The advent of AI agents - AI systems that can autonomously achieve goals in the world, with little to no explicit human instruction about how to do so - is ushering in a transformative era for businesses. Leading tech companies, AI startups, and investors are heavily focused on these developments, envisioning a future where millions or billions of agents autonomously perform complex tasks across society. As these "AI teammates" become integral to daily operations, from automating data pipelines to generating executive insights, the critical challenge shifts to effectively managing, monitoring, and governing their behavior to ensure trust, consistency, and security. The Imperative of Agent Governance Deploying capable AI agents en masse promises transformative benefits but also introduces profound and novel risks. Without proper governance, issues like conflicting interpretations of data, inadvertent exposure of sensitive information, cascading errors from bad inputs, and an inability to reconstruct decision logic can arise. This necessitates a robust framework for managing the agent lifecycle, ensuring that these autonomous systems operate within trusted boundaries. Only a few researchers are currently actively working on agent governance challenges, primarily in civil society organizations, public research institutes, and frontier AI companies. Fortunately, platforms like Cube D3 are emerging to provide enterprise-grade solutions for this nascent field. Managing Your AI Teammates: D3's Approach to Spaces and Scoping Cube D3 is the first agentic analytics platform built on a universal semantic layer, designed to enable the development and deployment of AI data co-workers across the enterprise. A core aspect of managing these AI teammates effectively in D3 revolves around its concepts of "Agent Spaces" and scoping agent operations. Agent Spaces are logical groupings used to manage agents and their related resources. These spaces support a role-based permission model, simplifying the management and oversight of agent groups. This allows organizations to facilitate the creation, viewing, and deletion of agent groups in a structured manner. Scoping Agent Operations: Within these spaces, agents can be restricted to specific data views for enhanced security and focus. This ensures that each agent only interacts with the data relevant to its defined role, minimizing the risk of unauthorized access or misuse. For instance, an AI Data Analyst assists Data Consumers by creating Semantic SQL queries and visualizations, while an AI Data Engineering Agent supports Data Stewards in creating and optimizing semantic models. Rules for Behavior: D3 also incorporates "Rules," which are defined guidelines within a space that manage agent behavior and data interaction. These can include "always rules" and "agent requested rules," providing a crucial mechanism for guiding AI on how to query specific data points or respond to complex scenarios. Bring Your Own Model (BYOM): D3 supports the selection of various Large Language Models (LLMs), including the ability to Bring Your Own Model (BYOM). This flexibility allows enterprises to meet stringent requirements and keep their data secure. This structured approach to agent management through spaces and scoping ensures that agents operate within defined boundaries, mirroring how human teams are organized with distinct responsibilities and access levels. Monitoring Your AI Teammates: Transparency, Logging, and Rectification For AI agents to be trusted, their actions must be transparent and auditable. D3 provides robust mechanisms to monitor agent activities, understand their reasoning, and rectify any incorrect approaches. Explainable Outputs: D3 agents don't just generate answers; they explain their decisions. Every result includes transparent query logic, data sources, filters, metric lineage, and calculation logic. This "legibility of agent activity" provides users with the transparency and oversight necessary to understand how the agentic AI system works and why it carried out a particular action. This allows executives and data consumers to click into every result, inspect every query, and feel confident in every recommendation. Traceable Actions and Logging: D3 ensures that every agent’s action is logged, version controlled, and auditable for traceability. This includes Semantic SQL Tool Call Messages (SSTCMs), which are immutable records of agent-generated queries and visualizations. This capability is crucial for audit readiness and compliance, especially in regulated industries. Iterative Refinement and Rectification: The D3 Analytics Chat Interface allows users to interact with agents using natural language. When an agent generates a query or visualization, it's presented as an SSTCM in the chat. Users can then open these SSTCMs or "Workbooks" (saved analytical outputs) in the Data Explorer to view the underlying SQL query, results, and visualizations. Crucially, users can modify the SQL query, run it, and send the updated query back to the chat to refine their analysis or provide feedback to the agent. This direct ability to inspect and correct the agent's generated SQL facilitates understanding and rectifying incorrect approaches, enhancing the agent's learning over time. Certified Queries: To further ensure trust and consistency, D3 allows for Certified Queries, which are SQL queries that have been reviewed and approved by administrators. Agents can then reliably use these certified queries within a specific space, reinforcing best practices and preventing deviations from established logic. This comprehensive approach to monitoring provides both visibility and control, addressing the concerns of businesses moving from AI experimentation to AI-driven execution. Governing with a Semantic Core: The Single Source of Truth The foundation of trusted AI agent operations in D3 is Cube Cloud's universal semantic layer. This semantic core is not merely a data catalog; it is a structured bridge between complex data sources and business requirements, transforming technical data language into the understandable vocabulary of the business. Single Source of Truth: The semantic layer provides a single source of truth for business metrics and dimensions, ensuring consistent definitions and reducing ambiguity across the organization. This is paramount because if agents interpret key performance indicators (KPIs) differently, it can lead to conflicting recommendations and undermine trust. Deep Contextual Understanding: By embedding metadata and standardizing business logic, the semantic layer offers the deep contextual understanding necessary for AI agents. Without this governed, business-friendly layer, AI agents querying raw data directly are prone to errors, miscalculations, and "hallucinations". Automated SQL Generation: Cube's semantic layer includes a compiler that translates high-level business requests into correct and optimized SQL queries. This offloads the complex task of SQL generation from the AI agent, drastically reducing error risk and ensuring that the queries align with enterprise standards and policies. Fine-Grained Governance and Access Control: D3 extends the semantic governance of Cube Cloud into the agent ecosystem. This means that access policies are enforced at the semantic level, not just at the application layer. D3 validates inputs and outputs, enforces metric definitions, controls access permissions, and logs every action for traceability. This ensures that no matter how or where an agent acts, sensitive data remains protected and that agents cannot operate outside of defined rules or permissions. This provides enterprise-grade governance over every AI output. By grounding every agent in this consistent semantic model, D3 ensures that every human and every machine thinks consistently, acts responsibly, and drives real, scalable outcomes. This robust semantic foundation is what differentiates D3, delivering a modern, agentic analytics experience where all outputs are governed, consistent, and aligned with business logic. In conclusion, managing, monitoring, and governing AI teammates is not just about isolated controls but about an integrated system. Cube D3, with its agent spaces and scoping capabilities, its transparent logging and explainability features, and its foundation in Cube Cloud's universal semantic layer, provides a comprehensive framework for enterprises to build trusted multi-agent architectures, accelerate insight generation, and ensure the trustworthiness of AI-driven analysis. The future of data work is agentic, and with D3, organizations can unlock faster, more accurate, and more impactful decisions at every level of the business.