Learning
AI learning resources
Tutorials, guides, and courses for AI agent development, from foundational concepts to advanced deployment, with a focus on the Indian AI ecosystem.
How to choose a starting point
The three paths below assume different starting knowledge, not different ambitions — an experienced backend engineer can often start at Intermediate even with zero prior agent-specific work, while someone new to both programming and AI should expect the Beginner path to take longer than the listed 2-4 weeks. The paths are sequenced by dependency, not by difficulty label: Intermediate assumes you can already call an LLM API and handle a JSON response, and Advanced assumes you have something running that you now need to operate reliably rather than something to build from scratch.
A common mistake is starting at Advanced because a production deadline is looming. Deployment, security, and compliance content only pays off once there is a working agent behind it — reading about high availability for a system that doesn't exist yet just delays the point where you find out what actually breaks. If time is short, build the smallest working version first (Beginner path, module one and two), then jump straight to the specific Advanced module the deadline requires.
What this page does not do
This is a curated map of learning paths and external references, not a course platform — there is no login, no progress tracking, and no certificate. Each module description tells you what the module covers so you can judge relevance before spending time on it; the linked external resources are the official documentation and tutorials for the tools named, not summaries or paraphrases we've written ourselves. Where a module references a specific product (Sarvam-30B, Krutrim-2, Bhashini, CrewAI, LangGraph), cross-check its current status on that product's own directory page before committing significant time, since product capabilities and availability change faster than a static learning path can.
Learning path: Beginner · 2-4 weeks
Foundational concepts and practical introductions for those with basic programming knowledge but no prior AI experience.
Introduction to AI Agents
What AI agents are, how they differ from traditional software, and the key components: perception, reasoning, and action.
Your First AI Agent
Build and deploy your first AI agent — development environment, LLM API connection, prompting, context management, basic error handling.
Understanding APIs and Tools
How AI agents interact with external systems through APIs and tools — REST APIs, authentication, wrapping services as agent tools.
Learning path: Intermediate · 4-6 weeks
Model Context Protocol and multi-agent orchestration patterns, assuming beginner-path completion or equivalent experience.
MCP Fundamentals
MCP architecture, transport mechanisms (stdio and Streamable HTTP), and the security model.
Building MCP Servers
Create custom MCP servers with the official SDK — tools, handlers, error handling, transport configuration.
Multi-Agent Orchestration
Task decomposition, agent communication, and result aggregation using frameworks like CrewAI and LangGraph.
Learning path: Advanced · 6-8 weeks
Production deployment, security, and compliance for enterprise AI agent systems.
Production Deployment
Containerization, orchestration, CI/CD, high availability, load balancing, and disaster recovery for AI systems.
Security and Compliance
DPDP Act requirements, data residency, consent management, audit logging, and security assessments.
Indic AI Development
Indic language models, Hinglish code-switching, and India-specific regulatory compliance.
External learning resources
Official MCP Documentation
The official Model Context Protocol documentation with API references and best practices for server development.
LangChain Documentation
Guides for building AI agents with LangChain, including chains, agents, and memory management.
CrewAI Tutorials
Tutorials for multi-agent orchestration using CrewAI, with role-based agent collaboration examples.
IndiaAI Resources
Government of India AI resources including the IndiaAI Mission portal and BHASHINI documentation.
Frequently asked questions
How long does it take to learn AI agent development?
With consistent effort, the basics take 2-4 weeks and proficiency 3-6 months. Prior programming experience accelerates this.
Do I need a machine learning background?
No. Many AI agent tools abstract away ML complexity. Basic programming skills and API familiarity are sufficient.
What programming languages are used?
Python and JavaScript/TypeScript are the most common languages for AI agent development.
Can I build AI agents for Indian languages?
Yes — using Indic language models like Sarvam-30B or Krutrim-2, integrated with Bhashini for translation.