Vijil Raises $17M to Build Trust Infrastructure for AI Agents
Vijil secured $17 million in Series A funding to accelerate deployment of its AI agent trust infrastructure platform that addresses the enterprise adoption bottleneck through continuous resilience improvement.
The funding round, led by BrightMind Partners with participation from Mayfield and Gradient, brings the company’s total funding to $23 million and validates growing enterprise demand for trusted AI agent deployment capabilities that reduce time-to-production from months to weeks.
The Trust Bottleneck Crisis
Enterprises struggle to bring AI agents into production because teams lack the expertise, tools, or bandwidth to ensure reliability, security, and governance at scale. This creates a fundamental deployment bottleneck where organizations experiment with agents but can’t scale them across operations.
Traditional approaches require extensive manual testing, security reviews, and compliance validation—processes that can take six months or longer. SmartRecruiters, one of Vijil’s enterprise customers, exemplifies this challenge: “Our enterprise customers demand trust verification before deploying AI in hiring workflows,” said Michal Nowak, senior vice president of engineering.
The trust gap manifests in several critical ways: agents hallucinate unpredictably, leak sensitive information, create new network vulnerabilities, and operate as “black boxes” without auditability. These risks force enterprises to choose between innovation speed and operational safety.
Continuous Hardening Architecture
Vijil provides a modular platform to build, test, deploy, and continuously improve the intrinsic resilience of agents with reinforcement learning on production telemetry. Unlike point solutions that address specific vulnerabilities, the platform creates a comprehensive trust framework.
The architecture enables developers to build AI agents with hardened components, test reliability and security during development, mitigate risk before deployment, ensure governance at runtime, and continuously improve resilience by learning from operational data.
Key capabilities include:
- Development hardening: Pre-configured agent templates and custom testing frameworks
- Runtime defense: Real-time protection against prompt injection and unsafe outputs
- Trust scoring: Quantified reliability metrics based on defenses and testing results
- Automated compliance: EU AI Act and NIST framework documentation generation
- Continuous learning: Reinforcement learning from production telemetry to evolve defenses
Enterprise Validation and Results
SmartRecruiters demonstrates the platform’s impact, reducing deployment time from six months to six weeks—a 75% improvement in time-to-trust. The company now ships AI agents for hiring workflows while dramatically lowering compliance costs and meeting enterprise customer security demands.
Vijil’s approach addresses what BrightMind Partner Stephen Ward calls the core differentiation: “Vijil doesn’t just secure AI agents; it helps them adapt and evolve.” This continuous hardening through reinforcement learning creates a feedback loop where agents become more resilient over time rather than remaining static.
The platform’s modular design supports different enterprise deployment patterns: rapid prototyping with built-in safeguards, custom integration with existing security infrastructure, and gradual scaling from pilot to production across business units.
Trust Infrastructure Category Emergence
Mayfield Partner Vijay Reddy identified the market dynamic driving enterprise demand: “Most enterprises are experimenting with AI agents but only a small fraction are scaling them. The biggest barrier is trust, which point solutions cannot overcome.”
The recognition by Gartner as a Cool Vendor in the 2025 Agentic AI Trust, Risk and Security Management (TRiSM) report signals the emergence of trust infrastructure as a distinct category. This validates the need for comprehensive platforms rather than fragmented security tools.
Founded in 2023 by senior leaders from AWS, Vijil targets the infrastructure layer that enables enterprise AI agent adoption. The team’s experience building AI infrastructure at cloud scale provides credibility for solving enterprise trust challenges.
Looking Forward
Vijil CEO Vin Sharma emphasized the platform’s role in enterprise AI transformation: “Vijil delivers the essential infrastructure layer that enterprises need now to trust AI agents in production.” The company will use the Series A funding to accelerate platform deployments and expand the continuous learning capabilities.
The trust infrastructure category represents a shift from reactive security to proactive resilience building. As enterprises move beyond pilot programs to production deployments, platforms that can continuously evolve agent capabilities while maintaining governance controls become critical infrastructure.
Future development will likely focus on expanding the reinforcement learning models, deepening compliance automation for additional regulations, and building ecosystem integrations with major enterprise AI platforms.
The AI agent deployment bottleneck highlights the need for comprehensive trust infrastructure—exactly the type of foundational challenge that platforms like Overclock help enterprises navigate through orchestrated automation workflows that maintain governance while accelerating innovation.