Band Raises $17 Million to Build AI Infrastructure for Enterprise Agent Governance
Band Raises $17 Million to Build AI Infrastructure for Enterprise Agent Governance
As autonomous AI agents become more common in corporate environments, a new startup is aiming to solve one of the industry’s biggest emerging problems: how independent AI systems safely communicate, collaborate, and operate at scale.
Band, a startup based in Tel Aviv and San Francisco, has emerged from stealth mode after securing $17 million in seed funding. Led by CEO Arick Goomanovsky and CTO Vlad Luzin, the company is developing an “interaction infrastructure” designed to govern how multiple AI agents function across enterprise networks.
The rise of AI agents has allowed businesses to automate tasks such as engineering workflows, customer support, and cybersecurity operations. However, as more AI systems are deployed across departments and cloud platforms, companies face growing challenges in coordination, permissions management, data sharing, and reliability.
Band argues that simply adding more business logic is no longer enough. Instead, enterprises need a dedicated infrastructure layer similar to how API gateways and service meshes were introduced during earlier waves of software development.
According to the company, today’s enterprise AI environment is highly fragmented. Different teams use different frameworks, cloud providers, communication protocols, and models. No single vendor controls the full ecosystem, making cross-system collaboration increasingly complex.
Although standards such as the Model Context Protocol (MCP) and emerging agent-to-agent communication protocols help define how systems connect, they do not solve critical runtime issues such as routing, authority controls, error recovery, cost management, and human oversight.
Band’s platform aims to fill that gap by acting as a governance mesh for AI interactions.
One of the biggest risks identified is financial waste. If multiple AI agents repeatedly call expensive large language models or become stuck in looping negotiations, cloud computing costs can escalate rapidly. Band says enterprises need hard spending limits and automated circuit breakers to stop runaway token usage.
Security and compliance are also major concerns. In industries such as banking and healthcare, autonomous systems interacting with legacy databases or sensitive records could create data conflicts, corruption, or unauthorized access if left unmanaged.
Band plans to address this by enforcing capability restrictions, secure routing, cryptographic logs, and detailed audit trails that allow organizations to trace every automated decision to its source.
Rather than relying on one massive AI system, Band’s model supports networks of specialized AI agents working together while remaining subject to centralized governance.
Industry observers say this reflects a growing shift in enterprise AI strategy: success may depend less on flashy AI demos and more on the invisible infrastructure that keeps autonomous systems reliable, secure, and cost-effective.
Industry observers say this reflects a growing shift in enterprise AI strategy: success may depend less on flashy AI demos and more on the invisible infrastructure that keeps autonomous systems reliable, secure, and cost-effective.
