Brex built its AI agent policy by watching what agents actually do, not by writing rules first

OpenClaw has become one of the most widely adopted agentic frameworks, but it has yet to prove itself at enterprise scale. Agents need real credentials — API keys, OAuth tokens, service accounts — to work effectively, and Brex found that traditional guardrails couldn't contain what those agents were doing with them. Brex set out to overcome these limitations by building an internal platform it calls CrabTrap. The open-source HTTP/HTTPS proxy intercepts all network traffic, examines policy rules, and uses a LLM-as-a-judge to decide whether agent requests should be approved or denied. “What we noticed was that the network layer was an untapped enforcement point,” Brex co-founder and CEO Pedro Franceschi told VentureBeat. “Every request an agent makes is an opportunity to intercept, reason about, and make a policy decision.” The takeaway Franceschi wants IT leaders to draw: agent governance should shift from SDK-level permissions and model guardrails toward a centralized network control plane that enforces and learns from real in-the-wild agent behavior. How Brex targeted the transport layer The “obvious fix” (at least initially) to the agent security gap was guardrails, and much of the early work has centered on scoped tools, per-action permissions, and human-in-the-loop approvals. But as agents evolve, each new capability means there’s another API to tune or surface to audit, Franceschi noted. “Any agentic system with multiple tools and access to the open internet creates an immediate tension for builders: The more capable you make an agent, the more dangerous it becomes, and the safer you make it, the less useful it is,” he said. Existing solutions to this tradeoff were “weak”: Fine-grained API tokens help at the margins but can still be misused and constrain functionality. Semantic guardrails (such as context, skills, or prompt steering) are easily bypassed by prompt injection, especially for agents connected to the internet. Agents can be “defanged” when given read-only access or limited toolsets, but then they can't do meaningful work, Franceschi said. On the other hand, granting broad write access and a large tool surface can result in hallucinations and real production consequences. Model context protocol (MCP) gateways enforce policy at the protocol layer — but only for traffic using MCP. Meanwhile, guardrails from LLM providers are tied to a single model and can be “opaque” to customize with enterprise-specific policies. And powerful tools like Nvidia OpenShell offer more of a “per-sandbox egress control.” “When we started, we hadn’t found a solution to deploying harnesses like OpenClaw safely,” Franceschi said. “Instead of waiting for the industry to catch up, we decided to own the problem and invent the necessary tools.” Notably, they needed a platform that sat between every agent and every network request, and could make “nuanced decisions about what to allow,” he said. This made the transport layer a core architectural component and natural starting point, he said. By operating at this layer, CrabTrap is framework-agnostic, language-agnostic, and API-agnostic. It doesn't require SDK wrappers or per-tool integration. Users set HTTP_PROXY and HTTPS_PROXY in the agent's environment, and every outbound request routes through the proxy before it reaches a destination. However, Franceschi emphasized, Brex didn't start at the transport layer because it thought it was the only answer; rather, they believe in “security by layers.” “The transport layer was simply an underinvested one, and we saw an opportunity to add meaningful enforcement there alongside everything else,” he said. The LLM-as-a-judge training loop CrabTrap combines deterministic static rules with an LLM-as-a-judge for requests that fall outside known patterns, Franceschi explained. The judge only “fires on the long tail of unfamiliar endpoints or unusual request shapes,” which for a mature agent is typically fewer than 3% of requests. The more pressing problem was how to know that a policy is the right one? With static rules, it's “relatively straightforward” to reason about accuracy. But with an LLM judge, the system is nondeterministic, and users need confidence that the policy approves the right requests and blocks the rest. “Our key insight was to bootstrap policy from observed behavior rather than write it from scratch,” Franceschi said. Beginning with real behavior and editing down based on real-world learnings turned out to be “dramatically more effective than starting from a blank page.” Brex’s team built a policy builder (itself an agentic loop) that runs underlying agents in shadow mode, analyzes historic network traffic, samples representative calls, and drafts a natural-language policy that matches what the agent actually does. From there, they built an eval system that tests policy changes before they go live. CrabTrap compares historical audit entries against a draft policy and reports the exact changes to be made. Users can slice results by method, URL, original decision, and agreement status. All of this runs with concurrent judge calls, so replaying thousands of requests “takes minutes, not hours,” Franceschi said. Brex also developed a live feedback loop: Full audit trails are stored in PostgreSQL and queryable through the admin API and dashboard. In cases where a resource is continuously denied, the system can notify a human or an agent to propose a policy