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Most AI agents never make it to production. They stall in security reviews, fail compliance checks, or get blocked by risk committees who can’t verify what the agent will actually do. The gap isn’t capability—it’s trust. Enterprises need evidence that an agent is reliable, safe, and secure before they’ll deploy it. Vijil closes that gap. We provide the infrastructure to measure trust, test for it systematically, protect agents in production, and improve them continuously based on real-world behavior.

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The Trust Score

At the center of Vijil is the Trust Score—a quantitative measure of an agent’s trustworthiness across three dimensions:

Reliability

Does the agent do what it’s supposed to do, consistently and accurately?
  • Correctness
    Produces accurate and valid outputs
  • Consistency
    Behaves predictably across similar inputs
  • Robustness
    Handles edge cases and errors gracefully

Security

Can the agent resist adversarial attacks and protect sensitive data?
  • Confidentiality
    Protects sensitive data from exposure
  • Integrity
    Prevents unauthorized data modification
  • Availability
    Resists denial of service attacks

Safety

Does the agent avoid harmful outputs and respect boundaries?
  • Containment
    Operates within defined boundaries
  • Compliance
    Follows policies and regulations
  • Transparency
    Provides clear reasoning for decisions
The Trust Score turns trust from a subjective judgment into something measurable—evidence you can show to security reviewers, compliance teams, and business stakeholders.

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Build, Ship, Run, Evolve

Vijil lifecycle: Ship with Diamond, Run with Dome Vijil covers the full agent lifecycle with integrated products:

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Ship with Diamond

Test your agents before you trust them. Diamond evaluates agent behavior against hundreds of scenarios—reliability under stress, resistance to prompt injection, compliance with safety policies. You get a Trust Score and detailed findings in minutes, not weeks.

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Run with Dome

Protect agents in production. Dome provides real-time guardrails that filter harmful inputs and outputs, detect anomalies, and enforce policies—all with latency measured in milliseconds. When something goes wrong, you know immediately.

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Coming Soon

  • SOON Depot will provide hardened models and pre-validated building blocks to reduce months of security work to days.
  • SOON Darwin will use reinforcement learning over production telemetry to continuously improve agent resilience as behavior drifts over time.

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Next Steps

Understand the Trust Score

Deep dive into how trust is measured across reliability, safety, and security.

Get Started

Set up your account and run your first evaluation.
Last modified on April 20, 2026