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.
Writing
Essays on AI systems, architecture, and leadership.
Long-form notes for builders and leaders turning emerging capability into reliable software: agents, evaluation, enterprise platforms, distributed systems, and the judgment behind technical decisions.
- AI Agents
- AI Engineering
- Agent Experience
- Architecture
- Distributed Systems
- Engineering Judgment
- Enterprise AI
- Leadership
- Operating Model
- Performance
- Product Judgment
- Reliability
- Runtime Design
- Tooling
- WebAssembly
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.
Why I Fell in Love with WebAssembly (and You Should Too)
WebAssembly isn't just another compile target—it's a paradigm shift that's quietly revolutionizing how we think about performance, portability, and the future of computing.
Mental Models for Distributed Systems: Thinking Beyond Single Machines
Distributed systems break our intuitions about computing. Here are the mental models that help me reason about complex, distributed architectures without losing my sanity.