Is your data pipeline architecture ready for agentic AI workloads?

Most European infrastructure teams built their data pipelines for a specific job: move data from operational systems into a warehouse, run transformations overnight, surface results in a BI dashboard by morning. That architecture works well for what it was designed for. It fails badly when an autonomous AI agent needs a decision-relevant slice of context in under 100 milliseconds, at 3 a.m., while simultaneously coordinating with four other agents and writing back to a shared memory store.

Agentic AI is not a faster version of the analytics workloads your pipeline already handles. It is a fundamentally different class of system, and the infrastructure gap between demo and production is where most deployments stall. According to McKinsey’s 2025 AI report, nearly two-thirds of enterprises were already experimenting with AI agents, yet only one in ten successfully scaled beyond the pilot stage. The most common reason was not model quality. It was data infrastructure.

Understanding what breaks, and why, is the starting point for fixing it.

What agents actually demand from a data layer

A conventional ML model is passive. You send it a prompt, it returns a prediction, the transaction ends. An AI agent pursues goals across multiple steps, invokes tools, writes to memory, queries external systems, and feeds its own outputs back as inputs to the next reasoning step. That feedback loop has no tolerance for batch latency. A pipeline that refreshes context every 15 minutes is not a slow agent pipeline; it is a broken one.

The specific infrastructure requirements that emerge from this are not subtle. Agents need persistent memory with long retention horizons (research points to three to five years for enterprise context), low-latency inter-agent communication, heterogeneous compute that can handle both orchestration logic and inference simultaneously, and a streaming layer that delivers continuously refreshed, high-quality context. Token consumption for agentic workloads runs 20 to 30 times higher than for single-inference generative AI, which means every inefficiency in the data path compounds at scale.

Data quality becomes a first-order concern rather than a background hygiene task. Agent quality is directly proportional to the freshness, completeness, and reliability of the streaming context informing each decision. Data teams already spend up to 40% of their time on quality tasks in conventional pipelines; agentic workloads multiply the number of data touchpoints exponentially, making that burden unsustainable without architectural change.

The streaming data pipelines powering autonomous agents are critical infrastructure. They require the same engineering rigour and governance discipline as any mission-critical system, not the best-effort tolerance most analytics pipelines were built with.

Where batch pipelines break the feedback loop

The architectural mismatch is structural, not a matter of tuning. Batch-processed data introduces delays that sever the feedback loop agents depend on for safe, coherent operation. When an agent’s context is stale by minutes or hours, its decisions reflect a world that no longer exists. In a BI report that is an acceptable trade-off; in an autonomous system taking actions across enterprise systems, it is a correctness problem.

Multi-stage inference pipelines compound this. As agent workflows grow more complex, generation steps begin running serially, turning what should be parallel reasoning into a slow linear queue. Production agentic systems require high throughput (millions of tokens per second at the infrastructure level) and low latency (sub-second time-to-first-token). Batch-oriented pipelines that were never designed for this access pattern cannot be patched into that shape; they need to be replaced at the streaming layer.

Storage is the other failure point. Agentic AI has materially higher requirements for metadata performance, concurrency, immutability, and governance than conventional analytics pipelines. Organisations that have not invested in the right storage foundation encounter the bottleneck precisely when their deployments start showing real promise, which is the worst possible moment to discover it.

What a production-ready agentic data layer looks like

Building a data layer that can actually support agentic workloads in production requires decisions at several levels of the stack, and they need to be made together rather than incrementally.

At the streaming layer, the architecture needs a low-latency event bus with persistence, replay capability, and at-least-once delivery guarantees. Apache Kafka is the established choice; NATS JetStream is a lighter-weight alternative with strong latency characteristics that suits tightly coupled agent coordination. JetStream extends core NATS with streams (message storage and replay), consumers (subscription state and delivery management), file or memory-based persistence, and API-level audit trails. We run NATS JetStream as our own internal event-streaming backbone at DIVERSITY, which gives us direct operational experience with its behaviour under the kinds of bursty, latency-sensitive workloads that agents generate.

At the context retrieval layer, vector databases and fast caching tiers handle the semantic memory that agents query between reasoning steps. Latency here directly determines how quickly an agent can orient itself before acting. This is not a component to provision as an afterthought.

At the orchestration layer, LLM gateways and Model Context Protocol (MCP) are becoming standard patterns for coordinating across multiple models in enterprise deployments. Error handling, retries, and guardrails need to be built into the orchestration layer, not bolted on after the first production incident.

Security requirements are non-negotiable at this tier: encryption in transit (TLS 1.2 or higher), encryption at rest (AES-256), role-based access controls at multiple levels, and audit logging of every autonomous decision. Agents operating continuously across enterprise systems generate an audit surface that conventional access logging was not designed to cover.

Network latency is the invisible bottleneck most teams miss

Infrastructure teams evaluating agentic AI workloads focus, understandably, on compute and storage. Network latency gets less attention until it becomes the binding constraint. In a multi-agent system where agents are querying context stores, invoking external APIs, and coordinating with each other in tight loops, every millisecond of round-trip latency accumulates across the reasoning chain.

This is where network topology matters as much as raw hardware. DIVERSITY operates 44 points of presence across Europe and the Middle East, with direct peering to AWS, Azure, GCP, Oracle, and Cloudflare at all sites. For agentic workloads that span hybrid environments, where agent orchestration runs on sovereign infrastructure but tools and APIs live in hyperscaler environments, that peering architecture removes a latency tier that would otherwise sit between every tool invocation and its response. The operational consequence is not marginal: it is the difference between an agent that can complete a multi-step reasoning chain in acceptable wall-clock time and one that times out waiting for context it should have had in milliseconds.

Gartner projects that 33% of enterprise software applications will incorporate agentic AI by 2028, up from zero in 2024. The infrastructure decisions being made now will determine which organisations are running agents at scale by then and which are still debugging why their pilots never made it to production. The gap is not model capability. It is the data layer underneath.