How Vijil Measures Trust
| Dimension | Question | Learn more |
|---|---|---|
| Reliability | Does the agent do the right thing, consistently, under stress? | Reliability |
| Security | Can it resist abuse and protect sensitive data? | Security |
| Safety | Does it stay within policy and avoid harmful outcomes? | Safety |
Build, Ship, Run, Evolve
Agent prototypes are everywhere—but most don’t make it to production. To scale AI, enterprises face a critical choice: ship fast or ship resilient.- Agent prototypes are everywhere—but most don’t make it to production
- Shipping fast increases failure rates and elevates risk; without objective trust evidence, agents are in security limbo
- Generic red-teaming and guardrails don’t ensure agents are safe for their specific context
- Failure insights don’t feed back into development, leaving the trust gap open

Continue with Vijil
Pick where you work and what you need next:Concepts
You want the mental model - Trust Score, evaluation components (Harness, Scenarios, Probes), and Defense (Guards, Guardrails, Detectors).Start here → Trust Score, then explore this tab for deeper topics and the Glossary.
Agent Owner's Guide
You use the Vijil console to register agents, run evaluations, configure Dome, and report to stakeholders.Start here → Get started in the console.
Agent Developer's Guide
You integrate Vijil via SDKs & APIs, CI/CD, frameworks (e.g. LangChain, ADK), and production wiring.Start here → Developer introduction and installation.
Quick Resources
- Evaluate in the UI → Get started
- Protect in production → Configuring guardrails
- Install the SDK → Installation
Coming Soon
Two additional products are currently in development and will expand the Vijil platform:Depot
A catalog of hardened models and building blocks—guardrail models tuned for agent safety, hardened LLMs optimized for specific tasks, and pre-validated components that reduce months of security work to days.
Darwin
Continuous improvement powered by reinforcement learning over production telemetry. Darwin learns from real-world failures, edge cases, and behavior drift to keep your agents resilient over time.