Enterprises need AI control plane to scale agents
Companies require an AI control plane with identity, permissions, observability, evaluation, workflow and security controls to move autonomous agents from pilots to production.
Enterprises moving beyond standalone large language model demonstrations require an AI control plane to scale autonomous agents into production. The control plane is a software layer that enforces identity and permissions, provides observability and evaluation, integrates with workflows and applies security controls.
Technical priorities inside organizations are shifting from adding raw processing power to adding software that links models to data, infrastructure and business systems. Identity and permission systems limit what agents can access. Observability tools record agent actions and performance. Evaluation frameworks test agent outputs. Workflow connectors integrate agent tasks with enterprise processes. Security controls reduce the risk of data exposure or misuse.
Foundational model developers are scaling their models and using public cloud compute such as Amazon Web Services and Google TPUs, along with newer data platforms. Agent developers require developer tools and telemetry so agent actions can be audited, tested and rolled back when needed. Those needs extend the technology stack across big data, cloud infrastructure, cybersecurity and integration tools.
Some financial advisors and investors are seeking ways to gain exposure to companies that provide components of the control plane. To limit single-stock risk, advisors may use thematic exchange-traded funds. The ROBO Global Artificial Intelligence ETF (THNQ) lists holdings such as Datadog, Snowflake, CrowdStrike, Palo Alto Networks and developer-tool companies like JFrog and includes cloud-related infrastructure exposure. The ROBO Global Robotics and Automation Index ETF (ROBO) provides exposure to hardware and logistics that support scaled agent deployments.
An enterprise control plane does not eliminate the need for compute or model development. It adds operational, security and compliance layers that organizations must manage before broad deployment. Those layers support evaluation of agent decisions, enforcement of policies, maintenance of audit trails and secure connections to internal systems.
VettaFi, the index provider referenced for THNQ and ROBO, provided a disclosure that it receives index licensing fees for those funds and that THNQ and ROBO are not issued, sponsored, endorsed or sold by VettaFi. VettaFi and its affiliates say they have no obligation or liability related to the funds’ issuance or trading.
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