-improving systems

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Module 23: No-Code Workflow Automation with Zapier and Make (Self-paced)

Topics:

Zapier Fundamentals and Workflows
Trigger and Action Patterns
Multi-Step Zaps for Complex Logic
Zapier API for Custom Integrations
Zapier Tables for Agent Data Storage
Make (Integromat) Overview and Modules
Make Scenarios and Advanced Routing
Webhooks and API Integration in Make
Connecting Python Agents to Zapier/Make
Scaling No-Code Automation

Hands-on

Working with Zapier and Make

Skills you will learn:

Zapier automation
Make workflows
No-code integrations
Hybrid agent-automation

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Module 24: Agent Interoperability: ACP, ANP & A2A Protocols (Self-paced)

Topics:

Agent Interoperability Challenge
Agent-to-Agent (A2A) Protocol Overview
A2A SDK and Implementation
Agent Communication Protocol (ACP) by IBM BeeAI
Agent Network Protocol (ANP) with DIDs
Agent Cards for Capability Discovery
Cross-Framework Agent Communication
Building Multi-Protocol Gateways
Security and Trust in Agent Networks
Enterprise Agent Federation
Future Standards and Governance

Hands-on

Build an Interoperable Multi-Framework Agent Network

Skills you will learn:

Agent protocols
Cross-framework interop
Agent networks
Protocol governance

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Module 25: Generative AI and LLM Security

Topics:

Threats in Generative AI Systems
Common Attack Vectors in Generative AI Systems
Model Theft and Extraction Attacks
Mitigation Strategies for GenAI Risks
LLM-Specific Threats and Risks
Aligning LLM Output to Security Objectives
Securing AI Training Data and Pipelines
Risks in AI Model Hubs and Repositories
Dependency Scanning and Third-Party Model Risks
Bias, Fairness, and Ethical Design in AI Systems
Regulatory and Compliance Standards
Multimodal AI Threat Intelligence
Defending Cyber Operations with Agentic AI

Hands-on

Detecting Prompt Injection and Jailbreak Risks
LLM Integration with Gemini API
Securing AI Data Against Poisoning Risks
Tracking Model Provenance and Scanning Dependencies
Ethical Screening using Sola Security
Agentic AI for Cybersecurity Triage

Skills you will learn:

GenAI Security & Threat Analysis
LLM Risk Assessment & Mitigation
AI Pipeline & Data Security
Ethical AI & Compliance Implementation

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View course curriculum

What is Edureka's Agentic AI Engineering Training Course?

Edureka’s Agentic AI Training Course is a live instructor-led certification program designed to help learners build, deploy, and monitor production-ready AI agents and autonomous AI systems.

What are the prerequisites for the Agentic AI Training Course?

Learners should have a basic understanding of Python programming before joining the course. Familiarity with APIs, machine learning fundamentals, prompt engineering, or generative AI concepts is helpful but not mandatory. You do not need prior experience with LangChain, LangGraph, CrewAI, MCP, or other AI agent frameworks.

Who should take the Agentic AI Training Course?

The Agentic AI course is designed for professionals and learners who want to build autonomous AI applications and production-ready AI agents.

It is a strong fit for:

Software and AI engineers
Data and ML professionals
Technical leads and architects
Working professionals moving into AI engineering

What is the duration of the Agentic AI Training Course?

Edureka’s Agentic AI course is a 10-week live instructor-led program with classes held every Weekend. Each weekend includes 6 hours of live training, resulting in 60 hours of instructor-led training, along with self-paced learning modules for additional practice and learning.

What is the cost for the Agentic AI course?

Edureka’s Agentic AI course fee is ₹72,000 for learners in India and US$899 for international learners. Indian learners can also access an EMI option starting at ₹24,000 per month, subject to applicable payment terms. International payments are securely processed through PayPal.

What skills will I learn in the Agentic AI course?

You will develop practical skills for designing, building, and deploying AI agents.

Key skills include:

LangChain, LangGraph, and CrewAI
MCP and tool integration
Agentic RAG and GraphRAG
Multi-agent orchestration
AI guardrails and observability
FastAPI, Docker, and deployment

Who should take the Agentic AI Training Course?

