• views: 200
• date: 7 July 2026
• posted by: voska89

Explain the agent loop (reason, act, observe) and when an agent beats a script or a single LLM call.
Build conversation-driven agents and group chats in AutoGen, with safe code execution and human-in-the-loop.
Diagnose and fix the common agent failure modes: loops, drift, and hallucinated tool calls.
Control agent cost with model tiering, prompt trimming, and caching — and measure the savings.
Build single agents and multi-agent crews in CrewAI with clear roles, tasks, and processes.
Equip agents with custom tools, real API and database access, persistent memory, and RAG over your documents.
Deploy an agent as an observable API service with secrets, least-privilege access, and logging.
Choose the right architecture — single agent, crew, or group chat — for a given problem.
Add guardrails, step limits, timeouts, and output validation to keep agents safe and predictable.
Evaluate non-deterministic agents using golden tasks, an LLM-as-judge, and human review.
Schedule and trigger agents to runApply reusable agent design patterns and avoid the anti-patterns that waste we automatically from time or events, not by hand.

Requirements

Comfortable writing basic Python (functions, packages, virtual environments).
An API key for an LLM provider (we cover safe setup); a little credit for hands-on runs.
No prior AI-agent experience needed — we start from the agent loop itself.

Description

This course contains the use of artificial intelligence. AI tools were used only to rephrase and fix grammar in the course

Description

and supporting text. AI agents are crossing from demos into production, and the gap between watching one and building one that you'd trust on a schedule is exactly what this course closes. You will build real, working agents and multi-agent systems with the two frameworks the industry has settled on — CrewAI for role-and-task crews, and AutoGen for conversation-driven agents — and learn the model that underlies both, so you're never locked into one tool.This is a hands-on, build-along course for software engineers, data scientists, solution architects, and technical analysts who want to ship agents, not just talk about them. No prior agent experience is assumed — you start from the agent loop itself — but you should be comfortable writing basic Python. By the end you will have built four projects: a research crew, a multi-agent support system, a code-running data-analysis agent, and a deployed, observable multi-agent service.We go well past 'hello agent'. You'll give agents real tools, databases, memory, and retrieval over your own documents; then you'll do what most tutorials skip — make them reliable. You'll diagnose why agents loop, drift, and invent tool calls, add guardrails and cost caps, and actually evaluate whether an agent worked. Finally you'll deploy one: behind an API, with secrets handled, observability on, and a schedule firing it automatically.Every concept is taught from the operations floor, with both dollar and rupee cost examples, by Ganesh Ravikumar, who has spent 20+ years putting automation — and now agents — into real, regulated operations where a runaway loop or a surprise bill is a genuine problem. This is the course I wish existed when I shipped my first agent into production. Enroll now and build agents that actually do the work.
Software engineers who want to build and ship AI agents,Data scientists adding agentic workflows to their toolkit,Solution architects designing multi-agent systems,Technical analysts and builders automating real work,Engineering leads evaluating agents for production

Homepage

https://www.udemy.com/course/mastering-ai-agents-with-autogen-crewai/

   
   
   

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