Author: Tejasvi Sah

There is a question that comes up in almost every conversation about AI agent development. It is within the first ten minutes, and almost always receives an unsatisfying response: how much does it cost? The answer is unsatisfying, but it is not that the people giving it are being evasive. AI agent development cost varies widely from any other category of…

AI is the cat that’s now out of the bag, and everyone wants to see if it’s all just hype or the real deal. The industry has burned billions of dollars on projects like Metaverse, which can now be considered more or less failed.  The next frontier feels like AI, with no one really sure how well it is going. Students are worried about jobs, and businesses are wondering if…

Most enterprise Agentic AI projects follow a predictable arc. There’s excitement at the proof-of-concept stage, confidence, and then a quiet reckoning in production when the system that worked on curated test data starts producing inconsistent and irrelevant outputs in the real world.  The root cause is almost always the same. A system that can find relevant documents…

Artificial Intelligence is no longer limited to chatbots, predictive analytics, or workflow automation. Businesses are now entering a new era of intelligent enterprise operations. AI systems include reasoning, planning, learning, adapting, and executing tasks across complex business environments.  Unlike traditional automation tools that follow predefined rules, Agentic AI introduces intelligent decision-making into enterprise workflows. These systems can…

There is a gap at the centre of most enterprise AI deployments that nobody talks about clearly enough. On one side, you have powerful large language models capable of sophisticated reasoning, content generation, and analysis. On the other hand, you have the systems where your actual business data lives. Between these two sides, there is frequently no reliable bridge.…

Most Retailers Waste Their First Computer Vision Investment Not because the technology fails them. Because they deploy it in the wrong place, to solve the wrong problem, with no clear line to the P&L. The pattern repeats: a pilot goes live in a flagship store, a few cameras go up, a dashboard gets built, and…

Most of today’s AI is reactive. You ask a question, it gives an answer. You request a summary, it provides one. It’s a powerful tool. But it waits for your command.  Agentic AI is different. It doesn’t just respond – it executes. It doesn’t just assist – it owns the entire project.  Traditional AI =…

Most companies go into their first AI project with a number in their head, usually wrong by a factor of two or three, in either direction. The vendor proposals don’t help. They arrive with wide ranges, vague line items, and no explanation of what drives the difference between the low end and the high end.…

Most conversations about AI in business eventually arrive at the same question. Not “should we use AI?” That debate is largely settled. The question that actually matters now is “how do we build AI that does something useful rather than just impressively answering questions?”  That’s the gap LangChain was built to close.  A large language model on its own is extraordinarily…