Diary of an AI Architect

Diary of an AI Architect

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How to actually build an agentic RAG solution for the enterprise

Getting the right evidence to your agents, from documents to live business systems. A practical six-stage architecture.
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How I’d update my AI CoE for an agentic enterprise
The same three pillars, with clearer rules for what agents can do, when humans must approve, and who is accountable.
 • Anurag Karuparti
How to build a trusted intelligence layer for reliable AI agents
From context engineering to trusted intelligence: a practical architecture for relevant evidence, governed access and human control.
 • Anurag Karuparti
Why AI agents may need decision models, not more LLM calls. Exploring Jev for your AI architecture.
Where decision models fit in your agentic AI architecture, and why code should still make the final call.
 • Anurag Karuparti
10 ways to stop wasting tokens and build smarter AI workflows
From tokenmaxxing to valuemaxxing: a short field guide, with a practical example for every shift.
 • Anurag Karuparti
GPT-6 Astra vs Claude Fable 5.1: what’s different and how I’d use them in production
Beyond the benchmarks: a practical guide to choosing the right model for each task, setting autonomy limits, and measuring the cost of getting it right.
 • Anurag Karuparti
Why model optionality is useless if your business context can’t move with you
A guest essay by Rohan Kodialam, Founder & CEO of Sphinx AI
 • Anurag Karuparti and Rohan Kodialam
Why most AI agents fail in production. Building strong evals doesn’t necessarily mean a successful business outcome
Strong evals can prove your agent works. The Outcome Translation Layer proves whether it creates business value.
 • Anurag Karuparti
How I use agents to automate my content creation pipeline with Runner, Notion, Google Sheets, Google Docs, and Claude skills
I’m a part-time creator building a system so I never have to “find time”
 • Anurag Karuparti
Why graph engineering will be the next essential AI skill. Difference between prompt, context, harness, loop and graph engineering.
Prompt engineering controls one model call. Graph engineering orchestrates agents, evaluators, deterministic steps, and humans toward a reliable…
 • Anurag Karuparti
Diary of an AI Architect
Diary of an AI Architect
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