VB Transform 2026: Five startups tackle enterprise agent infrastructure gaps
Enterprise AI agents have outpaced the infrastructure needed to coordinate them safely in production. Coverage from VB Transform 2026 profiles five startups positioning themselves at the intersection of orchestration, observability, security, and auditability. The problems they identify are legitimate, but the source presents vendor claims without independent validation.
The gap is real: agents deployed across enterprises generate complex, multi-step workflows that existing tooling was not designed to track, govern, or secure. BAND describes the current state as agents working in "digital solitary confinement," unable to see each other or coordinate without manual onboarding. The company is building a coordination layer that supports autonomous workflows running eight to 20 hours, with compatibility for A2A and MCP protocols. BAND frames its approach around a transportation layer problem: remote processes need to communicate at an abstraction level rather than through IP addresses and URLs. The source describes a vision where agents discover each other, collaborate, delegate subtasks, and return summaries to human users. BAND also positions real-time task recording as a visibility feature.
On the security side, Conifers and Arcade represent two different threat models. Conifers targets defensive operations, arguing that security teams operate at human speed while adversaries have moved to machine speed. The company claims its agentic system condenses containment time from seven hours to 12 minutes and can complete complex cyber investigations in four minutes. That is a significant performance delta if it holds in production environments. Conifers connects to existing enterprise security tools including endpoint detection, SIEM, and posture management platforms. Arcade takes a different angle, focusing on the authorization problem: agents acting on behalf of users need a security architecture that can pass critical reviews. The company offers a secure runtime installed as a plugin, deployable on-premises, with role-based access controls, intrusion detection and prevention systems, and policy enforcement at every action. Arcade frames its primary problem as supply chain attacks on agent tooling, arguing that existing abstractions have placed security and observability in the wrong layer.
The auditability problem gets specific treatment from Raindrop AI. The company describes a "double whammy" as agents grow more capable: complexity increases and timelines extend, sometimes to hours or days of continuous operation. When issues arise in sectors like healthcare or defense, the consequences scale accordingly. Raindrop AI's platform identifies critical issues in agents running in production and simulates fixes based on past behavior before deployment. The source describes a reinforcement learning platform that optimizes training harnesses, with pre-deployment simulation to predict impact and live A/B testing to observe results. Error messages, tool calls, retries, and failures are aggregated into a navigable format with Slack notifications when issues occur. The company claims signals from each deployment train models continuously across customers.
Omilia addresses customer experience automation, a different operational domain from the others. The company describes heuristic-based systems as slow but controlled, and agentic systems as fast but unpredictable. Its approach observes actual customer service operations, listening to interactions, ingesting API specifications and standard operating procedures, then mapping those to support use cases. Omilia reports handling more than three billion calls per year, with some deployments processing more than one million voice calls daily. The company claims 30 to 45 percent improvement in time to resolution and generates 21 times more upsell revenue compared to human agents. Mature deployments reach 80 to 90 percent automation, though the source notes human oversight remains part of the workflow.
The source frames these five companies as early answers to a real infrastructure gap. That framing is accurate as far as it goes, but it leaves important questions unexamined. The performance numbers come from the companies themselves: Conifers reporting containment-time compression, Raindrop describing its simulation accuracy, Omilia citing upsell multipliers. None of these claims appear to be independently benchmarked or audited against standardized conditions. For enterprise buyers evaluating agent infrastructure, the gap between a demo-stage result and a production-verified outcome is where procurement decisions actually live. The source does not bridge that gap.
The five approaches also address fundamentally different failure modes: BAND targets coordination, Conifers targets response time, Arcade targets authorization scope, Raindrop targets auditability, and Omilia targets CX automation. These are not competing solutions to the same problem. The more useful frame is that enterprise agent deployments will likely need to combine multiple of these capabilities, which raises integration complexity as a separate challenge the source does not address.
The source presents the infrastructure gap as a problem being actively solved. A more precise reading is that the problem is being actively described by companies with commercial incentives to be seen as the solution. The distinction matters for teams evaluating vendor claims without independent validation data. What the source confirms is that the operational problems are real enough to attract startup capital and enterprise attention. Whether the solutions work as described under production conditions with a specific enterprise's workload remains the question the coverage does not answer.