Aishwarya Naresh Reganti

What Building AI in Regulated Industries Actually Looks Like

This article summarizes a discussion with André Kerr, Director of AI Product at Amex, about implementing AI within a highly regulated financial industry. Key takeaways include prio…

What Product Leaders at Netflix and Meta Actually Think About AI Product Management

This article summarizes a discussion with product leaders from Netflix and Meta about the emerging role of "AI Product Manager." They differentiate between products *with* AI featu…

From 6,000 Lines of Code in Two Days to Getting Acquired by IBM- The Langflow Story

Langflow, a rapidly growing open-source low-code AI tool, was recently acquired by IBM through DataStax, despite being built by a small, self-funded team in Brazil. The founders le…

How to Choose the Right AI Models For Your Use-Case?

This article argues that choosing the right AI model isn’t a simple decision, but a multi-dimensional process involving enterprise constraints, model type, and use-case specifics. …

Day 10: AI Agent Lessons and What’s Ahead

This article summarizes a 10-day series on building agentic AI applications, highlighting that successful implementation hinges on observability (tracking agent actions) and rigoro…

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…