This course is designed for software engineers, machine learning practitioners, AI/data scientists, and technology professionals who want to move from generative-AI fluency to building, deploying, and operating production-grade autonomous AI systems.

It is also a strong fit for technical leads, solution architects, and senior developers who need to evaluate or design agentic systems for their organisations.

View course curriculum

What is agentic AI?

Agentic AI refers to AI systems that can reason, plan, use tools, retrieve information, and take actions to achieve specific goals.

Unlike basic AI applications, agentic systems can:

Make decisions
Execute multiple steps
Interact with external tools
Retrieve relevant information
Complete tasks with greater autonomy

Is LangChain required for agentic AI?

LangChain is the industry-standard framework for building agent applications and is core to this curriculum. However, agentic AI can also be implemented with LangGraph (advanced state management), CrewAI (multi-agent systems), or custom Python. This course covers LangChain deeply, then shows how to layer CrewAI and LangGraph for enterprise-scale systems.

What are the refunds and batch changes policy for this program?

You can withdraw and claim a refund at any time up to 48 hours after your batch’s first live class.

Refunds are paid less a processing fee of ₹1,500 (US$30 for international participants). After that period, no refund is payable.

Changing your batch: You can defer once to a later batch free of charge at any time before your batch begins. After it begins, you can change batch once in any three-month period, subject to seat availability, for ₹1,500 (US$30).

How to request: Write to learner.support@edureka.co from your registered email address. We will give you a decision within 7 working days, and process an approved refund within a further 7 working days. If you disagree with a decision, write to grievance@edureka.co and we will respond within 3 business days.

The terms that apply: The terms published here apply to your enrolment. If anything different has been described to you, please ask us to confirm it in writing before you enrol. Full terms are available on our Terms & Conditions page.

What is agentic RAG and why is it better than traditional RAG?

Agentic RAG (like CRAG and Self-RAG) iteratively improves retrieval quality by having the agent evaluate whether retrieved context is sufficient, and re-retrieve or rewrite queries if needed. Traditional RAG retrieves once and passes context to the LLM. Agentic RAG produces higher-quality answers by closing the loop — this course teaches both patterns in depth.

How is agentic AI different from generative AI?

Generative AI primarily creates content, while Agentic AI uses AI models to perform tasks and take actions.

Generative AI can create:

Text
Code
Images
Summaries

Agentic AI adds planning, tool use, retrieval, decision-making, and autonomous task execution.

Can I learn multi-agent systems without prior agentic AI experience?

Yes. The first 6 modules establish agentic AI fundamentals, prompt engineering, embeddings, and LangChain basics. Modules 13 then teach CrewAI multi-agent orchestration, and modules 7–9 teach LangGraph multi-agent patterns. You do not need prior agentic AI experience, only working Python and comfort with APIs.

What is Model Context Protocol (MCP) and why is it important?

MCP is an emerging standard for tool interoperability between agents and backend systems. Instead of hardcoding API calls for each system, MCP allows agents to discover and invoke tools standardly. Modules 10-11 teach you to build custom MCP servers and integrate them into multi-agent pipelines — a critical skill for enterprise deployments.

How do I choose between LangGraph and CrewAI for multi-agent systems?

LangGraph excels at complex stateful workflows and long-horizon reasoning with checkpointing and human-in-the-loop. CrewAI excels at role-based agents with clear task dependencies and sequential execution. This course teaches both, and the capstone uses a hybrid approach — LangGraph for orchestration, CrewAI for crew-level collaboration.

What is prompt engineering for agentic systems and how is it different?

Agent prompt engineering focuses on eliciting reasoning (CoT, ToT, ReAct), structured outputs, and tool-use patterns. Module 3 teaches these patterns with real examples. Unlike static prompt engineering for text generation, agent prompts must guide autonomous decision-making across multiple steps and tool calls — a distinct skill.

Do I need to know Docker and Kubernetes before this course?

No prior Docker or Kubernetes experience is required. Module 15 teaches Docker fundamentals and containerisation of FastAPI agent services. The capstone uses Docker Compose for multi-service stacks. If you have DevOps experience, you will move quickly; if not, the course provides hands-on guidance.

