Gary Chigaros

Researching and building systems that make organizational rules executable.

My work focuses on governance, autonomous agents, and the gap between what institutions say their rules are and what their systems actually enforce.

Organizations spend enormous energy defining rules and almost none ensuring those rules are enforced consistently. Approvals, permissions, spending limits, and operational constraints exist almost entirely at the social layer. They live in documents, email threads, and the assumptions of whoever configured a dashboard. When an action executes, the system executing it rarely verifies whether the organization's actual rules were satisfied.

Artificial intelligence agents make this gap critical. Once an agent has access to tools or credentials, it stops being an assistant and starts being an operator. At that point, enforcement cannot live in policy documents or after-the-fact review. It has to happen at execution.

OrgForge

Cryptographic governance enforcement protocol

A system that enforces organizational rules at the moment actions occur, using cryptographic authorization instead of procedural trust.

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OrgForge: Deterministic Authorization Systems for Organizational Governance

Introduces a cryptographic governance enforcement protocol that evaluates signed intents against machine-readable organizational constitutions and produces signed authorization artifacts. Covers the five-stage deterministic pipeline, actor model, OrgSpec format, and gateway pattern across financial, AI agent, and enterprise domains.

The New Standard of Intelligence: Rote Recall, Artificial Intelligence, and the Urgent Redefinition of Human Cognitive Value

Advances the claim that in an AI-augmented epistemic environment, rote recall no longer functions as a valid proxy for human intelligence, and that institutional evaluation systems must reorient toward higher-order capacities resistant to algorithmic substitution.

Governing the Ungoverned

Why crypto's governance rulebooks were not built for AI, and what to do about it. Traces the structural mismatch between human-speed governance and machine-speed autonomous agents through the lens of the 2022 Beanstalk exploit.