Aishwarya Naresh Reganti

Evals Are NOT All You Need

This article argues that focusing solely on “evals” (traditional AI model evaluation) is insufficient for ensuring AI product quality. Instead, teams should build a continuous “fly…

Chai & AI Gems: January Edition

This article summarizes discussions from a community of AI practitioners, revealing a shift from exploring *if* to implement AI, to tackling the challenges of *how* to successfully…

The State of Applied AI 2025 Report

The “State of Applied AI 2025” report highlights that 2025 saw a shift from focusing on AI model benchmarks to building *reliable* AI systems that function effectively in real-worl…

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…