Why should I learn agentic AI?

Learning Agentic AI can help you develop skills for building intelligent applications that combine LLMs, tools, data, and automation.

Important areas include:

AI agent development
RAG
Multi-agent systems
MCP
Workflow automation
AI observability
Production deployment

What is NeMo Guardrails and when should I use it instead of Guardrails AI?

NeMo Guardrails (Colang-based) enforces topic and tone safety at the conversation level — best for controlling which subjects an agent engages with. Guardrails AI validates outputs with on-fail actions (reask, filter, exception) — best for ensuring structured outputs and avoiding PII leakage. Module 17 covers both patterns for different use cases.

Is the Agentic AI course live, online, or self-paced?

Edureka’s Agentic AI Engineering program is primarily a live, instructor-led online course supported by self-paced learning content.

Learners benefit from:

Live instructor-led sessions
Recorded classes
Self-paced learning resources
Practical projects
Mentor and learner support

How does observability with Langfuse and LangSmith help agentic systems?

Langfuse and LangSmith trace every agent step, token usage, and latency. You can evaluate agent quality, A/B test prompts, and cost-attribute per feature. For autonomous systems running in production, this visibility is critical — you need to know why an agent made a decision and where the cost is coming from. Module 16 and the capstone instrument full observability.

What projects and hands-on experience are included in the Agentic AI course?

The course includes practical projects based on real-world Agentic AI applications.

Projects include:

AI-powered returns triage
Semantic search pipelines
Market intelligence agents
Multi-agent travel planning
CrewAI workflows
Deep research agents
n8n automation
Customer intelligence systems

What is the difference between agentic AI and AI agents?

AI agents are software that perceive, decide, and act. Agentic AI refers specifically to autonomous systems powered by LLMs that can reason, plan, use tools, and retrieve knowledge at scale. This course focuses on agentic AI — using modern LLMs to build genuinely autonomous systems, not simple if-then bots.

Can I deploy agentic systems to production after this course?

Yes. You will deploy a production-shaped agent to the cloud. Module 15 covers Docker, Docker Compose, health checks, GitHub Actions CI/CD, and deployment to GCP Cloud Run and AWS ECS (Fargate). In the capstone, you ship a guarded, monitored and containerised agent system with a CI/CD pipeline.

What skills are required to become an Agentic AI engineer?

An Agentic AI engineer needs a combination of programming, AI, and application-development skills.

Important areas include:

Python
LLMs and prompt engineering
RAG and vector databases
APIs and tool calling
Agent frameworks
Multi-agent orchestration
Security and observability
AI deployment

What is the salary range for agentic AI engineers in 2026?

India: ₹16–52 LPA depending on role and experience. US: $110K+ depending on seniority. Entry-level AI Agent Engineers start around ₹10 LPA (India) or $115K (US). Multi-agent systems engineers and architects command the higher bands. This course aims to position you in the mid-to-senior range within 12–24 months post-certification.

What does an Agentic AI engineer do?

An Agentic AI engineer designs and develops AI systems that can reason, retrieve information, use tools, and complete multi-step tasks.

Typical responsibilities include:

Building AI agents
Developing RAG pipelines
Integrating APIs and databases
Orchestrating multiple agents
Implementing guardrails
Monitoring and deploying AI applications

What does an Agentic AI engineer do?

An Agentic AI engineer designs and develops AI systems that can reason, retrieve information, use tools, and complete multi-step tasks.

Typical responsibilities include:

Building AI agents
Developing RAG pipelines
Integrating APIs and databases
Orchestrating multiple agents
Implementing guardrails
Monitoring and deploying AI applications

How do I prepare for an agentic AI engineer interview?

Be able to articulate why you would choose LangGraph over CrewAI for a given problem, explain agentic RAG vs traditional RAG, design a multi-agent system architecture on a whiteboard, and walk through a production deployment checklist. The capstone forces you to do all of this — completing it is the strongest interview preparation.

What is the difference between agentic RAG and traditional RAG?

