Aishwarya Naresh Reganti

Day 9: Real-World Agentic Systems

This article showcases real-world examples of agentic AI systems – NotebookLM, Perplexity, and DeepResearch – and how they utilize core components like tools, planning, RAG, and me…

Day 8: Multi-Agent AI Systems

This article discusses multi-agent AI systems, explaining they can be beneficial for complex tasks requiring parallelization, specialization, or diverse perspectives. While promisi…

Day 7: Memory in Agents

This article explains that AI agents, unlike humans, don’t inherently have memory and require it to be intentionally built into their systems. It highlights the importance of memor…

Day 6: Planning in Agents + Reasoning Models

Today’s article focuses on the critical role of planning in building effective agentic AI systems. While agents can use tools and retrieve information, they often fail because they…

Day 5: What Is MCP and Why Should You Care?

This article introduces the Model Context Protocol (MCP), a standardized format for providing LLMs with all necessary information – tasks, tools, data, and memory – in a structured…

Day 4: What Is RAG, and What Does It Mean to Make It Agentic?

This article explains Retrieval-Augmented Generation (RAG), a key technique for enterprise AI applications. RAG enhances Large Language Models (LLMs) by retrieving relevant data fr…