From Words to Actions
Since late 2022, the global conversation on Artificial Intelligence has fixated on a single capability: generation. We marveled (sometimes uncomfortably) at Large Language Models writing poetry, debugging code, hallucinating legal precedents. Governance frameworks followed suit. They focused on copyright disputes, bias in text output, content moderation. Reasonable concerns, certainly. But the ground is shifting.
We’re leaving the era of passive generation. What comes next might be called the Agentic Frontier, though that term risks sounding more definitive than it probably should.
The Authorization Problem
The new frontier isn’t about what AI says. It’s about what AI does. Agentic AI systems can reason, plan, execute tasks autonomously. Unlike a chatbot waiting for your prompt, an agent receives a goal like “Optimize my supply chain” or “Book the most cost-effective travel” and is granted agency to browse the web, access APIs, authorize transactions. This shift introduces a different category of risk entirely: Authorization and Control.
In the generative era, worst-case scenarios often meant reputation-damaging hallucinations. Embarrassing, yes. Costly, maybe. But in the agentic era? A hallucination could trigger a financial transfer. Delete a database. Disrupt critical infrastructure. Governance frameworks must evolve from content moderation to behavioral containment. We need guardrails that act less like editors and more like circuit breakers, capable of intercepting an autonomous agent’s action before it executes a harmful command.
Shadow AI Agents: The Invisible Workforce
Then there’s the growing phenomenon of what I’d call “Shadow AI Agents” Early 2023 brought isolated incidents. A Samsung engineer inadvertently leaked proprietary code to ChatGPT, for instance. Concerning, but containable. By 2024, the problem exploded. Surveys found 75% of workers using generative AI at work, with 78% bringing their own tools into enterprise environments. Perhaps more troubling: 27.4% of corporate data entered into AI tools was sensitive. Organizations woke up to a reality where roughly half of all AI adoption was happening in the shadows. Unsanctioned, unmonitored.
Now, in 2025, the risk has mutated. It’s no longer just about employees pasting data into ChatGPT. They’re deploying autonomous agents to handle daily work. A software engineer using a coding agent to optimize proprietary algorithms. A sales rep relying on an unvetted negotiator bot to close deals. These shadow agents operate outside corporate governance perimeters, creating invisible systemic risks where decision-making power gets delegated tomachines without human oversight or audit trails. And as nearly every SaaS tool integrates an AI layer, the line between “standard software” and “AI application” has blurred almost beyond Recognition.
The Compliance Splinternet
This technological leap is happening against a backdrop of what might be called Regulatory Fragmentation, though “chaos” isn’t far off. The dream of harmonized global AI standards? It’s not fracturing so much as it never quite existed. The EU’s AI Act set a rigid, risk-based standard in August 2024. The United States, meanwhile, relies on a patchwork of state regulations and voluntary NIST frameworks. Executive orders get issued, then revoked. For multinational organizations, this creates a “compliance splinternet.” A model deployed in Berlin may violate regulations in Beijing or require different disclosures in California. The new frontier of governance requires what we might call “interoperability by design”: building compliance layers that dynamically adapt to whichever jurisdiction the agent operates in.
Is This Data Real?
And finally, there’s the quieter crisis of Data Provenance and Model Collapse. As the internet
floods with AI-generated content, new models risk training on synthetic “garbage,” leading to
what researchers call model collapse: a degradation of intelligence over successive generations. By April 2025, over 74% of newly created webpages contained AI-generated text. Governance must now mandate strict data lineage. We need to move beyond asking “Is this data fair?” to asking “Is this data real?” We need to verify the human origin of information feeding our critical systems.
The novelty of AI “talking” has worn off. The new frontier is defined by AI acting. As organizations rush to deploy autonomous workers, governance professionals must build the infrastructure ensuring these agents remain helpful servants rather than unmanageable risks.
The future isn’t about prompts. It’s about permissions.
