Ask Nikhil

A simple AI agent can plan a weekend. A family trip across three countries needs something with a plan, a team and a notebook. Wayfinder mid-run. Seven specialists are searching at once, and the trip brief the agent settled on is on the right. The short version - Simple AI agents work like one person doing everything from memory: think, do one thing, look at the result, repeat. That's fine for small jobs and gets messy on big ones. - Deep agents work like a small agency: a co

If you're building anything with LLMs in production, you eventually hit the same wall: you need to know what your app is actually doing — what prompts went out, what came back, how much it cost, and why it broke at 2am. That's observability, and two names keep coming up: Langfuse and MLflow. Before comparing them, it's worth laying out what you're actually trying to observe — because observability means something different at each layer of a modern AI system. LLM observabilit

Every time someone mentions running an LLM in production, the first instinct is to reach for a GPU instance. A100s, H100s, L40s — the cost spirals fast, and for most enterprise inference workloads, you're paying for compute you don't need. I've been running inference experiments on OCI Ampere A1 instances for a while now, and the results keep surprising me in a good way. DeepSeek-R1 7B is the model that changed the conversation. Released by DeepSeek AI, this reasoning-optimis