Key takeaways:
- Enterprise AI Success: It’s not just about models—it’s about robust systems, governance and infrastructure.
- RAG & Multi-Agent Systems: Focus on governance-first retrieval, precision, auditability and operational rigor.
- GPU Optimization: Balance speed, cost and trust with batching, KV caching and memory bandwidth efficiency.
- Real-World Learnings: Customer deployments reveal key challenges in orchestration, governance and unit economics.
- Teamwork at Dell: Enterprise AI is built by collaborative teams across engineering, design and product leadership.
- Guidance for Engineers: Be bilingual in ML and systems, prioritize safety and observability and build end-to-end solutions.
Enterprise AI is built by teams.
At Dell, that truth shows up every day. Engineers, architects, designers and product leaders work side by side, turning early experiments into reliable systems used across industries. Sagar is one of the people pushing the frontier technically, and he calls out that none of this happens without the coworkers around him—the teammates who obsess over retrieval edge cases, GPU kernels, audit logs, throughput numbers and experience design.
Together, they’ve learned that most conversations about enterprise AI begin in the wrong place. They start with the model. But the model is rarely what breaks first. The system is.
That’s why, before diving into architecture, Sagar likes to reset expectations with a myth he often hears.
Sagar tackles a misconception that misleads entire organizations:
“Pick the right model and everything else just works.”
In reality, what cracks under pressure isn’t the model—it’s the surrounding infrastructure: retrieval quality, governance enforcement, cost-per-query and observability. AI doesn’t fail because the model is weak; it fails because the engineering isn’t real.
And understanding that is the gateway into how Dell’s teams build AI that actually survives enterprise scale.
Designing for RAG and multi-agent systems at scale
Sagar’s approach begins with a system contract—latency budgets, quality expectations, failure behaviors and audit requirements. It’s the opposite of model-first thinking; it’s operations backward design.
His RAG foundations include governance-first retrieval, precision chunking and reranking, measurable evaluation and token efficiency as a KPI.
For multiagent systems, he treats tool calling like production software, not a chain-of-thought experiment: least-privilege access, observability, traces and failure testing. The goal isn’t “more agents” – it’s systems that can plan, act and be audited end to end.
GPU systems, real-time inference & the balance of speed, Cost, and trust
Inference is a three-way tradeoff between speed, cost and trust—and optimizing one at the expense of the others doesn’t scale.
Sagar and the Dell engineering teams focus on routing simple queries to small models, maximizing GPU utilization with batching, KV caching and quantization, and treating memory bandwidth as the real bottleneck.
And because enterprise AI must be explainable, retrieval provenance, tool-call traces, audit logs and reproducibility are non-negotiable.
Field learnings that shape Dell’s roadmap
Real-world customer deployments reveal where systems break first:
orchestration, governance and unit economics.
Those signals shape Dell’s priorities—validated patterns, embedded observability and rapidly advancing hybrid and on-prem AI systems.
Guidance for engineers building enterprise-scale AI
Sagar encourages engineers to be bilingual in ML and systems, treat safety and observability as first-class features and build end-to-end—from data through retrieval, evaluation and deployment. That’s how a good demo becomes a real system.
Why Dell is the place to build the future of enterprise AI
Behind every AI system Dell ships is a team: engineers solving GPU scheduling puzzles, architects defining governance boundaries, designers shaping human-AI workflows, PMs turning customer chaos into clarity and researchers pushing what’s possible. Sagar’s work sits on top of that collective foundation.
Dell is building the future of enterprise AI not because of one person, but because of teams of people collaborating across infrastructure, hardware, software and applied engineering to make AI real at scale.
And whether you’re early in your career or already deep in your craft, this is a place where you can contribute from day one.
The future of enterprise AI is being built here by teams who care deeply about getting it right. And there’s room for those ready to help shape what comes next.