update for review. “That closes the loop between observed denials and policy refinement,” Franceschi said. Core challenges and roadblocks Of course, the build wasn’t without its challenges. A big one was latency: “Putting an LLM between an agent and every outbound API request sounds like it would grind things to a halt,” he said. However, it didn’t turn out to be as big a problem as expected. This was for two reasons: The LLM judge only activates on a small fraction of requests (the aforementioned 3%). Agents quickly settle into predictable traffic patterns; once observed, high-volume patterns become static rules. Second, by using small, fast models like Claude Haiku meant that, even when the judge did fire, added latency was “negligible.” This can be further reduced with local models and prompt caching, Franceschi said. The harder and less obvious challenge was prompt injection, he said. The judge receives the full HTTP request and all content is user-controlled, so potentially, a crafted URL, header, or request body could manipulate the judge's decision. Brex addressed this by structuring the request as a JSON object before sending it to the model, so all user-controlled content is “escaped rather than interpolated as raw text,” Franceschi said. Results, and where CrabTrap might evolve Brex tracks a few factors to measure CrabTrap’s internal impact: Engagement with agents, network traffic patterns, and net promoter scores (NPS). The most meaningful result of CrabTrap has been “organizational confidence,” Franceschi said. Previously, the team had “real hesitation” when it came to deploying autonomous agents broadly across business operations, because the existing guardrail options didn't provide enough assurance. “CrabTrap changed that calculus,” Franceschi said. They now have an enforcement layer they trust, increasing confidence around expanding agent deployment into more parts of the business and delegating more agent configuration and management to users. Franceschi described the policies derived from traffic as “surprisingly strong.” The team expected the policy builder to produce a “rough starting point” requiring heavy manual editing. In practice, though, pointing the platform at a few days of real traffic produced policies that matched human judgment on the “vast majority of held-out requests.” Additionally, CrabTrap revealed how much noise agents generate. “The audit trail made this visible for the first time,” Franceschi said. They used denial logs and traffic analysis not only to tune policies, but to tighten agents themselves, remove tools, and cut out entire categories of requests that were wasting both time and tokens. “The proxy became a discovery tool, not just an enforcement one,” he said. Areas for growth (and input from the open-source community) Brex anticipates CrabTrap to continue to evolve, particularly as they have released it as open-source. “We hope the community helps shape it,” Franceschi said. Areas of improvement include deeper authentication functionality such as single-sign on (SSO), fine-grained role-based access control (RBAC); escalation workflows that allow agents to request additional permissions; and policy recommendations based on denial patterns. Programmatic configuration, or developing API endpoints for “creating, forking, and applying” policies to agents, could allow the whole policy lifecycle to be automated rather than managed manually, Franceschi said. As for escalation, if an agent is continuously denied a given resource or endpoint, it should be able to route requests to humans or other AI agents for review and back that up with a rationale for why it needs access. “That turns CrabTrap from a hard enforcement boundary into something more like a managed permission system,” Franceschi said. Additionally, the policy was built to bootstrap from network traffic, but there is opportunity to incorporate additional signals around agent traces and resource-calling, as well as broader context on what agents are ultimately trying to accomplish. This can help produce more accurate and nuanced policies. Finally, there's an “open philosophical question” about the right posture for CrabTrap: Should it be a fully transparent layer that the agent itself is unaware of, or should it operate more like a “well-intentioned manager”? (that is, the agent knows about the layer and can interact with it). The open-source community can help shape these developments, and CrabTrap will only get better with more users, Franceschi said. Brex’s agents speak to a specific set of APIs; teams using CrabTrap with different agents, services, and policy requirements will surface “edge cases and patterns we can't hit alone.” “We have ambitious plans for where it could go, and we’d rather build in the open,” Franceschi said. What other builders can learn from CrabTrap The response has been stronger than expected. CrabTrap has more than 700 stars on GitHub . Franceschi said Brex has also heard from OpenAI, Y Combinator CEO Garry Tan, and programmer Pete Steinberger, all expressing interest in deploying similar internal infrastructure. The broader lesson: “Don't let infrastructure gaps become excuses to wait," Franceschi advised. There are “real blockers” for every enterprise looking to seriously deploy AI agents, including security concerns, lack of tooling, or unclear guardrails. “It's tempting to sit on your hands until the industry catches up,” he said. “The lesson from CrabTrap is that you can own those problems directly.”