Traditional RAG retrieves documents once and passes them to the language model. Agentic RAG lets an AI agent decide whether to retrieve, what to search for, whether the results are good enough, and whether to rewrite the query or search elsewhere. This improves answer quality on complex questions but adds latency and cost.

Agentic RAG vs traditional RAG comparison
Criteria	Traditional RAG	Agentic RAG
Retrieval	Single retrieval step	Iterative; the agent decides when and how to retrieve
Query handling	Uses the user's query as-is	Rewrites, expands or decomposes the query
Quality checks	None built in	Scores retrieved chunks for relevance and re-retrieves if needed
Fallback when the knowledge base fails	Answers from poor context or hallucinates	Falls back to another source such as web search (Corrective RAG)
Multi-step questions	Struggles	Handles multi-hop reasoning, including via knowledge graphs (GraphRAG)
Latency and cost	Lower	Higher, since there are more model calls
Complexity	Simple pipeline	Needs orchestration, for example with LangGraph
Best for	FAQs and simple document Q&A	Complex, high-stakes or multi-source questions
Where you learn it	Modules 4–5	Module 14

When to use which:

Use traditional RAG when questions are simple, the knowledge base is clean, and speed and cost matter most.
Use agentic RAG when answers must be verified, queries are ambiguous or multi-part, or one source isn't enough.
In either case, measure quality with metrics such as faithfulness, answer relevancy and context recall (RAGAS, covered in Module 14).

What is the difference between deep agents and simple agents?

Simple agents reason in 2–3 steps. Deep agents reason hierarchically, decomposing complex goals into subtasks, spawning sub-agents, and using memory hierarchies. Module 12 teaches deep reasoning patterns — essential for complex planning tasks like research, analysis, and multi-step workflows.

How long does it take to learn agentic AI?

The time required depends on your programming experience, AI knowledge, and learning goals.

You can start learning the fundamentals within weeks through consistent practice. Developing production-level expertise takes longer and requires hands-on experience.

How do I choose between LangGraph and CrewAI for multi-agent systems?

Choose LangGraph when you need precise control over state, branching, loops, persistence and human approval steps. Choose CrewAI when your problem maps naturally to a team of role-based agents handing work to each other. Many production systems combine them, with LangGraph orchestrating the workflow and CrewAI handling collaborative sub-tasks.

LangGraph vs CrewAI comparison
Criteria	LangGraph	CrewAI
Core idea	Stateful graph of nodes and edges	Team of role-based agents (Agent, Task, Crew, Process)
Control over flow	Fine-grained: conditional edges, cycles, parallel branches	Higher-level: sequential or hierarchical processes
State management	Explicit typed state shared across nodes	Task outputs passed between agents
Persistence and recovery	Built-in checkpointers (SQLite, Redis), resume after failure	Limited out of the box; typically handled in your own code
Human-in-the-loop	Native interrupts and approval gates	Possible, but less granular
Learning curve	Steeper, since you design the graph	Gentler, since you define roles, goals and tasks
Best for	Long-running, auditable, production workflows	Research, content and analysis pipelines with clear role handoffs
Where you learn it	Modules 7–8	Module 13

When to use which:

Use LangGraph for approval workflows, self-correction loops, order-processing state machines and anything that must survive crashes.
Use CrewAI for a researcher, analyst and writer style crew that produces a report.
Use both when you need a controlled workflow where one step is a collaborating crew. The course capstone takes this hybrid approach.

Is this course hands-on or lecture-heavy?

70% hands-on labs, 30% theory. Each live module includes a guided demo and a hands-on lab where you build alongside the instructor. You ship 5+ projects, not watch 14 lectures. Every module assessment is a graded end-to-end project — theory is always paired with practice.

What is LangChain and why is it used for agentic AI?

LangChain is a framework for developing applications powered by language models. It provides components for prompts, tools, retrieval, memory, structured outputs, and agent workflows.

For Agentic AI development, LangChain helps developers:

Connect LLMs with tools
Retrieve information
Build agent workflows
Create multi-step AI applications

What is the difference between LangChain and LangGraph?

