AI Systems Architect | Agentic AI Control & Verification | LLM Systems | Founder, EvidenceBound | Technical Advisor
I design and evaluate agentic AI systems where consequential actions must remain authorized, explainable, and verifiable during execution.
I founded EvidenceBound around a runtime-control question:
What should an autonomous system do when the evidence, authority, policy, or human instruction that justified an action changes while the system is already acting?
Capability is not authority. Successful execution is not evidence that an action should have been allowed.
- Runtime authority and delegated-authority boundaries
- Execution evidence, provenance, and verification
- Recovery from uncertain outcomes / UNKNOWN state
- Agent evaluation and fail-closed control mechanisms
- Bringing agentic AI and LLM systems from prototype toward production
A public development challenge built around 10 fixed synthetic control scenarios and raw execution receipts.
Most agent evaluations ask:
Did the agent complete the task?
This challenge also asks:
Should the system still be allowed to continue?
The first independently authored external submissions are open.