STAGE 01
GenAI Foundations
Understand Generative AI, foundation models, LLMs, tokens, context and modern AI application patterns.
Learn how to design and build intelligent AI agents that can understand goals, plan tasks, use tools, retrieve knowledge, interact with APIs and execute controlled multi-step workflows using modern Generative AI.
Agentic AI moves beyond simple question-and-answer applications. An AI agent can combine a large language model with tools, external knowledge, memory, instructions and workflows to perform goal-oriented tasks.
This learning path starts with Generative AI and LLM fundamentals and progressively introduces agent architecture, tool calling, structured outputs, context management and workflow orchestration.
The advanced stages cover RAG, planning, reasoning, AI agent memory, multi-agent systems, Model Context Protocol (MCP), evaluation, observability, security and governance.
The emphasis is on practical engineering: designing agent workflows, connecting agents to APIs and enterprise data, controlling actions and building reliable AI applications suitable for real-world environments.
Progress from Generative AI fundamentals to production-ready AI agents and multi-agent workflows.
Understand Generative AI, foundation models, LLMs, tokens, context and modern AI application patterns.
Work with LLM APIs, prompts, structured outputs, context management and model integration.
Understand the components of AI agents including models, tools, memory, instructions and orchestration.
Connect AI agents with APIs, functions, databases, applications and external services.
Build agents capable of retrieving enterprise knowledge through documents, embeddings and vector search.
Design controlled workflows where agents decompose objectives and execute multi-step tasks.
Build deterministic and dynamic workflows combining LLMs, tools, retrieval and business logic.
Coordinate specialized agents for complex business and technical workflows.
Apply evaluation, observability, security, governance, deployment and operational controls to AI agents.
A structured curriculum covering the technical foundations and engineering practices required to build modern AI agents.
Understand the technology foundation behind modern LLM-powered applications and AI agents.
Learn the application engineering concepts required to integrate large language models.
Understand how models, tools, memory and orchestration combine to create agentic applications.
Design instructions and prompts that help agents operate reliably within defined objectives.
Connect AI agents to external tools and enterprise services so they can perform useful actions.
Explore approaches for managing state, context and information across agent interactions.
Build retrieval-based agents capable of accessing external and enterprise knowledge.
Design agent workflows that break complex objectives into manageable and controlled tasks.
Combine models, tools, retrieval and application logic into reliable AI workflows.
Understand architectures where multiple specialized agents collaborate to solve complex problems.
Explore standardized approaches for connecting AI applications with tools, resources and external systems.
Learn how agentic applications are evaluated, secured, monitored and governed for production environments.
Develop practical capabilities for designing, integrating, evaluating and operating intelligent AI agent systems.
Understand the architecture and components required to build goal-oriented AI agents.
Integrate large language models into intelligent applications and agent workflows.
Connect agents to APIs, functions, databases and external enterprise services.
Build knowledge-enabled agents using retrieval, embeddings and vector search.
Design multi-step task decomposition and controlled execution patterns.
Manage context, state and information persistence across agent interactions.
Design coordinated workflows involving multiple specialized AI agents.
Connect AI applications with tools, resources and external systems through standardized interfaces.
Build practical AI agent systems that connect LLMs, enterprise knowledge, tools and business workflows.
Build an AI agent that searches approved information sources, retrieves relevant knowledge and produces structured research output.
Create a knowledge agent that retrieves enterprise documents and uses relevant context to answer questions.
Build an agent that invokes defined APIs and tools to retrieve information or perform controlled actions.
Design an AI support agent capable of retrieving knowledge, identifying intent and escalating cases when human intervention is required.
Build a coordinated workflow involving specialized agents for research, analysis, validation and reporting.
Design an agent that can inspect approved operational information, invoke authorized tools and produce structured operational recommendations.
The learning sequence progressively moves from language models to intelligent, tool-enabled and controlled agents.
Agentic AI skills can support engineering, architecture, automation and AI leadership career paths.
Design and develop intelligent agents that combine LLMs, tools, memory, retrieval and workflows.
Build LLM-powered applications, RAG systems and modern generative AI solutions.
Develop multi-step AI workflows capable of planning, tool usage and controlled task execution.
Apply AI agents to automate selected business, operational and knowledge-intensive workflows.
Design enterprise architectures combining AI agents, data, applications, APIs, security and infrastructure.
Build the deployment, monitoring, evaluation, security and governance capabilities supporting production AI agents.
Continue building your capabilities across AI Engineering, Generative AI, LLMs, RAG and data disciplines.
Build end-to-end AI Engineering capabilities across Python, Data Science, ML, DL, GenAI and production AI.
Learn foundation models, LLMs, prompting, embeddings and modern Generative AI application architectures.
Develop practical capabilities for building applications around large language models.
Build retrieval-augmented AI applications using enterprise documents, embeddings and vector search.
An Agentic AI course in India can provide a structured learning path for professionals who want to move beyond basic Generative AI usage and understand how intelligent AI agents can perform multi-step tasks.
Modern Agentic AI combines large language models, prompt engineering, tool calling, APIs, retrieval, memory, workflow orchestration and application logic. These components allow AI systems to interact with external information and services under defined controls.
Advanced Agentic AI architectures can incorporate RAG, planning, reasoning, multi-agent systems, Model Context Protocol, evaluation, observability, security and governance. These capabilities are particularly relevant when organizations begin moving from experimental AI assistants toward enterprise AI workflows.
A strong Agentic AI learning path should therefore emphasize engineering discipline rather than simply demonstrating AI tools. Learners should understand how to design agent architectures, define tool permissions, control execution, evaluate outputs, handle failures and integrate AI systems responsibly with enterprise applications.
Agentic AI refers to AI systems designed to pursue goals through capabilities such as planning, reasoning, tool usage, information retrieval and controlled multi-step execution.
An AI agent is a software system that combines an AI model with instructions, tools, context, memory and workflows to perform tasks toward a defined objective.
Yes. Modern AI agents frequently use large language models and other foundation models as part of their reasoning, language and decision-support capabilities.
Tool calling allows an AI model or agent to invoke defined functions, APIs or services to retrieve information or perform authorized actions.
Yes. RAG is covered as a major component of knowledge-enabled agents. Topics include documents, chunking, embeddings, vector search and retrieval workflows.
Agent memory refers to mechanisms used to preserve relevant information, state or context across interactions and workflow steps.
Multi-Agent AI involves multiple specialized AI agents collaborating through defined communication and orchestration patterns to complete complex tasks.
Model Context Protocol, or MCP, is a protocol designed to provide standardized ways for AI applications to connect with external tools, resources and data sources.
Yes. Agentic AI can support enterprise knowledge, customer service, IT operations, analytics, business process automation and decision-support scenarios when implemented with suitable security, governance and human oversight.
Practical Agentic AI engineering benefits significantly from programming knowledge, especially Python, APIs, data handling and software engineering concepts.
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