Short answer: LangChain provides the building blocks for LLM applications, such as prompts, tools, retrievers, memory and chains. LangGraph builds on those blocks to run them as a stateful graph with branching, loops, persistence and human approval. Use LangChain for linear pipelines and LangGraph when the workflow needs to adapt or recover.

LangChain vs LangGraph comparison
Criteria	LangChain	LangGraph
Purpose	Components and chains for LLM apps	Orchestration of stateful, multi-step agent workflows
Flow	Mostly linear (prompt → model → parser)	Conditional, cyclical, parallel
State and persistence	Conversation memory	Shared typed state with checkpointing
Best for	RAG pipelines, simple agents, tool calling	Complex agents, approvals, self-correction
Where you learn it	Modules 5–6	Modules 7–8

Can I use open-source LLMs like Llama instead of OpenAI?

Yes. The course defaults to OpenAI for consistency, but Ollama (Llama, Mistral) is integrated for local inference. Module 1 and the self-paced LLM Essentials cover model selection — cost-benefit of cloud APIs vs self-hosted models. You can adapt any project to your preferred model.

What are the prerequisites for the capstone project?

Completion of all 20 live modules and passing all module assessments. The capstone is a 15-hour integrated build that synthesises every skill from the course. It is mentor-reviewed on a 100-point rubric and defended in a live demo with the mentor.

How often are new batches of this course launched?

New batches start every 4–6 weeks to accommodate different schedules and time zones. Weekend batches (Saturday + Sunday, 8:30 PM to 11:30 PM IST & 11:00 AM to 2:00 pm EDT) are the standard format. Cohort size is capped to ensure quality mentor and instructor attention.

Can employers sponsor employees for this course?

Yes. Edureka offers corporate enrolment packages with bulk discounts, completion tracking, and invoicing. Many organisations sponsor this course when scaling agentic AI systems internally. Contact the support team for a corporate proposal.

What happens if I fail the certification exam?

You are permitted two attempts. If both fail, additional mentor support and re-training modules are available at no extra cost. The cohort Discord and office hours support exam prep — most learners pass on the first or second attempt.

What is LangGraph and why is it used for agentic AI?

LangGraph is designed for building stateful, multi-step AI workflows.

It supports:

State management
Conditional routing
Cycles
Checkpointing
Human-in-the-loop interactions

These capabilities make LangGraph useful for complex AI agents that require controlled execution, persistent state, and iterative workflows.

Is there a guarantee that I will get a job after completing this course?

No job guarantee, but the course is designed to make you highly hireable. A strong portfolio (14 projects), an industry-recognised certificate, and hands-on capstone experience are powerful hiring signals. Edureka's career services team helps with resume, LinkedIn, and mock interviews, but hiring is ultimately the responsibility of the employer.

What is CrewAI and how is it used in multi-agent systems?

CrewAI is a framework for developing multi-agent applications where specialized AI agents collaborate on tasks. CrewAI is particularly useful when multiple role-based agents need to collaborate to complete a larger objective or business process.

What is DSPy and how is it used in AI applications?

DSPy is a framework for programmatically developing and optimizing LLM applications.

It helps developers move beyond manually written prompts by using:

Reusable components
Structured prompting
Evaluation
Optimization

Measurable performance improvements

This makes DSPy useful for developing systematic and repeatable AI application pipelines.

How do I network with other learners?

The cohort Discord channel runs throughout the course with peer discussions, study groups, and trainer office hours. Edureka also hosts quarterly alumni events and maintains an alumni community for continued peer learning and career referrals.

How is n8n used with AI agents?

n8n is a workflow automation platform that can connect AI agents with external applications and services. Combined with AI agents, n8n can automate business processes and connect intelligent systems with existing tools.

View course curriculum

Testimonial Reviews

Soumyaranjan nayakConsultant

I am using Edureka Online Learning Platform from almost more than 2 years. The Live classes are cond ... Read More

Venkat NukalaAWS/DevOps Engineer

Very clear teaching. We practiced Agentic Al tools daily. Madam also helped me prepare for interview ... Read More

Kodeswaran thiyagarajanSenior Project Manager

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Advanced Certification in Agentic AI Engineering

Go Beyond GenAI — Become a production-ready Agentic AI Engineer

Why should you take Advanced Certification in Agentic AI Engineering?

