Kurral offers a runtime monitoring and governance platform that instruments AI agents via a lightweight SDK or proxy, capturing execution traces, tool provenance, latency, and token usage across major model providers without modifying existing code. The system provides automated adversarial testing, real‑time policy enforcement, and immutable audit logs to support continuous risk mitigation and compliance throughout development, staging, and production pipelines.
Funding
Funding not disclosed
Founders
Product
Problem
AI agents are increasingly deployed in production environments, but organizations lack a unified way to observe runtime behavior, trace tool usage, and assess security risks without rewriting existing agent code. Undetected unsafe actions can lead to data breaches, compliance violations, or operational failures. Traditional red‑team scans provide static findings but do not capture live execution contexts needed for continuous governance.
Solution
Kurral delivers a runtime monitoring and governance platform that instruments AI agents through a lightweight SDK or proxy layer, preserving the existing model stack. The system records execution traces, tool provenance, latency, and token consumption in real time, creating an immutable decision trail. Integrated adversarial testing and policy evaluation automatically flag risky behavior and generate release decisions before agents reach production. Replayable evidence is stored in a secure cloud service, enabling auditors to reproduce incidents and verify compliance. The platform supports major model providers—including OpenAI, Anthropic, and Gemini—and can be embedded in development, CI/CD pipelines, staging, and production environments. Governance policies are enforced centrally, allowing teams to control agent actions across tools and providers without modifying business logic.
Target Audience
The primary customers are AI product teams, MLOps engineers, and security operations groups that deploy autonomous agents at scale and require continuous risk mitigation and governance. It is also suited for enterprises integrating mixed‑model AI workflows across multiple cloud providers.
Features
- SDK and proxy integration that capture full runtime traces, tool provenance, execution context, latency, and token usage without code rewrites
- Multi‑provider compatibility (OpenAI, Anthropic, Gemini, and mixed‑model deployments) through a single decision layer
- Automated adversarial testing engine that evaluates behavioral risk against configurable security policies
- Policy enforcement engine that issues release decisions and blocks unsafe agent actions in real time
- Replayable evidence storage with immutable audit logs for post‑incident analysis and compliance reporting
- CI/CD and incident‑response hooks for continuous security review across development, staging, and production
- Centralized governance dashboard exposing trace visualizations, risk scores, and policy compliance metrics
- API‑first design with FHIR‑compatible endpoints for integration into existing MLOps and security tooling