AI Automation & Agent Development Course in Ahmedabad

Module 1: Introduction to AI Automation

• • AI, generative AI, automation, and AI agents explained
• • Rule-based automation versus AI-powered automation
• • Business use cases across IT, sales, HR, support, and operations
• • Understanding where AI is suitable and where it is not
• • Setting up the development environment

Module 2: Python Programming from Zero

• • Variables, data types, operators, and input/output
• • Conditions, loops, functions, and modules
• • Lists, dictionaries, sets, and tuples
• • File handling, exceptions, and logging
• • Object-oriented programming fundamentals
• • Virtual environments, packages, and debugging

Module 3: APIs and Web Integration

• • HTTP, requests, responses, and status codes
• • REST API concepts and JSON
• • GET, POST, PUT, PATCH, and DELETE
• • API authentication, API keys, and environment variables
• • Using Python requests and API documentation
• • Rate limits, retries, timeouts, and error handling

Module 4: Working with Large Language Models

• • How large language models work at a practical level
• • Prompts, system instructions, and structured outputs
• • Calling LLMs through APIs
• • Token usage, latency, and cost management
• • Function calling and tool calling
• • Model selection and output validation

Module 5: No-Code and Low-Code Automation

• • Workflow automation concepts
• • n8n fundamentals and workflow design
• • Triggers, actions, conditions, and data transformation
• • Webhooks, schedules, and event-based workflows
• • Connecting email, forms, spreadsheets, and APIs
• • Workflow testing, error handling, and monitoring

Module 6: Building AI-Powered Workflows

• • Integrating LLMs into automation pipelines
• • Classifying and extracting information from text
• • Summarization and content generation workflows
• • Processing structured and unstructured data
• • Human approval steps and escalation rules
• • Handling failures and preventing duplicate actions

Module 7: AI Agent Fundamentals

• • What AI agents are and how they differ from chatbots
• • Agent goals, instructions, context, and memory
• • Planning and multi-step task execution
• • Tool selection and tool calling
• • Single-agent and multi-agent concepts
• • Agent limitations, guardrails, and controlled permissions

Module 8: AI Agent Development with Frameworks

• • Introduction to LangChain
• • Creating tool-enabled agents
• • Structured outputs and reusable components
• • LangGraph nodes, edges, state, and workflows
• • Conditional routing and controlled loops
• • Choosing between custom Python, LangChain, and LangGraph

Module 9: Knowledge Bases and RAG

• • Embeddings and semantic search fundamentals
• • Document loading and text chunking
• • Vector database concepts
• • Retrieval-Augmented Generation (RAG)
• • Source-grounded answers and citations
• • Testing retrieval quality and reducing hallucinations

Module 10: Databases and Persistent Memory

• • SQL fundamentals and PostgreSQL
• • Storing users, conversations, tasks, and workflow results
• • Database integration using Python
• • Conversation history and persistent state
• • Data validation, access control, and retention
• • When to use relational databases versus vector stores

Module 11: Developing AI Automation APIs

• • FastAPI fundamentals
• • Creating REST endpoints for AI workflows
• • Request validation and response models
• • Authentication and authorization basics
• • Connecting the frontend or automation platform to backend services
• • API documentation and integration testing

Module 12: Business Applications and Integrations

• • CRM and lead-management automation
• • Email classification and response drafting
• • Customer support knowledge assistants
• • Spreadsheet and document processing
• • Webhooks, messaging integrations, and notifications
• • Approval-based automation for business-critical actions

Module 13: Advanced AI Agent Workflows

• • Multi-step agent orchestration
• • Human-in-the-loop workflows
• • Multi-agent coordination concepts
• • Short-term and long-term memory patterns
• • Scheduling, background jobs, and task queues
• • Safe tool execution and permission boundaries

Module 14: AI Safety, Security, and Reliability

• • Prompt injection and untrusted input
• • Protecting API keys and sensitive information
• • Least-privilege access for agents
• • Validating AI-generated actions and outputs
• • Logging, audit trails, and error recovery
• • Cost controls, rate limits, and fallback strategies

Module 15: Testing and Evaluation

• • Unit and integration testing
• • Testing prompts, tools, and workflow branches
• • Evaluating answer quality and retrieval accuracy
• • Measuring task success, latency, and cost
• • Regression testing after prompt or model changes
• • Using AI coding assistants responsibly and verifying generated code

Module 16: Deployment and Production

• • Git and GitHub fundamentals
• • Docker basics
• • Environment configuration and secrets management
• • Deploying APIs and automation services
• • Monitoring, logs, and failure alerts
• • Documentation, maintenance, and version control

Module 17: Portfolio and Career Preparation

• • GitHub portfolio and project documentation
• • Architecture diagrams and workflow demonstrations
• • Resume and project explanation preparation
• • Technical interview questions and practical exercises
• • Debugging and problem-solving assessments
• • Understanding real-world AI automation job responsibilities

What is the AI Automation & Agent Development course?

It teaches you to build automated workflows and AI agents that can interpret requests, use approved tools, retrieve information, and assist with business tasks.

Do I need to know Python before joining?

No. Python is covered from the beginning. Prior programming experience can help but is not mandatory.

What is the difference between AI automation and AI agents?

AI automation follows a defined workflow, while an AI agent can select from approved tools and perform multiple steps toward a goal. Many real-world systems combine both.

What career opportunities are available after the course?

Potential entry-level roles include AI Automation Developer, Workflow Automation Engineer, Junior AI Application Developer, and Business Process Automation Specialist. Opportunities depend on your skills, portfolio, interview performance, and market demand.

Will AI tools replace the need to learn programming?

No. AI tools can speed up development, but you still need programming fundamentals, debugging, API knowledge, security awareness, testing, and the ability to verify AI-generated code and actions.