Vision
Vision
The Accountability Inversion
The assumption is straightforward: better models unlock broader autonomy, and the most technically adventurous organizations lead agent adoption. The evidence points somewhere unexpected. Heavily regulated, accountability-dense environments are reaching production first. Compliance monitoring, clinical documentation, financial back-office. The places you'd expect to move slowest are moving fastest. The gating factor for agent deployment at scale turns out to be whether anyone can later prove who authorized what, within what limits, and where the records live. Banks solved a version of this problem fifty years ago. The structural logic is worth revisiting.
The Accountability Inversion
The assumption is straightforward: better models unlock broader autonomy, and the most technically adventurous organizations lead agent adoption. The evidence points somewhere unexpected. Heavily regulated, accountability-dense environments are reaching production first. Compliance monitoring, clinical documentation, financial back-office. The places you'd expect to move slowest are moving fastest. The gating factor for agent deployment at scale turns out to be whether anyone can later prove who authorized what, within what limits, and where the records live. Banks solved a version of this problem fifty years ago. The structural logic is worth revisiting.
Accountability in Practice
Why the Most Regulated Industries Are Deploying Agents First
Mastercard's Agent Pay announcement read more like a policy document than a product launch. That's the point. Payments, financial compliance, and regulated operations are deploying agents faster than anyone expected, drawing on decades of managing authorization, audit trails, and dispute resolution to provide exactly the scaffolding agents need. Regulation pre-answered the hardest delegation questions. Everyone else is still improvising.
The Demo-Mode Trap
Marketing, content generation, internal ops. Low regulatory exposure, minimal chargeback liability, and nobody goes to jail if a draft is slightly off. These should be the easy domains for agent deployment. Yet McKinsey found no more than 10% of organizations scaling agents in any single function. Without the forcing function of expensive failure, teams never built the delegation infrastructure that regulated industries take for granted. Agents stay stuck as fast interns.
We Built the Infrastructure Agents Need — We Just Built It for Examiners
CONTINUE READINGRegulation as Accelerant
Five weeks from now, the EU AI Act's Article 50 transparency rules take effect. Any AI system that interacts directly with a person must disclose that fact. AI-generated content needs machine-readable marking. Deepfakes require visible labels.
These are disclosure obligations, not operational ones. They don't mandate audit trails or delegated-authority architectures. But they arrive as the first hard deadline inside a broader compliance stack that does. The Act's high-risk provisions, applicable from December 2027, require activity logging, technical documentation, human oversight by a specifically equipped person, and demonstrated accuracy and robustness.
Most coverage treats this as friction. For teams still running agents as experiments, it probably is. But read the compliance checklist from the perspective of an organization trying to move agents into production and it looks more like an implementation roadmap. Who authorized the action, what did the system do, where does a human review the output, what evidence exists that it worked correctly. Those questions come up whether or not Brussels requires answers.
A McKinsey survey last year found just 23% of respondents scaling an agentic system anywhere in the enterprise. Model capability isn't the bottleneck. The surrounding infrastructure of accountability is. Regulation didn't create that gap, but it does put a date on it.
Further Reading
Past Articles
If the individual question is whether curating AI outputs builds real expertise, the organizational question is whether ...
You spend your days reviewing, refining, and redirecting AI-generated work. You learn where the model hallucinates, whic...
When AI took over compliance report drafting at a pharmaceutical company, a three-day analyst task collapsed to overnigh...
Organizations are building increasingly sophisticated systems to monitor whether their AI agents perform well. Almost no...