Make capability measurable
AI systems only become durable when quality, regressions, and risk are visible to the whole team.
Senior AI engineer and engineering leader
Shashwat Jain
I write about building AI systems that survive production: agents, enterprise platforms, evaluation loops, reliability, and the leadership work that turns technical possibility into shipped outcomes.
- Agentic systems
- Enterprise AI
- Platform architecture
- Engineering leadership
- Reliability and evals
- Commerce systems
Latest writing
Field notes on AI systems and engineering judgment
Agent Experience: Better Tools Beat More Connections
Connecting every system to an agent is not the same as making the agent effective. The next leverage point is agent experience: tools designed around intent, context, recovery, and trust.
What Senior AI Engineers Actually Own
The senior part of AI engineering is not knowing every model release. It is owning the product, system, risk, and organizational judgment that turns model capability into durable software.
A Practical Operating Model for Enterprise AI Agents
Enterprise agents need more than tools and prompts. They need ownership, evaluation, permissions, observability, and a rollout model that matches the risk of the workflow.
The Hidden Cost of Abstractions: When Elegant Code Becomes a Performance Trap
Every abstraction is a trade-off. Here's how to identify when your beautiful, clean code is silently destroying performance—and what to do about it.
Operating principles
How I think about senior AI engineering
Treat autonomy as earned
Ship narrow, observable workflows first. Expand autonomy when the system has proved it deserves trust.
Lead through ambiguity
The job is to turn uncertain technology into crisp decisions, accountable ownership, and working software.