Most discussions of AI governance still orbit around systems that advise, recommend, score, or generate. The legal and policy frameworks built around them assume a human moment. A pause. Someone reads the output and decides what to do next. Agentic AI quietly removes that pause. It interprets goals, decomposes them, invokes tools, adapts, and proceeds. Often faster than oversight can keep up. This is not a slight variation of existing governance problems. It is a major shift.
This paper argues that agentic systems convert risk from flawed data into potentially flawed behavior. That sounds abstract until you watch a system authorize payments, send instructions, or change records while everyone involved insists, they are “still in control.” Well guess what? Control is a slippery word.
Why Agentic AI Is Not Just “More AI Governance”
AI governance frameworks to date rest on familiar pillars. Risk assessment, transparency, accountability, documentation, human oversight. They work reasonably well when the system’s role ends at producing an output. When the system begins acting, those same pillars become unstable.
Agentic AI systems operate across time. They are making plans. They adapt. They call tools. This is clearly a shift from advice risk to agency risk, and that framing is hard to unwind once you notice it. Delegated authority is clear and understandable. Unfortunately, apparent authority emerges accidentally. Oversight drifts from active to symbolic. Logs, if they exist, might be scattered across vendors and platforms. Having read a few governance documents keep thinking about how they often promise “human-in-the-loop or human-oversight” supervision. On paper, it looks reassuring. In practice, humans in the loop become reviewers of exceptions, not decisions. Then reviewers of summaries. Then reviewers of incident reports. By the time something goes wrong, the loop is mostly a memory.
The problem is not malice or even negligence in the ordinary sense. It is structural. Agentic systems are optimized to act. Governance structures are optimized to review.
Let’s take a construction dispute that involves Agentic AI as a thought experiment. Consider a mid-sized commercial construction project. Nothing exotic. Office building, urban site, multiple trades, a tight schedule, and the usual pressure to finish “just a little faster than planned.”
The general contractor uses a project management platform that includes an AI Agent-based scheduling feature. At first, the system only analyzes delays and suggests adjustments. Later, to save time, the contractor enables an option allowing the system to automatically update the schedule when certain conditions are met. Let’s say it’s just minor re-sequencing. Nothing that, in theory, changes scope or price. For several weeks, the system made minor adjustments. Activities move by a day here, two days there. The updates are logged. No formal change orders are issued. No one objects. The project moves on. Eventually, subcontractors work longer hours and out of sequence. Crews overlap. Costs increase. One subcontractor submits a claim for constructive acceleration and delay damages, arguing that it was effectively directed to perform work faster and differently than planned. The general contractor responds no directive was ever issued. The schedule updates, it argues, were automated. No human ordered acceleration. The owner points to the contract, noting that no change of order exists, and no written instructions were given.
Legal Fault Lines That Appear Only When Systems Act
Several legal issues emerge that general AI governance discussions gloss over. First, an issue related to delegated authority. Agentic AI forces parties to confront how much authority was granted versus how much authority appeared to exist. Apparent authority doctrines were built for human agents. Second, the idea of foreseeability. When a system is designed to adapt and optimize, its harmful actions are rarely surprising in hindsight. The research highlights how foreseeability arguments will dominate disputes, with parties arguing not about whether harm occurred, but whether it was an expected by product of autonomy. Third, oversight failure. Governance frameworks often treat oversight as a checkbox. Agentic systems turn oversight into a technical capability question. Could a human intervene in time? Did they even know intervention was needed? These questions feel factual, yet they carry moral weight. Fourth, evidentiary integrity. Logs that are sufficient for debugging are often insufficient for adjudication. The difference matters. Arbitration, especially in construction, depends on reconstructible histories. Agentic systems prefer the present.
Why Existing Governance Frameworks Strain
The major AI governance frameworks remain essential. Risk-based approaches, lifecycle management, and accountability standards all apply. Agentic AI governance is not a rejection of existing frameworks, but an intensification of them. Still, something new emerges. Governance must now address authority boundaries, real-time control, and audit-grade action trails. These are not philosophical additions. They are operational burdens. They cost money. They slow deployment. They also prevent disputes from being metastasized. I have heard people describe these controls as innovation killers. That always strikes me as backwards. Unbounded autonomy is not innovation. It is deferred litigation.
Human Oversight as Reality, Not Theater
The phrase “human oversight” appears everywhere. It is comforting. It is also dangerously vague. In agentic contexts, oversight must be defined in terms of latency, authority, and visibility. Oversight that arrives after harm is commentary, not control. What is needed is some form of tiered oversight tied to risk, not hope. Low-risk actions may proceed autonomously. High-risk actions must pause. Emergency actions must stop. These distinctions sound obvious until someone has to implement them. Construction disputes illustrate this vividly. Cash flow delays triggered by automated agentic AI systems can collapse subcontractors before any human reviews a dashboard.
Conclusion
Agentic AI governance is not about smarter rules. It is about governing action itself. Systems that plan and act create legal and practical problems that output-focused governance simply does not anticipate. If governance frameworks fail to evolve and take into account Agentic AI, mediators, arbitrators, and courts will fill the gaps. They will do so inconsistently, case by case, with hindsight as their guide. That is not a governance strategy. It is an abdication.
The uncomfortable truth is that agentic AI demands more discipline, not less. Clear authority boundaries. Real oversight. Logs designed for disputes, not demos. Without these, the question will not be whether disputes arise. It will be how many, and how bitter.
Endnotes
Editorial Note: This article was originally published in Conciliation Quarterly, Fall 2002, Vol. 21, No. 4. as "Peace and the Internet. The article has now been updated and expanded by...
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