Rebuno enforces execution guardrails for AI agents via policy-driven runtime
Builders need a reliable runtime that enforces guardrails independently of model prompts to maintain safety and auditability in AI agent workflows, especially when integrating heterogeneous frameworks and tools.
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Rebuno open-sources a runtime enforcing AI agent execution policies
Rebuno released an open-source runtime that manages execution state and guardrails for AI agents across multiple frameworks. It dispatches work via signed webhooks, evaluates per-agent YAML policies before tool and model calls, and records all decisions and outcomes in an append-only event log. It supports retries for safe tools and human approval gating.