Governing AI agents
What an agent is allowed to do, who answers for it, and the controls that make autonomy something an organisation can run.
6 episodes · 1 h · from bradshaw.cloud: Audio Edition
In this playlist
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Agent Authority
An AI agent needs more than an API token. The framework for assigning a sponsor, mandate, permissions, autonomy and evidence before it can act.
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An AI Agent Is Not a Service Account
An AI agent can act with or without a human in the loop. That needs more than a credential: a named sponsor, bounded mandate, scoped permissions, explicit autonomy and an evidence trail that survives an incident review.
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Agentic API Security
The caller is no longer a person. The identity, task-scoping, reversibility gates, and observability an API estate needs once autonomous agents hold the credentials.
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MCP Security and Governance: The Control Map Enterprises Actually Need
A practical control map for deploying Model Context Protocol safely: identity, permissions, supply chain, approval gates, logging and operational ownership.
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AI Governance Is Enforcement, Not Intent
A principle that cannot point to the moment it says no is a preference. What separates governance from a PDF: controls in the delivery path, a data layer you can actually see into, and gates priced by reversibility.
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Agentic AI Control Planes: The Guardrails That Make Autonomy Operable
A practical architecture for agent guardrails: task boundaries, permissions, approvals, budgets, evaluation, observability and incident response.
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