AI systems that survive production
Most AI agents in production are demos with extra steps. I help teams build the ones that aren't.
I'm Bhanu Chaddha. I work with teams putting AI agents and RAG into production: the parts that hold up once the demo is over.
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- posts published
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- Docker, Step by Step
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- Agents in Production
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- The Delivery Layer
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- The AI Agent Playbook
Fifteen years building distributed systems before AI is why the production critique lands. About me
What I write about
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View all →The Decision You Didn't Make
Most project damage comes from choices nobody recorded making. A bad call can be reviewed and reversed. An unmade decision just accrues until it breaks.
Part 8 - Your Tool Schema Is a Prompt, Not an API
A tool schema is read by the model on every call, not once by an engineer. Design the contract, the errors, and the catalog size, because MCP will not.
Part 7 - The Pipeline That Feeds Your Agent Is Broken
Production AI data pipelines have four jobs: extraction, normalization, enrichment, indexing. Most teams build only the last one and ship stale answers.
Part 6 - Most Production RAG Is Quietly Wrong
Part 6 of a series on what actually goes into production agentic systems.
Working on something in this space? I'm happy to talk it through.