I build AI systems.
I've been at this long enough to know where most of them fail.
I'm Rupreet Gujral — an AI and Systems Architect with 25 years spanning enterprise tech, global consulting, and the builder trenches. I've founded startups, shipped patents, and worked inside organisations large enough to know why most AI projects stall. Today I architect agentic systems and LLM infrastructure — and I write about what I learn, without the hype.
Not a ladder. A loop.
"Most careers in tech are a ladder. Mine has been a loop — and it's given me something a straight line never could."
Corporate to startup, practitioner to strategist, founder to architect — and back again. 25 years across enterprise tech, consulting, and building my own ventures has given me a vantage point that's hard to get from one track alone.
I've sat in rooms where AI projects die slow deaths — not because the technology failed, but because the architecture was wrong from day one. I've also been on the other side — founding startups, shipping under pressure, learning what actually works when the demo is over and the budget is real.
Today I architect agentic systems: multi-agent pipelines, LLM infrastructure, cost governance, observability. I write about what I'm building and learning — in real time, from first principles, not from a vendor whitepaper.
From the blog
Practitioner notes on agentic systems, LLM infrastructure, and what I learn building AI in the real world — not the demo version.
Production AI Is an Engineering Discipline, Not a Demo
Over 25 years in enterprise tech, and more intensely in the last couple of years building AI products and advising teams on agentic systems, I've watched the same pattern repeat itself so many times that I can predict it almost word for word. Someone gets pressure from the top to "do something with AI." The conversation starts with the wrong question: which model should we use, GPT or Claude? A model gets picked, a few features get bolted on, it gets tested against a clean, predictable dataset,
Your Agent Has More Access Than Your Junior Developer. That's a Problem.
Agent security, governance, and why the industry is sleepwalking into production without guardrails. I watched an agent bypass its container and write to the host filesystem. Nothing catastrophic happened. It didn't run a destructive script. It didn't exfiltrate data. It just reached outside the boundary it was supposed to stay inside, and wrote files where it had no business writing. A quiet reminder that the container you think is sandboxing your agent may not be doing what you think it's do
Prompts Are Code. Treat Them Like It.
Prompt versioning, model routing, and why your agentic system's inference bill is lying to you. There's a pattern I keep seeing in teams building agentic systems. The architecture is solid. The agents work. The critic-falsifier loop catches real problems. Everything looks good in the demo. Then someone changes a prompt — a single system prompt, one paragraph reworded — and three agents downstream start behaving differently. No error. No schema violation. Just subtly worse outputs that take a w
Where I go deep
Agentic Systems
Multi-agent orchestration, supervisor patterns, memory systems, tool routing. Designing autonomous loops that do real work — not demos.
LLM Infrastructure & Cost Governance
Semantic routing, SLM/LLM hybrid stacks, observability pipelines, token cost reduction. Making AI deployable at scale without the LLM Tax eating your margins.
AI Product Engineering
Spec-driven development, eval frameworks, RAG pipelines, production deployment patterns. The full system — not just the model layer.
Enterprise AI Adoption
Architecture reviews, build-vs-buy frameworks, AI governance, team structure. The decisions that determine whether an AI investment succeeds or stalls.
Tools of the trade
What I'm Thinking About
Working through what "memory" actually means for a long-running agent. Episodic? Semantic? Neither pattern from human cognition maps cleanly.
Enterprise teams are spending 4–6× what they should on inference. The answer isn't a cheaper model — it's a smarter router.
How do you debug an agent that's three hops deep in a tool-use loop? The tracing primitives don't exist yet.
Pick my brain
I do a limited number of 1:1 sessions — on AI architecture, building defensible AI products, technical strategy for non-tech founders, and career decisions in tech. 25 years of context, no slides, no fluff.
Fractional CTO & Tech Advisor
Expert tech review for non-tech founders. Save dev cost before you spend it.
Build an AI Moat, Not a Wrapper
Turn your thin wrapper into a defensible AI asset.
Career Mentorship
Career clarity through honest conversation. 25 years of pattern-matching.