Generative AI (GenAI) is a class of AI models, led by large language models (LLMs) such as GPT, Claude and Gemini, that create new text, code, images, audio or video by learning the patterns of enormous datasets. An AI agent is software that uses such a model as its reasoning engine to plan a multi-step task, call tools (search, databases, APIs, browsers), observe results and keep going until a goal is met, with limited human input. Agentic AI is the shift from AI that responds to AI that acts.
How generative AI works
An LLM is a neural network trained to predict the next token (a word fragment) across trillions of words. That single objective, at scale, produces a system that can summarise contracts, write SQL, translate Marathi, explain a radiology report and draft a sales email. Three practical concepts matter for building with it:
- Prompting and context — the model only knows what is in its context window; prompt engineering is the craft of putting the right instructions, examples and data there.
- Retrieval-Augmented Generation (RAG) — your documents are chunked, embedded into vectors and retrieved at question time, so answers are grounded in your data with citations instead of hallucinated.
- Fine-tuning — adjusting a model's weights on your examples; useful for style and format, rarely for knowledge, and usually the last thing to try.
What makes something an AI agent
- Goal"Reconcile this month's invoices against bank statements and flag mismatches."
- PlanThe LLM breaks the goal into steps and decides which tools it needs.
- Act with toolsIt calls functions: read a spreadsheet, query an ERP, search email, run code. Standards such as MCP (Model Context Protocol) make tools plug-and-play.
- Observe and loopIt reads each result, corrects course, retries on errors, and stops when done or when a human must approve.
- Memory and governanceShort-term scratchpads, long-term stores, audit logs and human-in-the-loop gates keep it useful and safe.
Multi-agent systems coordinate several specialised agents, a researcher, a writer, a reviewer, under a supervisor, using frameworks such as LangGraph, AutoGen and CrewAI. This is the pattern behind coding agents (Claude Code, Cursor, Codex), customer-support agents and the "AI employees" enterprises are now piloting.
Chatbot vs copilot vs agent vs multi-agent
GenAI and AI-agent jobs in India
GenAI created a new layer of roles above classic data science, and they are among the best paid in Indian tech because supply is thin. Banks, IT services, SaaS companies, GCCs and start-ups are all hiring for the same titles.
Skills roadmap for GenAI and agents
Which Aegis path fits you?
Learn GenAI and AI agents at Aegis
Aegis's flagship 11-month PGP covers LLMs, RAG, multi-agent orchestration, MCP, multimodal AI, MLOps and responsible AI with 11 portfolio projects. Two shorter formats serve engineers and domain experts:
PGP in GenAI & AI Agents
11 months · Hybrid · 11 portfolio projects
LLMs, RAG, multi-agent orchestration, MCP, MLOps and responsible AI, with a 12-week capstone.
Aegis programThe Agentic Professional
20 hours · Lab + theory · Privacy-first
Domain experts build private, governed AI agents on their own machine with Ollama, n8n and MCP.
Frequently asked questions
What is the difference between generative AI and AI agents?
Generative AI is the model that produces content. An AI agent is a system built around such a model that plans, uses tools and takes actions to achieve a goal. Agents use GenAI; GenAI alone does not act.
What is RAG in simple terms?
Retrieval-Augmented Generation: before answering, the system searches your own documents, puts the relevant passages into the model's prompt, and the model answers from them, with citations. It is how companies make LLMs accurate on private data.
What is MCP (Model Context Protocol)?
An open standard, released by Anthropic in late 2024, that lets AI models and agents connect to tools, data sources and APIs through one uniform interface. It is quickly becoming the standard way to give agents capabilities.
What jobs does generative AI create?
GenAI/LLM Engineer, AI Agent Developer, RAG Systems Engineer, AI Product Manager, AI Solutions Architect, Prompt/Evaluation Engineer, Conversational AI Designer and Enterprise AI Consultant, among others.
What is the GenAI engineer salary in India?
Indicatively ₹8–16 LPA at entry, ₹16–35 LPA mid-level and ₹35–60+ LPA senior in 2026, depending on employer and depth of production experience.
Can a non-engineer learn to build AI agents?
Yes, at the level of private agents for your own domain work, using low-code tools such as n8n with local models via Ollama. Aegis's 20-hour Agentic Professional course is designed for exactly that; production enterprise agents still need engineering.