Why AI agents need interaction infrastructure

To stop automation waste, enterprises must deploy interaction infrastructure that physically governs how independent AI agents operate.

AI agents now populate corporate networks, reasoning through tasks and executing decisions with increasing autonomy. Yet, when these independent actors attempt to coordinate work, exchange context, or operate across varied cloud environments, the interaction framework degrades quickly. Human operators find themselves acting as the manual glue between disconnected systems, managing fragile integrations while the rules dictating permissions and data sharing remain implicit.

Band, a startup based in Tel Aviv and San Francisco, has exited stealth mode with a $17 million seed round to address this infrastructure problem. The funding backs CEO Arick Goomanovsky and CTO Vlad Luzin in their effort to build a dedicated interaction layer for autonomous corporate systems. The concept mirrors earlier computing evolutions, wherein application programming interfaces required dedicated gateways and microservices necessitated a service mesh to function at scale.

As distributed systems multiply under the ownership of different internal teams, adding more business logic fails to resolve the underlying instability. Rather, interaction reliability requires a distinct infrastructure layer.

Market dynamics have changed in three key ways. First, autonomous actors have graduated from experimental deployments into active runtime participants managing engineering pipelines, customer support queries, and security operations. Enterprise usage is no longer a future consideration; it is an active operational state. The pressing issue involves managing what occurs when these distinct actors must collaborate.

Second, the operational environment is entirely heterogeneous. Engineering teams build distinct tools across varied frameworks. These models execute on competing cloud platforms, utilise varying communication protocols, and report to separate business owners. No single vendor maintains control, and no uniform framework encapsulates the entire ecosystem. This fragmentation represents the permanent shape of the enterprise market.

Third, a foundational standards layer is taking shape. Initiatives like the Model Context Protocol (MCP) afford models a uniform method for accessing external tools. Similarly, A2A communications efforts are establishing baseline conversational parameters.

Yet, while protocols define the handshake, they fail to manage the production environment. Standardised protocols do not administer routing, error recovery, authority boundaries, human oversight, or runtime governance. They cannot manifest the shared operational space necessary for reliable interaction. Band intends to fill this infrastructure void.

The financial liability of unmanaged automation

Deploying independent models across business units creates compounding integration challenges. If point-to-point integrations must be hand-wired by internal development teams, the maintenance burden will drag down profit margins and delay product releases. The financial risk extends beyond simple integration costs.

When autonomous actors pass instructions between themselves without a central governor, organisations face ballooning compute expenses. Multi-agent inference requires continuous API calls to expensive large language models. A failure in routing or a looping error between two confused entities can consume substantial cloud budgets within hours.

Autonomous multi-agent workflows threaten this predictability if left unmanaged. An unmonitored negotiation between an internal procurement model and an external vendor model could trigger hundreds of inference cycles, inflating token usage costs beyond the value of the underlying transaction. Infrastructure layers must therefore implement hard financial circuit breakers, terminating interactions that exceed pre-defined token budgets or computational thresholds.

Hardening the multi-agent execution layer

Integrating these intelligent nodes with legacy corporate architecture demands intense engineering resources. Financial institutions and healthcare providers operate upon heavily fortified on-premises data warehouses, mainframe computation clusters, and customised enterprise resource planning applications.

Without a hardened interaction infrastructure, the risk of data corruption multiplies with every automated step. A billing model might initiate a transaction while a compliance model simultaneously flags the same account, creating a database lock or conflicting entries. The interaction layer prevents these collisions. By enforcing capability limits, the infrastructure guarantees an autonomous entity cannot force unapproved modifications to primary source systems.

Vector databases, which house the contextual memories required for retrieval-augmented generation, present a similar challenge. These storage systems are frequently configured in isolated environments tailored to individual use cases. If a technical support bot must transfer an ongoing customer interaction to a specialised hardware diagnostic bot, the contextual data must pass between isolated vector environments accurately.

Data degradation happens when models are forced to interpret summarised outputs from other models rather than accessing the original, cryptographically verified data logs. Halting this degradation requires rigid contextual borders and a central interaction mesh capable of tracing the complete lineage of all shared information.

The risk of data contamination creates liability issues. If a customer service model accidentally ingests highly classified financial data from an internal audit model during a contextual exchange, the compliance violation could trigger severe regulatory penalties.

Establishing a secure communication mesh allows data officers to enforce highly specific access controls at the interaction layer rather than attempting to reconstruct the logic of individual models. Every digital interaction requires cryptographic logging to ensure regulatory bodies can trace automated decisions back to their exact origination point.

Treating the communication mesh as a security perimeter

The platform’s design rejects the notion of a monolithic model managing the entire enterprise. Instead, it anticipates teams of specialised participants holding different strengths and fulfilling distinct roles, operating synchronously without requiring identical architectures.

Operating as a framework-agnostic and cloud-agnostic platform, the system acknowledges the value of existing tools. The market already possesses functional development frameworks. Band focuses on the operational phase, engaging when models leave the laboratory and enter the physical enterprise network as distributed entities.

Governance constitutes the core of this strategy. A frequent error in enterprise technology deployments involves treating governance as a secondary feature, patched onto the system after initial deployment. This approach fails when applying it to autonomous enterprise actors. These systems delegate tasks, transfer context, and execute actions across organisational lines. If authority rules remain implicit and data routing lacks transparency, the operation will lack the necessary trust, even if it functions technically.

To mitigate this risk, the underlying mesh must function as a security boundary. Organisations require mechanisms to inspect delegation chains, enforce strict authority limits, and retain comprehensive audit trails detailing runtime actions. Human participation must be integrated deeply into the execution layer. 

Collaboration mechanisms and governance controls must occupy the same infrastructure level. Without this foundation, the transition from single-model usage to a networked enterprise implementation will stall, hindered by compounding system failures and compliance violations. The companies that successfully deploy scalable operations will be those investing heavily in the underlying interaction infrastructure rather than simply accumulating impressive software demonstrations.

See also: The billion-dollar startup with a different idea for AI

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