The Control Plane for AI Agents
Agents Are In Production. Do You Know What They’re Doing?
Agents are first-class enterprise actors, making autonomous decisions across your operations. Agents aren’t just sending data out. They’re pulling it in through MCP servers, WebFetch calls, and tool endpoints that can return PII or PHI directly into agent context. That inbound vector is the one existing security tooling wasn’t designed for. By the time the data is in the agent’s context, it’s already too late to stop it from landing in a prompt, a response, or committed code.
Most organizations don’t fully know what they’re accessing, what they’re spending, or what sensitive data has moved through them.
That’s not a coding agent problem. That’s an enforcement and observability problem.
Visibility. Context. Control.
Fiddler is the system of trust for your agents. The Fiddler AI Control Plane spans across the creation layer and the production layer, covering first party, third party, and coding agents.
The Fiddler Control Plane
Standard Telemetry
Reliable Evaluation
Continuous Monitoring
Enforceable Policy
Auditable Governance
The Only Inline Enforcement at the Agent's Request and Response Path
One Control Plane. Through the Gateway You Already Run.
The Only Inline Enforcement at the Agent's Request and Response Path, Guaranteeing Safe Outcomes
Models only process approved inputs. Developers only receive approved outputs. Enforced at the gateway, in both directions.
- PII/PHI in a developer’s prompt, caught before it reaches the model. The model only processes approved inputs.
- PII/PHI returned by a tool or endpoint, caught before it enters context. The developer only receives permitted outputs.
- Inline secrets detection and redaction, a key in a prompt, a .env or config file read into context, a token returned by an MCP server, caught before they go anywhere.
Agentic Fleet Intelligence Across Every Developer, Token, and Dollar
Observability beyond performance = organizational understanding of how AI is working across every part of your business.
- Token usage, spend, latency, throughput, and adoption broken down by developer, model, and team.
- Pairs cost and performance analytics with security in a single pane.
- Native OpenTelemetry, no instrumentation; full telemetry through your gateway.
Frequently Asked Questions About the Control Plane for AI Agents
What is a Control Plane for AI?
An AI Control Plane is the system of trust for AI agents, providing standardized telemetry, reliable evaluation, continuous monitoring, enforceable policy, and auditable governance across the AI agent lifecycle. Unlike observability tools that only show what happened, a Control Plane for AI agents actively governs what agents can do, enforcing enterprise rules in real-time and preventing violations before they occur.
Why do AI agents need a Control Plane?
AI agents make autonomous decisions that trigger cascading tool calls and policy decisions, which can fail or violate compliance requirements. A Control Plane for AI agents provides visibility into agent behavior, context for decision-making, and control through runtime guardrails. This prevents security risks, enables deployment of high-performance agents that drive business results, and maximizes ROI by reducing incident response time while providing governance and executive oversight.
What's the difference between AI observability and AI Control Plane?
AI observability monitors what agents are doing. An AI Control Plane adds enforcement and governance. The difference is timing: flagging is not enforcing. An evaluator that scores a risk after the agent has already acted documents the incident, it doesn't prevent it. Fiddler observes and enforces on the same request, before any data moves.
How does a Control Plane help with AI compliance and regulations?
A Control Plane provides auditable governance, capturing every decision with full traceability for frameworks like NIST AI RMF, ISO/IEC 42001, HIPAA, SR 11-7, NAIC, and the EU AI Act. Guardrails run in your own environment, so prompts never leave your infrastructure, which is often the difference between a control you can deploy and one procurement won't approve.
What capabilities should an AI Control Plane include?
An AI Control Plane should integrate five core capabilities:
- Standard Telemetry (capturing AI agent lifecycle data)
- Reliable Evaluation (testing agents before deployment)
- Continuous Monitoring (100+ metrics for hallucination, toxicity, PII/PHI)
- Enforceable Policy (runtime guardrails blocking harmful outputs)
- Auditable Governance (complete evidence to support audit trails).
These must operate as a system of trust for AI agents, not fragmented tools.
What are the key use cases for an AI Control Plane?
The Control Plane for AI agents enables business-critical use cases:
- Customer service agents preventing PII leaks
- Healthcare AI agents protecting PHI and blocking biased clinical recommendations
- Insurance agents with NAIC-aligned governance
- Financial services gain full audit trails for regulatory compliance and oversight
- Code generation agents monitored for security
- Business process automation coordinating multi-agent workflows with complete visibility and control
What are the benefits of an AI Control Plane?
An AI Control Plane delivers three benefits:
- deploying high-performance AI agents through continuous evaluation
- preventing compliance risks via runtime guardrails that block hallucinations and PII leaks
- maximizing ROI by reducing incident response time while cutting compliance efforts
This enables organizations to scale AI agents confidently without fragmented tooling or manual oversight bottlenecks.