Key Takeaways
- Proxy-based tools validate the need for real-time agent policy enforcement. For enterprises, they address only part of what governance requires.
- For enterprises, a proxy layer alone doesn't cover the requirements. Auditability, policy enforcement, and evaluation need to work together from the start.
- Governance assembled from disconnected tools creates fragility, and teams are already running into that.
- A purpose-built AI control plane handles telemetry, evaluation, monitoring, policy enforcement, and governance as a single system.
The question of how to govern AI agents in production is no longer theoretical. Tools like CrabTrap, Brex's open-source LLM-as-a-judge HTTP proxy, validate the need: real-time policy enforcement for agents is both feasible and necessary. But for enterprises deploying agents across regulated environments, a proxy addresses only part of what governance requires.
The requirements at that scale go beyond what an interception point handles: you need to know who authorized what, produce audit trails that satisfy legal and compliance, enforce policy consistently across infrastructure you don't fully control, and do all of that without your evaluation costs approaching your inferencing costs. A proxy can intercept and block. It doesn't give you the governance system enterprises need.
Why AI Agent Governance Can't Be Added After the Fact
Most teams hit this problem after they've already shipped. They built something, moved fast, got to production, and then realized they have no consistent evaluation framework, no audit trail that would satisfy legal, and no enforcement mechanism between the agent and the systems it can reach.
They go looking for tools and find a market full of point solutions, some focused on evals, some on monitoring, some on guardrails, most not designed to work with each other. Every integration becomes a custom project. Every new model or agent framework means renegotiating the stack. Governance assembled from disconnected tools has the same fragility as LLM wrappers built on top of models.
What Enterprises Need From AI Agent Policy Enforcement
What enterprises need is policy enforcement: defining what agents are allowed to do, applying those rules at runtime, and producing records that hold up to scrutiny.
That matters because the questions have changed. It's not just "did this output hallucinate?" It's "did this agent access what it was authorized to access? Did it stay within scope? Can I show a regulator that it did?" Monitoring tells you what happened. A governance system lets you define and enforce what's supposed to happen, and demonstrate it after the fact.
What an AI Control Plane Does
Governing AI agents in production requires an AI Control Plane that is a system of trust that includes five capabilities working together: standardized telemetry, reliable evaluation, continuous monitoring, enforceable policy, and auditable governance. That support spans both layers where agents operate: in production across 1st and 3rd party agents, LLM applications, and predictive AI deployments, and at the creation layer, where coding agents are writing code, accessing MCP tools, and establishing behavior patterns. The Fiddler AI Control Plane is built to cover both.
Integral to the platform are the secure Fiddler Centor Models, which power the industry's fastest guardrails and evaluations, handling tasks that require low latency and cost-effectiveness as well as complex reasoning with accuracy. Compared to external LLMs, Fiddler Centor Models offer a compelling TCO with no hidden costs or Evaluation Trust Tax.
Infrastructure independence matters too. Enterprises deploying agents at scale aren't standardizing on one cloud or one framework. They need governance that works regardless of what the underlying systems are built on or where they run.
For a deeper technical look at how a control plane differs from a gateway and where the two work together, read our technical guide.
What Proxy Tools Are Telling Us
Tools like CrabTrap show that developers want to build real-time, policy-driven governance for agents and that the architecture to do it exists. Those ideas are worth taking seriously.
Enterprise deployments need that same capability with production reliability, security controls, audit trails, and evaluation built in from the start. Cloud computing got a control plane. Container orchestration got a control plane. AI agents need one too, and teams moving fast on agents are already running into the consequences of not having one.
If that's where you are, we'd like to talk.
Frequently Asked Questions
What is an AI control plane?
An AI control plane is the system that governs how AI behaves in production. It handles standardized telemetry, evaluation, monitoring, policy enforcement, and auditable governance as a unified system rather than as separate tools stitched together.
What is the difference between AI observability and AI governance?
AI observability tells you what happened. It surfaces outputs, traces, and metrics from your AI systems. AI governance goes further: it lets you define what your agents are allowed to do, enforce those rules at runtime, and produce audit trails that satisfy legal, compliance, and regulatory requirements.
What is policy enforcement for AI agents?
Policy enforcement for AI agents means defining rules for agent behavior, applying them in real time as agents make requests, and blocking actions that fall outside authorized scope. It answers questions like: did this agent access what it was authorized to access? Did it stay within scope? Can I prove that to a regulator?
Why isn't a proxy layer enough for enterprise AI agent governance?
A proxy can intercept and block agent requests, but it doesn't provide the full governance system enterprises need. That includes knowing who authorized what, producing audit trails that satisfy legal and compliance requirements, enforcing policy consistently across infrastructure you don't fully control, and keeping evaluation costs manageable at scale.
How do enterprises govern AI agents in production?
Governing AI agents in production requires standardized telemetry, reliable evaluation, continuous monitoring, enforceable policy, and auditable governance working together as a single system. A control plane is different from an agent harness. A harness helps you build and orchestrate agents. A control plane governs them in production. Enterprises that try to assemble this from point solutions typically end up with gaps, especially around consistent policy enforcement and audit trails across different models, frameworks, and infrastructure.