Patent Pending · Built by Raksha AI
Operational Safety
for Agentic AI
Govern what your agents can do and what they can acquire & know — at runtime, at scale, with an immutable audit trail.
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Agent Governance Plane
Runtime enforcement between agents and every tool they can reach. Identity, behavior profiles, policy, approvals, and an immutable audit trail.
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Context Governance
Policy-mediated control over what agents can acquire, retain, reason over, and operationalize — across shell, browser, MCP, and every capability surface.
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CaSH
Context-Aware Shell. Intercepts agent shell invocations at the execution layer — not the tool-name layer — so no bypass is possible.
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CABR
Context-Aware Browser Runtime. Governs what browser agents can acquire and know, defending against ambient authority and credential exposure.
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Behavior Profiles
Identity-bound operating envelopes for agents. Approved tool access, data scopes, autonomy levels, and runtime context constraints — as code.
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Context Firewall
The last line of defense. A policy gate between acquired context and the model's reasoning — ensuring agents only acquire & know what they're allowed to.
Empirical Result · May 2026
One prompt. Three steps. Seven credential types exposed.
A production browser agent asked to "summarize this page" autonomously exposed AWS keys, a Stripe live key, a PostgreSQL password, three customer SSNs, a Kubernetes token, and a webhook secret — without prompt injection, without compromise. The browser handed the agent everything it had.
Read the Threat Model →