Descope closed an $88 million seed round extension, bringing total funding to $88 million as the drag-and-drop external IAM platform positions itself at the center of enterprise AI agent deployment infrastructure.

Founded by the team behind Demisto (acquired by Palo Alto Networks), Descope addresses a critical bottleneck emerging across enterprise AI initiatives: how to securely manage identity and access for AI agents, MCP servers, and autonomous systems at scale. The extension round, available exclusively to existing investors including Notable Capital, Lightspeed Venture Partners, and Dell Technologies Capital, validates enterprise demand for purpose-built identity infrastructure as organizations move beyond AI pilots toward production deployments.

Workato today launched the industry’s first fully managed enterprise Model Context Protocol (MCP) platform, backed by partners including Anthropic, AWS, Atlassian, and Box. The release directly targets the security and governance bottleneck that has prevented organizations from deploying AI agents at scale.

While MCP has emerged as a promising standard for connecting AI agents to business applications, the thousands of available open-source MCP servers lack the enterprise-grade controls required for production environments. This has created a critical infrastructure gap, trapping many agentic AI initiatives in the pilot phase.

Zenity secured $38 million in Series B funding led by Third Point Ventures and DTCP, bringing total capital raised to over $55 million as enterprises grapple with a fundamental infrastructure bottleneck: securing AI agents at scale.

The timing reflects a critical enterprise adoption paradox. While Forbes reports over 51% of companies actively use AI for process automation and Microsoft notes daily Copilot usage has nearly doubled quarter-over-quarter, only 11% of CIOs have fully implemented AI according to Salesforce research. The primary constraint isn’t technological capability—it’s security infrastructure.

Modal Labs raised $80 million in Series B funding led by Lux Capital, achieving a $1.1 billion valuation as enterprises struggle with the infrastructure complexity gap between AI prototyping and production deployment.

The serverless AI infrastructure platform addresses a critical bottleneck: while AI model capabilities advance rapidly, the underlying cloud infrastructure wasn’t designed for modern AI workloads. Legacy clouds create friction through complex GPU capacity management, highly variable demand economics, and configuration overhead that prevents AI teams from focusing on core development.

Paid, the London-based startup founded by Outreach veteran Manny Medina, just closed a $21.6 million seed round led by Lightspeed to tackle a critical infrastructure gap: how AI agents get paid for the value they create.

The funding addresses a mathematical problem blocking enterprise AI adoption. Current billing models—per-user SaaS fees, unlimited usage, or API key access—break down when applied to autonomous agents that work continuously in the background without direct human oversight.

AppZen raised $180 million in Series D funding led by Riverwood Capital, bringing the agentic AI finance platform’s total funding to $290 million as the company serves 500+ global enterprises including 65 Fortune 500 companies like Amazon, Salesforce, and JPMorgan Chase.

The San Jose-based company addresses a fundamental enterprise finance bottleneck: traditional back-office operations rely heavily on manual reviews and offshore processing because existing rule-based automation systems cannot handle the complexity of human finance work at enterprise scale.

Mimica raised $26.2 million in Series B funding led by Paladin Capital Group to solve a critical enterprise AI deployment bottleneck: 95% of generative AI pilots fail because agents lack understanding of how work actually gets done in practice.

The Brooklyn and London-based process intelligence company addresses what CEO Tuhin Chakraborty calls the enterprise AI capability-context gap. “In enterprise AI, capability means nothing without context,” Chakraborty explained. “The agents that will win are the ones that understand the work—that’s what we make possible.”

Obot AI raised $35 million in seed funding to tackle the enterprise AI agent access management bottleneck that’s blocking large-scale AI deployment. Co-led by Mayfield Fund and Nexus Venture Partners, the investment targets the Model Context Protocol (MCP) Gateway infrastructure that enterprises need to safely connect AI agents with sensitive business systems.

The funding addresses a fundamental infrastructure gap: while AI agents proliferate across enterprises, organizations lack centralized governance tools to manage how these agents access APIs, databases, and business applications. Obot’s open-source MCP Gateway provides the missing control plane for enterprise AI agent deployments.

CodeRabbit raised $60 million in Series B funding at a $550 million valuation just two years after founding, addressing a critical enterprise infrastructure bottleneck: AI-generated code is creating exponential review backlogs that traditional processes can’t handle.

The Scale Venture Partners-led round, with participation from NVIDIA’s NVentures, validates an urgent infrastructure reality—teams using AI coding tools now generate 2x to 3x more pull requests while senior engineers struggle to review 20-30 PRs daily instead of the traditional 5-10. This mathematical impossibility is forcing enterprises to choose between deployment velocity and code quality.

Gartner predicts that by 2028, one in four enterprise breaches will trace back to compromised AI agents. Yet today’s identity systems still assume browser logins and manual logouts—a fundamental mismatch as autonomous agents begin invoking business applications at scale.

Scalekit, founded by the team behind Freshworks’ authentication platform, just secured $5.5 million in seed funding to build the missing authentication infrastructure for AI agents. The round was led by Together Fund and Z47, positioning the startup to address what may become the most critical security bottleneck in enterprise AI deployment.