Suvra

Policy-first execution guardrails for AI agents

Suvra sits between your AI agent and real-world actions.
Allow, block, or require human approval - before anything executes.
Deterministic policy enforcement with full audit logging.→ Deny-by-default enforcement. No accidental side effects.
→ Deterministic policy engine. No LLM in the enforcement path.
→ Full audit trail with rollback. Every action logged, every decision explainable.

pip install suvra

How Suvra works

Agent → Suvra Policy Engine → Executor → Audit LogAgents submit actions.
Suvra evaluates them against policy rules.
Every decision is logged and explainable.

Add guardrails to an AI agent in minutes

pip install suvrafrom suvra import Guard
guard = Guard(policy="policy.yaml")result = guard.execute({
"actionid": "agent-task-1",
"type": "fs.writefile",
"params": {
"path": "workspace/output.txt",
"content": "done"
},
"meta": {"actor": "my-agent"}
})

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