To eliminate automation waste, companies must deploy interaction infrastructure that physically controls how independent AI agents operate.
AI agents are now being deployed in enterprise networks to reason tasks and make decisions with increased autonomy. However, as these independent actors attempt to coordinate their work, exchange context, and operate across different cloud environments, the interaction framework quickly degrades. Human operators themselves act as manual glue between disconnected systems, managing fragile integrations while rules governing permissions and data sharing remain implicit.
Band is a Tel Aviv and San Francisco-based startup that has come out of stealth mode with a $17 million seed round to address this infrastructure problem. The funding will support CEO Arick Goomanovsky and CTO Vlad Luzin in their efforts to build a dedicated interaction layer for autonomous enterprise systems. This concept reflects the early evolution of computing, when application programming interfaces required specialized gateways and microservices required a service mesh to function at scale.
As distributed systems proliferate under the ownership of different internal teams, adding more business logic does not solve the underlying instability. Rather, interaction reliability requires a separate infrastructure layer.
Market dynamics have changed in three main ways. First, autonomous actors have graduated from experimental deployments to active runtime participants managing engineering pipelines, customer support queries, and security operations. Enterprise use is no longer a future consideration. It is an active operating state. The pressing issue is managing what happens when these different actors have to cooperate.
Second, the production environment is completely heterogeneous. Engineering teams build their own tools across a variety of frameworks. These models run on competing cloud platforms, utilize different communication protocols, and report to separate business owners. No single vendor maintains control, and there is no unified framework that encapsulates the entire ecosystem. This fragmentation represents the enduring shape of the enterprise market.
Third, the underlying standards layer is taking shape. Initiatives like the Model Context Protocol (MCP) provide models with a unified way to access external tools. Similarly, A2A communication efforts establish baseline conversation parameters.
However, while the protocol defines the handshake, it cannot control the production environment. Standardized protocols do not manage routing, error recovery, privilege boundaries, human oversight, or runtime governance. It fails to articulate the shared operational space required for reliable interaction. The band intends to fill this infrastructure void.
Financial liability of unmanaged automation
Introducing independent models across business units creates complex integration challenges. When point-to-point integrations have to be manually connected by in-house development teams, the maintenance burden reduces profit margins and delays product releases. Financial risks extend beyond simple integration costs.
When autonomous actors pass instructions to each other without a central governor, organizations face ballooning computing costs. Multi-agent inference requires continuous API calls to expensive large-scale language models. Routing failures or loop errors between two disrupted entities can consume large amounts of cloud budget within hours.
Autonomous multi-agent workflows threaten this predictability if left unmanaged. Unsupervised negotiations between internal sourcing models and external vendor models can trigger hundreds of inference cycles and inflate the cost of using a token beyond the value of the underlying transaction. Therefore, the infrastructure layer must implement a hard financial circuit breaker to terminate interactions that exceed a predefined token budget or calculation threshold.
Enhanced multi-agent execution layer
Integrating these intelligent nodes with traditional enterprise architectures requires significant engineering resources. Financial institutions and healthcare providers operate with highly-hardened on-premises data warehouses, mainframe computing clusters, and customized enterprise resource planning applications.
Without a hardened interaction infrastructure, the risk of data corruption increases with each automated step. At the same time that the billing model starts a transaction, the compliance model may flag the same account, causing database locks and entry conflicts. The interaction layer prevents these conflicts. By enforcing functional limits, the infrastructure ensures that autonomous entities cannot force unauthorized changes to primary source systems.
Vector databases that store the context memory required for search expansion generation have similar challenges. These storage systems are often configured in isolated environments for specific use cases. When a technical support bot needs to forward an ongoing customer interaction to a dedicated hardware diagnostic bot, context data must be accurately passed between separate vector environments.
Data degradation occurs when models are forced to interpret summarized output from other models rather than accessing the original cryptographically verified data logs. Stopping this degradation requires strict context boundaries and a central interaction mesh that can trace the complete lineage of all shared information.
The risk of data contamination raises liability issues. If a customer service model accidentally ingests highly sensitive financial data from an internal audit model during a context exchange, non-compliance can result in severe regulatory penalties.
Establishing a secure communication mesh allows data owners to enforce very specific access controls at the interaction layer, rather than rebuilding the logic of individual models. All digital interactions require encrypted logs so that regulators can trace automated decisions back to their exact starting point.
Treat the communication mesh as a security perimeter
The platform’s design rejects the concept of a monolithic model for managing the entire enterprise. Instead, we expect teams of specialized participants with different strengths, playing different roles, and working synchronously without requiring identical architectures.
The system operates as a framework- and cloud-agnostic platform and recognizes the value of existing tools. The market already has a framework for feature development. Band focuses on the operational phase and is involved when a model leaves the laboratory and enters a physical enterprise network as a distributed entity.
Governance is central to this strategy. Common errors in enterprise technology deployment include treating governance as a secondary function that is patched into the system after initial deployment. This approach fails when applied to autonomous corporate actors. These systems delegate tasks, transfer context, and execute actions across organizational units. If permission rules remain implicit and data routing lacks transparency, operations lack the necessary trust, even if they are technically functional.
To mitigate this risk, the underlying mesh must act as a security perimeter. Organizations need mechanisms to inspect delegation chains, enforce strict privilege limits, and maintain comprehensive audit trails detailing actions at runtime. Human participation needs to be deeply embedded in the execution layer.
Collaboration mechanisms and governance controls must occupy the same infrastructure level. Without this foundation, the transition from using a single model to a networked enterprise implementation will be hampered and stalled by a combination of system failures and non-compliance. Companies that successfully deploy scalable operations are those that go beyond simply amassing good software demonstrations to investing heavily in the underlying interaction infrastructure.
See also: Billion-dollar startups with different ideas for AI
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