Deploy agents you can trust with centralized AI governance

Most organizations we encounter want to deploy enterprise agentic systems, but they’re currently stuck at agent demos and vibe coding. The primary reason? CIOs say they can’t trust autonomous agents. This roadblock leads to stalled agentic projects and reallocated sprints to build a custom solution (or scrapping the projects altogether).

In a recent Tech Talk, Inside the Agentic Data Plane for IT Leaders, Redpanda CTO Tyler Akidau discussed why the promise of enterprise AI hasn't yet materialized, and why the missing piece of the puzzle is a central governance layer built specifically for agents (not humans). 

We’ll cover some of the highlights in this blog, including why AI governance frameworks built for humans fall short for autonomous agents, and what the missing layer looks like.

Why are organizations struggling to deploy agentic systems?

Because the tooling to trust agents in production doesn’t exist yet in most stacks. Humans built governance for people, not agents. There’s no unique agent identity, no audit trail, and no kill switch if things get out of control. That gap is why most enterprise AI is stuck in demo mode.

The struggle can be tied to a few key reasons:

• The autonomy issue: Autonomous agents are capable, but they’re far more unpredictable than human workers. Organizations are hesitant to push agents to production without proper guardrails and oversight.
  
  
• The infrastructure gap: Existing governance systems were designed for human workers and deterministic software that performs a predetermined set of commands. These systems can’t address common problems with agents (like hallucination and prompt injection).
  
  
• The lack of granular controls: Agents require explicit governance to understand what they’re doing, what data they access, and how much budget they’re using—and the ability to shut them off if they’ve gotten out of control.

Rather than focus on building perfect agents, the real goal is to build a system that makes even the imperfect agents safe. For this, one principle must underpin everything: governance must be enforced by infrastructure that agents cannot access or modify.

Tyler discusses all of these issues in detail in the Tech Talk, so make sure you give it a watch to dig deeper. (If you're more of a skimmer, here's a two-pager with the main takeaways you can download.)

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How can organizations trust agents with their data?

By placing a single governance layer between your agents and your data. A governance framework is always the answer; you just need a version built to address the unique needs of enterprise AI.

Critically, you need a platform separate from your agentic infrastructure so the agents can’t govern themselves. This single governance layer must deliver four key pillars:

• Trust: Agent identities, tool calls, and data access points are all specified.
• Explainability: All agent actions are recorded, replayable, and auditable.
• Context: Agents have task scopes and can only connect to the systems they need.
• Control: Agents have limits on actions, access, and budget, with a kill-switch option.

Such a solution should also include the capabilities you need to safely scale agents, from AI gateways and MCP hosting to observability and budgeting—all in one place. 

Introducing the Redpanda Agentic Data Plane

Redpanda’s Agentic Data Plane is AI governance infrastructure for enterprise agents. It sits between an organization's agents and everything they access: data, tools, identities, and models. Every agent gets an identity, every tool call and data access is mediated, and every action is recorded for replay and audit. 

It gives you everything you need to run agents on real enterprise data using a centralized set of rules, and builds on globally-trusted data infrastructure for unified agent governance and real-time streaming.

Here’s how the Redpanda Agentic Data Plane makes sure you can trust agents in production:

• Every agent gets a verified identity. No anonymous agents acting on your data, so you always know who did what—and on behalf of which human.
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• Every action is recorded and replayable. The audit trail is tamper-proof by design, not a separate system you maintain. No agent can alter the log.
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• Every agent is scoped to only what it needs. Set task limits and least-privilege access so a compromised or confused agent can't reach beyond its job.
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• Every agent can be stopped and capped. Cap spend, access, and instantly deactivate any agent if it goes off the rails. 
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• A single governed gateway. Connect agents to any model, cloud, or database through one gateway. One clear path in and out, not a sprawl of point integrations.
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• One pane of glass. See spend and activity for every agent and server in one dashboard, whether the agent is Redpanda-hosted or your own.

Learn more from our Head of AI about what an agentic data plane is and why you need one. 

What makes the Redpanda Agentic Data Plane different?

On paper, many AI governance vendors offer the same features: AI gateways, MCP hosting, on-behalf-of identity, tool-call policy, evaluation, tracing. Two questions tell them apart: where does governance run, and what can it actually govern?‍

Redpanda Agentic Data Plane is a neutral governance plane that runs inside your own cloud account and governs anything you run—any model, any API, any database, on any cloud. Here’s what you can count on: 

• Governance by default, not bolted on. Rivals add policy and observability as separate tools you wire in yourself. We make secure, auditable behavior a property of how every agent runs. The audit trail is a byproduct, not a project, and teams can't ship an agent without it.
  
  
• Open architecture. A vendor that sells you the model governs only its own walls, and you inherit its roadmap for anything you swap later. We sit above the choice of model, cloud, database, and framework. No walled gardens, no lock-in.
  
  
• Governed data access out of the box. Competing connectors and templates need a separate governance integration before they're enterprise-safe. We make data connectivity governable from the start, with no extra layer to build.
  
  
• Cost capped before the bill, not after. Provider dashboards show spend after it's gone. We calculate and cap it per agent and per provider budget, before a runaway loop or a prompt injection turns into an end-of-month surprise.

Watch the full Tech Talk for more

If your organization wants to become an agentic enterprise but can’t figure out how to trust your agents let alone scale them, this talk is a must-watch. Check out the full Tech Talk on Inside the Agentic Data Plane for IT Leaders to learn:

• The challenges today’s IT leaders are facing when deploying agentic systems
• Why agents aren’t the villain—but do require stricter oversight
• How enterprises can scale agentic AI safely
• The capabilities you need to consider for effective agent governance
• Answers to the most common questions we’re hearing from CIOs

If you’re ready to roll and want to take your agents from prototype to production, book a demo to see the Redpanda Agentic Data Plane in action.