Fiddler AI Secures $30M Series C to Power Control Infrastructure for Autonomous AI

Fiddler AI has raised $30 million in Series C funding in a round led by RPS Ventures, with participation from existing investors Lightspeed Venture Partners, Lux Capital, Insight Partners, Capgemini Ventures, Dallas VC, Dentsu Ventures, and Mozilla Ventures, alongside new strategic investors LG Technology Ventures, Benhamou Global Ventures, and LDV Partners. The raise brings Fiddler’s total funding to $100 million.

The new capital will support Fiddler’s push to become a neutral control plane for compound AI systems as enterprises increasingly deploy autonomous agents across business-critical workflows.

The Governance Gap Emerging Around AI Agents

Enterprise AI has entered a new phase. What began as narrowly scoped predictive models has evolved into agentic systems capable of reasoning, calling tools, interacting with external APIs, and making decisions with limited human oversight. While these systems unlock powerful efficiencies, they also introduce new layers of risk.

Traditional AI monitoring tools were built for deterministic models with well-defined inputs and outputs. Autonomous agents break that paradigm. A single workflow may span multiple models, decision chains, and third-party services, each adding opacity and potential failure points. When something goes wrong, tracing responsibility across the system becomes difficult, if not impossible.

This gap between capability and control is now one of the primary barriers to scaling agentic AI inside large organizations, particularly in regulated environments where explainability and auditability are mandatory rather than optional.

From Model Observability to System-Level Control

Fiddler’s platform is designed around a system-level view of AI behavior. Rather than monitoring individual models in isolation, it provides a unified layer for telemetry, evaluation, continuous monitoring, and policy enforcement across predictive models, generative AI, and autonomous agents.

The company positions this as a control plane—a neutral system of record that captures what AI systems are doing, why they behave the way they do, and whether their actions comply with internal policies and external regulations. This distinction matters as enterprises move from experimentation into production deployments where AI decisions have real financial, legal, and reputational consequences.

Unlike point solutions that focus narrowly on developer debugging or outsource evaluation to external models, Fiddler emphasizes built-in trust mechanisms that can operate directly within enterprise environments. The goal is to give organizations consistent oversight without forcing them to stitch together fragmented governance tooling.

Why Enterprises Are Moving Now

The urgency around AI governance is being driven less by hype and more by exposure. Autonomous agents increasingly interact with customers, influence pricing, approve transactions, and make recommendations that carry regulatory and ethical implications.

Failures in these systems can lead to fines, litigation, or brand damage, especially in sectors such as financial services, healthcare, and insurance. As a result, enterprises are looking for infrastructure that allows them to deploy agents confidently while retaining meaningful control.

Fiddler reports strong adoption in these environments, alongside rapid revenue growth over the past 18 months. Recognition in AI agent security and risk management categories reflects how observability and governance are becoming core requirements rather than afterthoughts.

Built on Years of Explainability Work

Founded in 2018, Fiddler began with a focus on explainability, root-cause analysis, and responsible AI—areas that were often treated as secondary concerns during earlier waves of machine learning adoption. That foundation now underpins its expansion into compound AI systems.

Understanding why a single model behaved a certain way is challenging enough. Understanding why an autonomous system composed of many interacting components took a specific action requires deep instrumentation and contextual analysis across the entire workflow. Fiddler’s platform is designed to surface that context in real time, enabling teams to diagnose issues before they escalate.

Scaling Across the AI Ecosystem

With this Series C funding, Fiddler plans to deepen integrations across the AI ecosystem and expand its presence in industries where governance is non-negotiable. As enterprises adopt a growing mix of proprietary models, foundation models, and third-party tools, the need for a vendor-agnostic control layer becomes more pronounced.

Rather than locking organizations into a specific model or framework, Fiddler aims to sit above the AI stack, providing consistent oversight regardless of how the underlying system evolves.

What This Signals for the Future of AI Infrastructure

Fiddler’s raise highlights a broader shift in enterprise AI. As autonomy increases, value creation moves higher up the stack—but so does risk. The next phase of AI adoption will be defined not just by smarter models, but by the infrastructure that makes those models reliable, auditable, and safe to operate at scale.

Just as cloud computing required orchestration, monitoring, and security layers to become viable, autonomous AI is driving demand for control planes that provide visibility and enforcement across complex systems. In that sense, governance is no longer a constraint on innovation; it is becoming a prerequisite for deploying AI responsibly in the real world.