Research

AI infrastructure, observability, and compute systems research.

A structured index of published work, frameworks, and active research arcs — from silicon observability to autonomous agent risk.

The Observability Stack Read flagship essay

Featured series

Observability for Frontier AI Systems

A connected body of work mapping silicon bring-up observability onto agent safety: tracing, replay, escalation checkpoints, maturity levels, and eval infrastructure.

Flagship essay Agent debuggability

Index

Filter the research surface.

PublishedCompute Economics

The Cost of Usable Intelligence

A framework for measuring the cost of useful AI output under latency, reliability, safety, and quality constraints.

PublishedFrontier AI Systems

Observability is an underbuilt safety primitive for frontier AI systems

As AI agents move from answering to acting, safety depends on whether we can trace, replay, and debug their behavior.

PublishedFrontier AI Systems

Debuggability for autonomous agents

A public thesis connecting silicon bring-up observability to agent safety, eval infrastructure, replay, and root-cause workflows.

PlannedAutonomous Agent Risk

AI agents as infrastructure risk

Planned framework: tool-using agents as infrastructure-risk participants with reliability contracts.

PlannedAI Infrastructure

The next AI infrastructure bottleneck is observability

Planned framework: observability, not raw compute, as the binding constraint on agentic workloads.

DraftQuantum-Classical

Quantum computing needs an accelerated classical control plane

Draft memo: quantum systems depend on fast, observable classical orchestration.

PlannedAutonomous Agent Risk

A taxonomy of autonomous agent failure modes

Planned taxonomy connecting agent behavior failures to the instrumentation that would catch them.