Introduction: Effective multi-agent systems depend on well-designed communication patterns that enable agents to collaborate, share context, and coordinate actions. This comprehensive guide explores AutoGen’s communication mechanisms, from two-agent conversations and group chats to nested conversations and sequential workflows. After implementing complex agent orchestration for enterprise applications, I’ve found that communication pattern selection significantly impacts system […]
Read more →Author: Nithin Mohan TK
Cloud LLMOps: Mastering AWS Bedrock, Azure OpenAI, and Google Vertex AI
Deep dive into cloud LLMOps platforms. Compare AWS Bedrock, Azure OpenAI Service, and Google Vertex AI with practical implementations, RAG patterns, and enterprise considerations.
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We’ve reached an inflection point in artificial intelligence that most organizations haven’t fully grasped yet. While the world obsesses over chatbots and prompt engineering, a more profound shift is quietly reshaping how software systems operate. Agentic AI—autonomous systems capable of reasoning, planning, and executing multi-step tasks without constant human intervention—represents the most significant architectural transformation […]
Read more →Building Multi-Agent AI Systems with Microsoft AutoGen: A Comprehensive Introduction to Agentic Development
After building multi-agent systems with Microsoft AutoGen across enterprise deployments, I’ve learned that AutoGen isn’t just another LLM framework—it’s a paradigm shift in how we build autonomous AI systems. This comprehensive introduction covers everything you need to know to start building production multi-agent applications. 1. What Is Microsoft AutoGen? AutoGen is Microsoft’s open-source framework for […]
Read more →Cloud-Native AI Architecture: Patterns for Scalable LLM Applications
Cloud-Native AI Architecture: Patterns for Scalable LLM Applications Expert Guide to Building Scalable, Resilient AI Applications in the Cloud I’ve architected AI systems that handle millions of requests per day, scale from zero to thousands of concurrent users, and maintain 99.99% uptime. Cloud-native architecture isn’t just about deploying to the cloud—it’s about designing systems that […]
Read more →MLOps vs LLMOps: A Complete Guide to Operationalizing AI at Enterprise Scale
Understand the critical differences between MLOps and LLMOps. Learn prompt management, evaluation pipelines, cost tracking, and CI/CD patterns for LLM applications in production.
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