Instructor-led Advanced Certification in Agentic AI Engineering live online classes

Financing Options

Curriculum

Module 01: Python & AI Dev Environment Setup

Module 02: FastAPI, Streamlit & Gradio for AI Apps

Module 03: LLM Fundamentals, Context & Prompt Engineering

Module 04: Embeddings, Vector Databases & Semantic Search

Module 05: LangChain Core - Chains, Memory & RAG

Module 06: LangChain Agents & Tool Use

Module 07: LangGraph - Stateful Workflows & Routing

Module 08: LangGraph - Cycles, HIL & Persistence

Module 09 - LangSmith - Tracing, Evaluation & Testing

Module 10: Model Context Protocol: Architecture & Custom Servers

Module 11: MCP - Ecosystem Integrations

Module 12: Deep Agents - Reflection, Planning & Long-Term Memory

Module 13: CrewAI - Multi-Agent Orchestration & Integration

Module 14 - Agentic RAG & GraphRAG

Module 15: Dockerizing & Deploying AI Agents

Module 16: Monitoring, Evaluation & Fine-Tuning

Module 17: Guardrails: NeMo, Guardrails AI & Safety

Module 18: Agentic Workflows with N8N

Module 19: Capstone Part 1: Architecture, Build & Integration

Module 20: Capstone Part 2: Deployment

Module 21: Vibe Coding for Developers (Self-paced)

Module 22: Programmatic Prompting with DSPy (Self-paced)

Module 23: No-Code Workflow Automation with Zapier and Make (Self-paced)

Module 24: Agent Interoperability: ACP, ANP & A2A Protocols (Self-paced)

Module 25: Generative AI and LLM Security

Agentic AI Course Description

What is Edureka's Agentic AI Engineering Training Course?

What are the prerequisites for the Agentic AI Training Course?

Who should take the Agentic AI Training Course?

What is the duration of the Agentic AI Training Course?

What is the cost for the Agentic AI course?

What skills will I learn in the Agentic AI course?

Who should take the Agentic AI Training Course?

Testimonial Reviews

Video Reviews

Advanced Certification in Agentic AI Course FAQs

What is agentic AI?

Is LangChain required for agentic AI?

What are the refunds and batch changes policy for this program?

What is agentic RAG and why is it better than traditional RAG?

How is agentic AI different from generative AI?

Can I learn multi-agent systems without prior agentic AI experience?

What is Model Context Protocol (MCP) and why is it important?

How do I choose between LangGraph and CrewAI for multi-agent systems?

What is prompt engineering for agentic systems and how is it different?

Do I need to know Docker and Kubernetes before this course?

Why should I learn agentic AI?

What is NeMo Guardrails and when should I use it instead of Guardrails AI?

Is the Agentic AI course live, online, or self-paced?

How does observability with Langfuse and LangSmith help agentic systems?

What projects and hands-on experience are included in the Agentic AI course?

What is the difference between agentic AI and AI agents?

Can I deploy agentic systems to production after this course?

What skills are required to become an Agentic AI engineer?

What is the salary range for agentic AI engineers in 2026?

What does an Agentic AI engineer do?

What does an Agentic AI engineer do?

How do I prepare for an agentic AI engineer interview?

What is the difference between agentic RAG and traditional RAG?

What is the difference between deep agents and simple agents?

How long does it take to learn agentic AI?

How do I choose between LangGraph and CrewAI for multi-agent systems?

Is this course hands-on or lecture-heavy?

What is LangChain and why is it used for agentic AI?

What is the difference between LangChain and LangGraph?

Can I use open-source LLMs like Llama instead of OpenAI?

What are the prerequisites for the capstone project?

How often are new batches of this course launched?

Can employers sponsor employees for this course?

What happens if I fail the certification exam?

What is LangGraph and why is it used for agentic AI?

Is there a guarantee that I will get a job after completing this course?

What is CrewAI and how is it used in multi-agent systems?

What is DSPy and how is it used in AI applications?