AgentBuild
The hardest part of shipping AI isn't the model. It's everything around it.
SANDIPAN BHAUMIK
01
What can you expect
The technical side, explained plainly
Why production AI systems fail — and what the architecture actually needs to look like
The organisation side, named honestly
The governance gaps, data debt, and decision-making patterns that kill AI projects before they ship
A community working through it together
Practitioners across industries — navigating the same problems
Because the gap between AI demo and AI production is enormous — and almost nobody talks about it honestly.
The Problem
You've seen the demo. You know it's not that simple.
Every enterprise AI project starts the same way. A compelling proof of concept. Board-level excitement. A mandate to move fast. Then reality arrives — incomplete data, unclear ownership, evaluation frameworks that don’t exist, infrastructure that wasn’t designed for this. Most content tells you how to build the model. Almost none of it tells you how to survive the organisation. That’s the gap AgentBuild exists to close.
The Approach
Complex ideas, translated into decisions you can actually make.
The research exists. The patterns are documented somewhere. But they’re buried in academic papers, vendor whitepapers, and conversations that happen behind closed doors at Tier 1 institutions. AgentBuild brings that thinking into the open — in plain language, with real context, built for the people responsible for making AI work in the real world.
AgentBuild exists because enterprise AI deserves honest, plain-language thinking — not more hype.
Built for the person holding the hardest brief in their organisation.
You’re the one who has to make AI actually work. Ship it, sustain it, defend it when it underperforms, and fix it when the data isn’t what anyone thought it was. AgentBuild was founded out of a simple frustration: the resources that exist for enterprise AI practitioners don’t match the complexity of what they’re actually dealing with. The technical content is either too shallow or too academic. The organisational challenges are barely discussed at all. This community exists to change that. To take the hardest problems in production AI — technical, organisational, and everything in between — and work through them together, in plain language, without pretending they’re simpler than they are.