Agent Control

Most agent governance tools force you into a specific framework or guardrail provider. Agent Control takes a different route: it's an open-source layer that sits on top of whatever agents you already have, intercepting steps via a decorator and checking them against centralized policies. That's a big deal if you're running a mixed fleet across LangChain, CrewAI, and OpenAI Agents SDK—you get one control plane instead of bolting on separate guardrails per framework. The pluggable guardrail providers are another strong point. You can start with one provider and switch or mix later, which keeps you from being locked in. Real-time policy updates mean you can tighten or loosen rules without redeploying agents—useful when a new compliance requirement lands mid-cycle. Audit logs capture full traces, which is exactly what you need for evidence in regulated environments. Where it bites: there's no GUI. If your team prefers clicking through a dashboard to approve policies or view audits, this tool will feel bare-bones. You'll be in YAML and code. Also, SSO and user management aren't built in, so if you need fine-grained access control for multiple teams, you'll have to handle that yourself. The Apache 2.0 license means you can self-host and modify it, but you're also responsible for running and securing the control plane. The closest alternative is Guardrails AI, which also focuses on ML-based guardrails but is more framework-agnostic and offers a hosted option. Agent Control differentiates with its multi-framework support and the Control Store concept—you aren't tied to a single guardrail provider's ecosystem. For a team already using one of its supported frameworks, adoption is a few lines of code. In practice, we'd reach for Agent Control when you need to enforce