Building AI Agents & Agentic AI System via Microsoft Autogen
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Unclaimed ProfileIn the rapidly evolving landscape of generative AI, the shift from simple prompt-response interactions to complex, multi-agent systems is the next frontier for developers. This course provides a comprehensive exploration of Microsoft AutoGen, the premier framework for building orchestrations of autonomous agents. Students will move beyond static LLM implementations and learn how to construct collaborative teams of agents that can perform iterative reasoning, execute code, and solve multifaceted problems through communication. The curriculum dives deep into the architecture of AutoGen, explaining how to define agent roles, set up conversation patterns, and manage memory across agent hierarchies. By focusing on practical, hands-on implementation, learners will bridge the gap between theoretical AI models and production-ready automation. Whether you are aiming to build sophisticated research assistants, automated software development pipelines, or complex data analysis tools, this course equips you with the technical architectural knowledge to create agentic workflows that are both reliable and scalable in a modern enterprise environment.
About the creator
KRISHAI Technologies Private Limited
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KRISHAI Technologies Private Limited, spearheaded by industry expert Krish Naik, is a pioneering educational and technology solutions hub dedicated to democratizing advanced artificial intelligence training. With a specialized focus on Generative AI, Retrieval-Augmented Generation (RAG), and Agentic AI frameworks, KRISHAI Technologies translates complex algorithmic theory into practical, production-ready systems. Their comprehensive curriculum spans critical modern technologies such as LangChain, LangGraph, LangSmith, Microsoft AutoGen, n8n, and the Model Context Protocol (MCP). The teaching methodology centers on project-based, end-to-end implementation, steering clear of pure academic lectures in favor of real-world deployment challenges. By guiding learners through building functional multi-agent systems, automated workflows,…Show more
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Program Overview
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Learning format
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Price may change · updated within 1–2 weeks
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What You'll Learn
- Master the architectural design of multi-agent systems using Microsoft AutoGen's ConversableAgent and GroupChat components.
- Implement specialized agents capable of executing code, performing web searches, and analyzing complex datasets autonomously.
- Configure human-in-the-loop interaction patterns to maintain control over critical decision-making processes within AI workflows.
- Design and optimize custom conversation orchestration patterns including linear, hierarchical, and dynamic group chat configurations.
- Develop robust agentic workflows that leverage external APIs and document retrieval systems to enhance model contextual awareness.
- Implement error-handling and feedback loops that allow agents to self-correct during complex code generation and problem-solving tasks.
- Scale agentic systems by integrating state management and persistence layers to ensure consistency across extended sessions.
Best For
- Software engineers looking to incorporate multi-agent orchestration into existing LLM applications.
- AI developers specializing in automation and autonomous system design.
- Data scientists who want to build modular agents for data processing pipelines.
- Technical architects aiming to implement sophisticated agentic frameworks.
Not For
- Absolute beginners with no prior knowledge of Python or LLM fundamentals.
- Non-technical business professionals looking for a high-level overview of AI without implementation details.
- Developers focused exclusively on prompt engineering without interest in architectural framework coding.
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