New VB Pulse data suggests the enterprise fight is shifting from model quality to the “control plane” where AI agents plan, call tools, access data, and get audited. Microsoft Copilot Studio and Azure AI Studio lead adoption, OpenAI follows, and Anthropic’s Claude registers a first measurable foothold at orchestration—hinting model momentum may be spilling into runtime infrastructure.
AWS Quick has expanded into a desktop-native agent that maintains a persistent personal knowledge graph built from local files and connected SaaS apps. Instead of waiting for prompts, it can proactively suggest and execute actions using learned context—potentially creating “shadow orchestration” beyond what enterprise control planes can fully observe.
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Mistral AI has released Workflows in public preview, positioning it as the missing infrastructure layer that turns enterprise AI from pilots into reliable business operations. Built on Temporal for durable execution, it keeps sensitive data in customer environments, adds deep observability with OpenTelemetry, and is already in production processing millions of executions daily.
The most costly enterprise AI failures may produce no errors, no red dashboards, and no alerts—yet deliver confident, consistently wrong outputs. The issue isn’t the model’s benchmark performance, but “context decay,” orchestration drift, stale retrieval, and silent partial failures across infrastructure and workflows. Fixing it requires behavioral telemetry and intent-based stress tests, not just uptime monitoring.
After 18 months of “agent building,” BAND (Thenvoi AI Ltd.) targets the tougher next step: letting AI agents built on different frameworks actually collaborate. Exiting stealth with $17 million, it offers a deterministic “Slack for agents” interaction layer with multi-peer comms, identity-based permissions, and an enterprise control plane for auditability—positioned as infrastructure for a universal orchestrator.
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