MASTER AGENTIC AI & AI AUTOMATION
From Zero-Level Foundations to Production-Ready AI Agents
This program takes learners from the fundamentals of programming and AI to designing, developing, integrating, testing and deploying intelligent AI agents and automation workflows.
Build practical skills across Python, LLMs, AI agents, tool calling, LangChain, LangGraph, MCP, RAG, multi-agent systems, workflow automation, APIs, Docker and cloud deployment.
- Beginner-to-Production Learning Journey
- Practical AI Agent Development Labs
- Intelligent Workflow & Business Process Automation
- AI Integration, Testing & Production Deployment
- 6-8 Months
- 300+ Practical Hours
- Live Online Training
- Industry Practice Labs
What You Will Learn
The learning journey begins with programming and AI foundations, then progresses through agent development, business workflow automation, system integration, testing and production deployment.
Python & AI Development Foundations
Build the programming and software foundations required to develop AI-powered automation systems.
- Python fundamentals, data structures and Object-Oriented Programming
- Functions, modules, packages and exception handling
- JSON, HTTP, REST APIs and API authentication
- Git, GitHub, virtual environments and dependency management
- Debugging, logging and reusable code
- Asynchronous programming fundamentals
- Working with external services and databases
- Python scripting for task automation
LLM Fundamentals & AI Application Development
Understand how large language models work and how to integrate them into software applications.
- LLM fundamentals, tokens and context windows
- Prompt engineering and structured outputs
- LLM APIs and model selection
- System instructions and conversation management
- Function calling and tool-use fundamentals
- Streaming responses and asynchronous API calls
- Model limitations, hallucinations and error handling
- Cost, latency and response-quality considerations
AI Agents & Agent Development Frameworks
Progress from simple model interactions to agents that use tools and execute multi-step tasks.
- AI agents, goals, instructions and execution cycles
- Agent architecture and decision-making workflows
- Tool definitions and tool calling
- State management and conversation memory
- LangChain fundamentals
- LangGraph workflows, nodes, edges and state
- Conditional routing, retries and checkpointing
- Human approval and controlled agent execution
Retrieval-Augmented Generation (RAG) & Knowledge Integration
Learn to connect AI systems with external knowledge so they can retrieve relevant information before responding.
- Document ingestion and text extraction
- Document chunking and metadata
- Embeddings and semantic search
- Vector databases and similarity search
- Retrieval pipelines and context construction
- RAG application development
- Retrieval and answer-quality evaluation
- Source attribution and access-controlled retrieval
MCP, Tools & Enterprise Integration
Connect agents to external tools, APIs and business systems.
- Model Context Protocol (MCP) fundamentals
- MCP clients, servers and tool discovery
- Tool calling and structured tool inputs
- REST APIs, webhooks and third-party integrations
- Database and file-system integrations
- Authentication, permissions and secret management
- Tool errors, timeouts and retry handling
- Safe execution and approval-based actions
Multi-Agent Systems & Workflow Automation
Design systems that coordinate multiple tasks, tools and specialised agents.
- Multi-agent architecture and task decomposition
- Agent orchestration and workflow routing
- Sequential, parallel and conditional execution
- Specialised agents and role-based responsibilities
- Shared state and inter-agent communication
- Business workflow automation
- Event-driven execution and scheduled workflows
- Human-in-the-loop review and escalation
Agent Testing, Evaluation & Security
Develop the skills required to make agentic applications predictable, secure and maintainable.
- Unit, integration and end-to-end testing
- Agent trajectory and tool-call evaluation
- Testing RAG retrieval and generated answers
- Prompt injection and unsafe tool-use risks
- Input validation and output constraints
- Guardrails, permissions and approval mechanisms
- Tracing, logging and failure analysis
- Latency, reliability and cost evaluation
Production Deployment & Operations
Take agentic applications beyond local experiments into deployed, observable and maintainable services.
- FastAPI and agent service endpoints
- Docker and containerisation
- Environment configuration and secrets
- Cloud deployment fundamentals
- CI/CD and repeatable release workflows
- Monitoring, tracing and application logs
- Error recovery, retries and service health checks
- Production evaluation, versioning and improvement
Hands-On Industry Practice
Architect. Build. Orchestrate. Automate. Scale.
