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Agentic AI Governance

Governing AI agents that act autonomously — set tasks in motion, call tools, and interact with other systems on a user's behalf.

Current understanding

The corpus treats agentic AI as a step-change in governance, not a continuation of LLM compliance. Governing Agentic AI - some thoughts on best practice makes the framing explicit: agentic systems don't wait for prompts — they anticipate, plan, and act. Existing AI governance was built on a request-response mental model that no longer fits. The Icarus Directive: Fly, But Not Too High! argues the failure mode is altitude: agents promise escape from process labyrinths but the question is who is checking how high they fly. AI Governance: Ethics, Agents & the Human Question surfaces a striking consensus across roles (GC, enterprise AI lead, Big Law partner) on what matters — primarily auditability and human oversight points. The most concrete contribution comes from OpenMandate: Governing AI Agents by Authority, Not Instruction , which proposes runtime enforcement of agent authority — governance by *what an agent is allowed to do*, not by what it was told to do. This is a fundamentally different stance: instead of trusting the prompt, you bound the action surface. How To Train Your Agent looks at the Skills standard as a related move toward declarative agent capability. Permissionless paints the broader picture: when agents work, the world reshapes around them in ways most lawyers aren't watching for. There's a sub-theme on agent risk that overlaps with security: AI Security & Agentic Risk with Rok Popov Ledinski frames this as a discipline of its own — what enterprise security teams need to understand about agent attack surfaces. Recently, the conversation has expanded to include the defensibility of agentic decisions, as highlighted by The Defensibility Question. This source emphasizes the importance of both legal and ethical defensibility in the deployment of agentic systems, raising questions about how these systems can be held accountable for their actions and decisions, particularly in cross-border contexts. The discussion features insights from Helen Fan, a California lawyer and Chief AI Officer, who brings a unique perspective by combining legal practice with technical literacy in agentic systems.

Tensions

Prompt-time vs runtime governance: do you constrain what agents are told, or what they can do? OpenMandate argues the latter; most governance frameworks still focus on the former.
Auditability of agent decisions vs the natural opacity of LLM reasoning. How much explanation is enough for legal accountability?
Who governs agents that interact with other agents? Cross-organizational agent traffic is barely discussed yet.
Most agentic governance writing comes from vendors and consultants. Practising in-house teams are largely silent — possibly because they haven't deployed enough agents to have informed opinions yet.
Defensibility of agentic decisions: how can agentic systems be held accountable for their actions, especially in cross-border legal contexts?
The intersection of legal expertise and technical understanding in shaping agentic governance frameworks remains underexplored.

Mino relevance

Mino's small-focused-agents-one-task-each architecture is *itself* a governance pattern: each agent has narrow scope, limited blast radius, and a verifiable purpose. This is a Mino-native answer to the agentic governance problem and worth saying loudly in messaging — "governance through scope, not through prompts." When selling to firms with mature governance functions, this becomes the lead. Adjacent product opportunity: a per-agent capability manifest (in the OpenMandate spirit) that compliance teams can review before enabling.

Sources

ai-trust-and-output-verification ai-security-and-resilience vibe-coding-and-self-built-tools

Agentic AI Governance

Current understanding

Tensions

Mino relevance

Sources

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