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AI Agent Governance & Operating Model Design
Expert Service
Shivanath Devinarayanan
Design the context, permission, memory, and audit-trail architecture that turns AI agent pilots into governed, production-grade digital labor.
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Common Challenges
- • AI agent pilots stall before reaching production because no one owns context, permissions, or audit trails
- • Executives have no structured way to review which agent decisions to trust
- • Context and memory sprawl across tools with no single source of truth
- • No feedback loop connects agent output back to the people accountable for it
Our Approach
1. Agent Registry & Audit-Trail Design
Name every agent in production, the actions it may take, the identity it uses, and the trail it leaves. MuleSoft's 2026 Connectivity Benchmark found 50% of agents already run in isolated silos. A registry is the control surface, not a spreadsheet after the fact.
2. Context, Permission & Memory Governance Audit
Review how agents get context and memory today, and who has root on that store. Industry AI TRiSM estimates put documented memory policy under 20% of production generative-AI estates. If the vendor disappears on a Friday, the weekend test is whether the workflow still runs.
3. Delegate, Review, Own workshops
For each production agent: what it handles, who reviews output by name, and who owns the outcome when it is wrong. Cisco's research found organizations with a dedicated AI operations owner are 4x more likely to move a pilot into production.
Why do most agent pilots stall?
MIT NANDA research covering 300 public deployments found 95% of enterprise generative-AI pilots fail to deliver measurable business value. A March 2026 survey of 650 technology leaders found 78% have at least one active agent pilot and only 14% have scaled an agent to organization-wide operation. The average stall comes at 4.7 months. Gartner has said more than 40% of agentic projects will be canceled by the end of 2027.
BCG has repeated the same split since 2020: 10% of AI success is algorithms, 20% is technology and data, 70% is people, process, and change. Most estates invert the spend. Digital labor is not a model purchase. It is Delegate, Review, Own: identity and scope before an agent touches a deliverable, a human who can override, and a named owner when the output is wrong.
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