Notes on Building Agentic AI for Indian Enterprises
Practical observations from designing autonomous-agent systems in resource-constrained, multilingual contexts.
Practical observations from designing autonomous-agent systems in resource-constrained, multilingual contexts.
The Setup
Every engineering decision starts with a context. In the last six months, I have shipped systems where the obvious answer turned out to be exactly wrong — and where the unintuitive choice quietly outperformed for months.
The Core Insight
What follows is not a rulebook. It is a list of failure modes I have walked into so you do not have to.
1. The Cheap Win
Most teams over-engineer the model and under-engineer the data pipeline. Reverse that ratio.
2. The Slow Win
The systems that win in production are the ones that fail gracefully — not the ones that have the lowest benchmark error.
More on this in the next post.