智能体监督的未来（英）

The Future of Agentic SupervisionWE ACCELERATE DATA AND AI ADOPTION TO POSITIVELY IMPACT PEOPLE AND ORGANIZATIONS.Artefact is a global leader in consulting services, specialized in data transformation and data & digital marketing, from strategy to the deployment of AI solutions. We are offering a unique combination of innovation (Art) and data science (Fact).25COUNTRIES+1000CLIENTS1700EMPLOYEESSTRATEGY & TRANSFORMATION | AI ACCELERATION | DATA FOUNDATIONS & BIIT & DATA PLATFORMS | MARKETING DATA & DIGITALTHE FUTURE OF AGENTIC SUPERVISION3Executive summaryLast February, we published “The Future of Work with AI”, our first study on Agentic AI. We found that although AI agents will replace humans on tedious and repetitive tasks, a new type of work will appear: Agentic Supervision. During the industrial revolution, machines replaced humans on manual tasks, but new jobs appeared such as machine purchasing, operational supervision and maintenance. With Agentic AI, cognitive jobs will be replaced by other higher-level and more productive cognitive jobs. This study intends to deep dive into the early days of Agentic Supervision and to draw the outline of the Future of Supervision in terms of Agent lifecycle management, governance and supervision tooling.To gather the current state of Agentic Supervision, we in-terviewed 14 enterprises and 5 Artefact Agentic Product Managers & Engineers. We also contacted key Agentic Supervision providers, including major Data & AI platforms with years of software supervision experience (such as Google and Microsoft) as well as specialized start-ups (WB, Giskard, RobustIntelligence…).The first insight we found is that while Agentic Supervision extends the principles established in DevOps (software op-erations), DataOps (data operations), and MLOps (Machine Learning operations), it dramatically increases the demand for robust governance to keep AI Agents aligned and under control. Indeed, with “software that starts to think”, unseen risks are emerging, such as hallucination, reasoning errors, inappropriate tone, intellectual property infringement or even prompt jacking. Mitigating these reliability, behavioral, regulatory and security risks now requires governance that is not only more rigorous but also broader than what has previously been applied to tech products.This markedly greater need for governance is the chal-lenge that may define the emerging operational paradigm of “AgentOps”. Interestingly, AgentOps will need to build upon each organization’s existing DevOps, DataOps, and MLOps foundations and governance, and companies lag-ging in these operational domains will have to bridge any gaps in these areas while setting their Agentic governance framework.The second major challenge identified by our interview-ees is the need to strengthen their AI supervision tooling. Many are currently relying on existing RPA and Dev/Data/MLOps tools, or experimenting with custom-built solutions as they search for more sustainable,