A Theory of Accountability Boundaries in Agentic Ecosystems
๐กLearn how to prevent governance failures and 'rule debt' when deploying modular agentic AI systems in your organization.
โก 30-Second TL;DR
What Changed
Introduces 'accountability assets' to ensure AI outputs are auditable and assignable.
Why It Matters
The theory provides a framework for enterprises to maintain control and legal compliance as they transition to agentic AI workflows. It helps leaders avoid the pitfalls of unmanaged automated decision-making.
What To Do Next
Audit your current agentic workflows to identify where 'rule debt' exists by mapping decision logic to specific accountability assets.
Key Points
- โขIntroduces 'accountability assets' to ensure AI outputs are auditable and assignable.
- โขDefines three boundary strategies: component, integrated, and dual-track.
- โขIdentifies 'rule debt' as a governance burden when decision rules migrate to agentic environments.
- โขProposes seven propositions linking assembly-cost reductions to boundary strategy and value appropriation.
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Original source: ArXiv AI โ