Agentic AI and the inevitable evolution of enterprise data platforms

Agentic AI and the inevitable evolution of enterprise data platforms

The enterprise data stack is changing. Not because a new tool arrived, but because the way value is created has shifted, from dashboards reporting history to systems that act in the present. Generative and agentic AI push data from a reporting asset into a live ingredient. The winners will be firms that redesign their stack around products, context and control, not just storage and pipelines.

An AI system is only as good as the context it can reach and the guardrails that keep it useful. That demands a data platform that can supply fresh, well described facts, and an orchestration layer that can reason, take small safe actions, and learn from outcomes. PE and VC operators describe this as a move from passive analytics to decision engines. Practitioners inside large firms describe it as the shift from build reports to deliver outcomes. Recent industry analysis argues that agents will handle non deterministic, multi-step work and that success will depend on upgrading enterprise architecture, especially the control plane that governs agents, tools and access.

So, what has changed?

• Text and code are now first-class interfaces. Product teams can ask for answers in natural language and can tie those answers to actions
• Retrieval and tool use let models fetch context on demand, which raises the bar for metadata quality, security and data freshness
• Regulators and risk teams expect explainability, audit trails and graceful failure
• Academic work on retrieval augmented generation (RAG) and agent design patterns shows that context routing, memory limits and evaluation harnesses matter as much as the base model. The stack now needs to serve people and machines at the same time, with evidence.

6-layer target reference model

1. Experience: Human and machine touchpoints, for example copilots in internal tools and lightweight agents for routine tasks. The focus is clarity, consent, and a good 'I changed my mind' path.
2. Orchestration and agents: A thin control layer that handles planning, tool use, memory and limits. It decides when to retrieve, when to call a system of record, and when to ask a human. Rate limiters, circuit breakers and an agent registry live here. Treat this as a control plane, with policy, observability and safe rollout.
3. Knowledge and context: Domain glossaries, semantic search, retrieval indexes, feature stores and rules. This is where business meaning is made explicit so answers are consistent and actions are safe. Invest in evaluation data and test benches so retrieval quality and reasoning steps are measurable.
4. Data platform: Ingest, quality, modelling and storage. Treat raw, refined and serving zones as products, with owners and SLAs. Automate lineage and quality signals so they are visible by default. Connect retrieval to serving layers rather than to raw sources.
5. Governance and safety: Policies for privacy, access, explainability and retention. Logs for who asked what, what the system used, what it returned, and what happened next. Redress flows are designed in. Add prompt and tool versioning, agent decision logs, and proportionate approvals for higher risk actions.
6. Runtime and infrastructure: Compute, networks, secrets and cost controls. Aim for predictable performance and a clear cost to serve per use case. Track cost per action and cost per successful outcome, not only spend by service.

This model is vendor agnostic. It reflects investor tests for readiness, for example the shortest path from data to a safe business action, and it mirrors what experienced data leaders repeat, value arrives when products own their context and when the platform removes toil.

Interim phases on the road to target

No one gets there in one leap. Here’s a practical path in a series of contained upgrades.

• Phase 1: Make meaning visible. Publish domain glossaries and start a single source of business definitions. Add basic retrieval over approved documents and data. Ship one copilot for a narrow, high demand workflow. Prove an end-to-end slice that includes audit, human override and a decision log.
• Phase 2: Stabilise the platform. Clean your serving layer. Introduce freshness and quality signals that are visible to product teams. Add simple cost controls and a weekly triage for data incidents. Connect retrieval to the serving layer and add basic evaluation harnesses.
• Phase 3: Scale with control. Stand up the orchestration layer with memory limits, a tool registry and policy enforcement. Introduce red team testing for prompts, retrieval and actions. Expand to two or three business areas and start portfolio steering by outcome and cost to serve. Treat agent rollout like feature rollout with canaries and safe rollback.

Risks and trade offs

Agentic patterns can amplify both value and error. If context is poor, answers will be confidently wrong. If tool use is unbounded, systems may become costly or unstable. If governance is heavy, teams will route around it. Industry analysts warn of vendor hype and agent-washing and predict high scrap rates where value is unclear. The remedy is proportionate control. Keep pilots small, keep evidence visible, and keep a human path open where stakes are high.

The bottom line

Generative and agentic AI do not replace the data platform. They demand more from it. The firms that will win will treat data, context and orchestration as products, will measure cost to serve, and will design for explainability from day one. That path is ambitious but achievable if decisions don't chase hype. It turns today’s stack from a place where data rests, into a system that helps people decide and act with confidence.