From Business Problem to Production-Ready AI Automation
Identify the Business Problem & Define Requirements
- Identify a repetitive business process suitable for AI automation
- Analyse existing workflows, inputs and outputs
- Define the agent's responsibilities and boundaries
- Specify success criteria and measurable outcomes
- Document functional requirements and acceptance criteria
Design the AI Agent & Workflow Architecture
- Design the agent's goals, instructions and execution flow
- Select appropriate LLMs and agent frameworks
- Define tools, APIs, data sources and dependencies
- Design state management and workflow orchestration
- Establish human approval points and execution limits
Develop the Agent & Integrate Tools
- Implement agent logic using Python
- Connect LLMs through model APIs
- Implement tool calling and structured inputs
- Integrate external APIs, databases and business services
- Handle tool failures, timeouts and invalid responses
Build Knowledge-Based AI & RAG
- Ingest documents and prepare searchable content
- Generate embeddings and configure vector retrieval
- Connect retrieved information to the agent workflow
- Implement source-aware responses and access controls
- Validate retrieval quality and answer accuracy
Automate Business Workflows
- Build sequential and conditional automation workflows
- Coordinate multiple tools and specialised agents
- Integrate MCP servers and external capabilities
- Implement scheduling, triggers and event-driven execution
- Add approval steps, exception handling and escalation
Test, Evaluate & Secure the Agent
- Test agent decisions and tool execution paths
- Validate outputs against defined acceptance criteria
- Evaluate retrieval quality and response accuracy
- Implement guardrails, permissions and input validation
- Investigate failures, unsafe actions and prompt injection risks
Package & Deploy the Application
- Expose agent functionality through FastAPI endpoints
- Containerise the application using Docker
- Configure credentials, environment variables and secrets
- Deploy the application to a cloud environment
- Verify deployed services and integration workflows
Release, Monitor & Improve the System
- Configure release workflows and application versioning
- Monitor execution traces, errors and agent performance
- Track latency, token usage and operational costs
- Troubleshoot production issues and failed workflows
- Improve reliability through evaluation and controlled updates
Agentic AI & Automation
What the Job Market Is Telling Us
Companies are exploring AI agents for business workflows, customer service, software development and enterprise operations. The opportunity is not just to build agents, but to integrate, evaluate, secure and operate them reliably.
Tools & Technologies
Programming:
Python, SQL, JSON, Bash
Development
Git, GitHub, REST APIs, FastAPI, asynchronous programming
LLMs & AI
LLM APIs, prompt engineering, structured outputs, function calling
Agent Frameworks
LangChain, LangGraph
Knowledge & Retrieval
RAG, embeddings, vector databases, semantic search
Agent Integration
MCP, API integrations, webhooks, database connectors
Automation
Workflow orchestration, conditional routing, scheduling, event-driven execution
Testing & Security
Pytest, agent evaluation, tracing, guardrails, access control
Who is this Program For?
Beginners & Fresh Graduates
Start with programming and AI foundations, then progress towards building and deploying AI agents.
Python Developers
Extend existing development skills into LLM integration, agent frameworks and business workflow automation.
Software Engineers
Learn to incorporate AI agents, tool calling and knowledge retrieval into software products.
Automation & QA Professionals
Explore intelligent automation, workflow orchestration, evaluation and exception handling.
IT & Cloud Professionals
Develop capabilities for deploying, integrating and monitoring agentic AI applications.
Working Professionals Transitioning into AI
Follow a structured path from AI fundamentals to practical agent development and production workflows.
Your Outcome
Build, Integrate & Deploy AI Agents
By the end of the program, your practical learning should enable you to
Why Primebit Solution?
17 Years of Training Experience
Learn with an established training company with 17 years of experience in technical education.
In-Depth Technical Training
Build a strong understanding of concepts, tools and implementation through detailed, step-by-step teaching.
100% Practical Lab-Based Learning
Apply your knowledge through hands-on labs, real-world workflows and practical implementation exercises.
Career & Placement Support
Get career guidance, interview preparation and placement assistance for relevant national and international opportunities.
Global Alumni & Career Opportunities
Connect with a learning community spanning the USA, UK, India, Dubai, Canada, Australia and other regions.
Global Companies with Relevant Telecom, Cloud and DevOps Roles
Our alumni are working in leading semiconductor, networking and technology companies in India and abroad, including Qualcomm, MediaTek, Samsung, Apple, Broadcom, Cisco, AT&T Labs and many more.
PROGRAM AT A GLANCE
Duration & Practical Hours
Flexible 6/8 months. 300+ Hours
Mode
Live Online Hands-on Training
Core Focus
AI agent development, intelligent workflow automation and production deployment
Deployment
FastAPI, Docker, cloud deployment, monitoring and evaluation
Program
Agentic AI & AI Automation
Learning Path
Python Foundations → LLMs → AI Agents → Agent Frameworks → RAG → MCP & Integrations → Workflow Automation → Testing & Security → Cloud Deployment
AI Technologies
LLMs, LangChain, LangGraph, RAG, MCP and multi-agent systems
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Frequently Asked Questions
The program is designed to start with foundational programming and AI concepts before progressing to agent development. Learners new to programming should complete the foundation modules rather than jumping directly into advanced frameworks.
No prior Agentic AI experience is assumed in the beginner learning path. The program introduces the concepts and frameworks progressively.
These technologies are included in the proposed learning journey. The final syllabus should specify the exact framework versions, practical exercises and integration tools covered
The curriculum includes multi-agent architecture and orchestration, alongside the foundations needed to understand when a single agent or a multi-agent workflow is appropriate.
The program covers API development, Docker, cloud deployment, testing, monitoring and operational practices. The final syllabus should specify the cloud provider and deployment environments used in practical labs.
No. Prompt engineering is one component. Agentic AI engineering also involves programming, tool integration, workflow control, state management, evaluation, security and deployment.
Start Your Agentic AI Journey
Develop. Integerate. Test. Automate.
Move beyond basic AI interactions. Learn to build intelligent agents, automate workflows and deploy practical AI applications.
Explore the Agentic AI & AI Automation program with Primebit Solution.
PRIMEBIT SOLUTION
- Agentic AI & LLMs
- LangChain & LangGraph
- RAG & Knowledge System
- Workflow Automation
- FastAPI & Production Ops
Design the Agent. Understand the workflow. Build the future of autonomous systems.