Enterprise AI isn’t failing because of weak models. It’s failing because of weak controls.
That’s the thesis behind the latest update from Redpanda, which has rolled out new core capabilities for its Agentic Data Plane (ADP). The release introduces a centralized AI Gateway, OpenTelemetry-based AI observability and evaluation, built-in AI agents, and unified authentication and authorization.
Taken together, the features form what Redpanda describes as a governance layer for AI agents—designed to securely connect agents and Model Context Protocol (MCP) servers to live enterprise data with visibility and policy enforcement baked in.
In short: if enterprises want agents operating on real systems—not just sandbox demos—they need a control plane.
From Building Agents to Governing Them
Over the past year, spinning up an AI agent has become almost trivial. Productionizing one inside a regulated enterprise environment? That’s where things break down.
Redpanda CTO Tyler Akidau argues that the enterprise agentic AI market is stalling not because of model performance, but because organizations lack centralized control over how agents access systems and data. Rather than configuring access rules at every individual data source, ADP acts as a single connectivity layer through which all agent interactions flow.
The shift mirrors what happened in cloud security years ago: perimeter-based thinking gave way to centralized identity and policy enforcement. Redpanda is attempting to apply that lesson to AI agents.
AI Gateway: A Traffic Cop for Agent Workflows
The new AI Gateway serves as a unified access layer between applications, AI models, and MCP services. It centralizes:
- Routing of AI traffic
- Policy enforcement
- Cost controls
- Observability
Enterprises can define token budgets and spending limits—critical as model usage costs scale unpredictably. Redpanda also supports deferred tool loading to optimize resource usage during complex agent workflows.
Notably, the Gateway aggregates and governs MCP servers via an admin-controlled registry, using YAML-based configuration for deployment. That design signals Redpanda is targeting platform teams and DevOps engineers, not just AI experimenters.
As enterprises juggle multiple models, vendors, and internal services, a centralized AI traffic layer could become as essential as an API gateway.
AI Agents—Bring Your Own or Use Redpanda’s
Redpanda ADP is framework-agnostic. Organizations can govern agents built on existing frameworks or opt for Redpanda’s managed AI agents.
All agents—internal or external—interact through open standards such as the A2A protocol and plug into ADP’s unified authentication, authorization, and observability services.
Agents can also be triggered through Redpanda Connect pipelines, enabling real-time, event-driven workflows and human-in-the-loop processes. That’s a key differentiator: unlike batch-oriented AI orchestration platforms, Redpanda’s streaming foundation is built for low-latency execution.
With more than 300 connectors available through Redpanda Connect, enterprises can expose data from SaaS platforms, databases, data streams, and data lakes without moving it—an increasingly important architectural requirement as data gravity grows.
Unified Identity: No More Rogue Agents
Security-wise, ADP enforces OIDC-based identity and fine-grained authorization policies across every component.
Every request—whether from a human user, service account, or AI agent—is authenticated and governed. The platform eliminates long-lived credentials, reducing the risk of agents retaining uncontrolled access to sensitive systems.
This reflects a growing concern in enterprise AI: agents granted overly broad permissions can become internal attack vectors or compliance liabilities. Redpanda’s approach shifts control to a central policy layer rather than scattering permissions across systems.
Observability and Evaluation via OpenTelemetry
Redpanda ADP emits metrics, traces, logs, and transcripts using the OpenTelemetry Protocol (OTLP). Enterprises can inspect agent behavior in the Redpanda console or export telemetry to third-party observability platforms.
That matters for three reasons:
- Debugging agent behavior
- Compliance and audit requirements
- Post-incident forensics
Unlike many AI orchestration tools that treat model outputs as opaque transactions, Redpanda aims to provide full-fidelity traces across inputs, intent, and outputs.
As regulatory scrutiny around AI grows, traceability may shift from “nice to have” to mandatory.
Why This Matters Now
Industry analysts have repeatedly pointed to governance and real-time data integration as the bottlenecks for enterprise AI adoption. Consumer AI thrives because it operates in low-stakes environments. Enterprises operate in high-stakes ones.
Redpanda is betting that the winning AI infrastructure won’t just orchestrate prompts—it will govern identity, cost, data access, and observability from day one.
By building ADP on a streaming backbone, Redpanda also differentiates itself from static AI workflow platforms. Real-time context updates and event-driven triggers position the platform closer to operational systems rather than experimental AI sandboxes.
The Bigger Picture: The Rise of the Agentic Control Plane
The broader trend is clear: enterprises are moving from experimentation to operational AI. And operational AI demands governance.
Just as Kubernetes introduced a control plane for container orchestration, companies now need an equivalent control layer for AI agents—covering identity, policy, cost management, and telemetry.
Redpanda’s latest release positions ADP as that layer.
Whether the market consolidates around streaming-native platforms or standalone AI gateways remains to be seen. But one thing is increasingly obvious: without centralized governance, enterprise agents won’t scale.
And that’s a problem no large organization can afford.
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