मुफ़्तArtificial Intelligence
AI Agents Engineering
Build and deploy real AI agents, not just prompt one: LangChain vs LangGraph, multi-agent systems with CrewAI, tool use via MCP, and RAG-powered retrieval, capped with your own deployed multi-agent research assistant.
Get the vocabulary and mental model right first. Understand what makes a system 'agentic', how agents differ from plain LLM calls, and the compound-AI architecture behind them.
- The anatomy of an agent: LLM + tools + memory + planning
- How agents move from monolithic models to compound AI systems
- Reason-act loops and when an agent decides to act
- Where agents genuinely help vs where they add risk
- Agentic AI vs generative AI, clearly distinguished
- Autonomy levels and human-in-the-loop control
- Real enterprise use cases and their guardrails
- The risks and governance questions agents raise