Designing AI agents and systems where reliability, evaluation, human review, and traceability are part of the architecture rather than afterthoughts.
Current thesis
The core problem is no longer making agents look autonomous in a demo. It is making them dependable enough that a human can understand their state, trust the right parts, and interrupt or correct them without paying a coordination tax.
2026 perspective
In 2026, the practical frontier is workflow reliability. Agents are becoming useful often enough that standards, identity, evaluation, and escalation design matter more than impressive one-shot completions.
Human factors lens
Oversight fails if review costs more than redoing the work. Good interfaces reduce cognitive load by foregrounding evidence, changes, uncertainty, and consequence before they foreground prose.
The 2025 Stanford AI Index reported that responsible-AI evaluation remains uneven even as documented incidents continue to accumulate. At the same time, NIST's February 2026 AI Agent Standards Initiative, draft benchmark guidance for language-model evaluation, and ongoing ARIA field-testing work all point in the same direction: the next serious step is comparable, operationally meaningful evaluation. My view is that agent progress is now gated less by raw fluency and more by control surfaces such as permissions, observability, interoperability, and human escalation.
Give agents explicit task scopes, tool budgets, and approval gates instead of vague permission to 'go solve it'.
Persist the plan, retrieved context, tool outputs, and meaningful diffs so reviewers can reconstruct what happened quickly.
Escalation should preserve context and evidence rather than forcing a human to restart the task from scratch.
Compare agents against strong non-agent baselines, mixed-initiative workflows, and failure-handling variants instead of weaker prompts alone.
Measure variance across repeated runs, near-miss behavior, and whether recovery steps converge or compound error.
Score whether retrieved or cited material actually supports the action or conclusion the agent produced.
Track time-to-review, override quality, and whether humans catch the failures that matter.
Test permission boundaries, escalation behavior, and refusal quality when requests conflict with policy or evidence.
Announced on February 17, 2026, with an explicit focus on identity, interaction protocols, safety, and trustworthy multi-agent interoperability.
A February 10, 2026 public draft that pushes benchmark design toward automation, comparability, and sound measurement practice.
Field-testing and evaluation work that treats trustworthy AI as a measurable systems problem rather than a purely abstract principle.
Useful for tracking the gap between rapid adoption and slower uptake of serious responsible-AI evaluation practice.