Is Your Enterprise Data Stack Ready for Agentic AI? 10 Signs to Check

Enterprise leaders are under pressure to deploy AI agents in 2026. Every major platform—Databricks, Snowflake, Google Cloud, AWS—launched agentic capabilities in 2025. Boards are asking: “When do agents go into production?” Executives are setting timelines. But 40% of agentic AI initiatives are projected to be cancelled by 2027 due to rising operational costs, unclear ROI, and insufficient risk controls.

The gap between ambition and reality comes down to one factor: data infrastructure. McKinsey’s 2025 State of AI report shows that whilst 23% of organisations report scaling agentic AI, the vast majority remain stuck in proof-of-concept purgatory. Boston Consulting Group found that 70% of obstacles to agent deployment are people and process issues—yet poor data plumbing remains a major cause of project drag.

Here’s the uncomfortable truth: Traditional data architectures built for analytics and reporting cannot support autonomous agents that must reason across multiple systems in real-time, write back state, and operate under strict governance. Agents need different data patterns: unified access across silos, sub-second latency, real-time freshness, embedded governance, and continuous observability.

This checklist provides a diagnostic framework for enterprise leaders and data architects. The 10 signs below represent critical readiness signals. Use them to assess whether your data stack can support agent deployment—and identify exactly where to invest before agents go into production.

1. Real-Time Data Freshness (<5 Minutes for Operational Agents)

Why it matters:
Agents make continuous decisions based on current business state. A customer service agent handling a delivery enquiry needs order status, inventory levels, and logistics updates simultaneously—all current to the minute. Unlike traditional ML models trained periodically, agents depend on data that reflects reality right now.

The problem:
Most enterprises operate batch-oriented data pipelines—daily ETL jobs, nightly snapshots, weekly data warehouse refreshes. Whilst chatbots can tolerate day-old knowledge bases, production agents reasoning with stale data make wrong decisions. A global e-commerce company’s recommendation agent operated on six-month-old inventory data, continuing to promote out-of-stock items. The cost: over £5 million in lost revenue.

What to audit:

• Can your critical data pipelines refresh within 5 minutes for operational agents?
• Do you have real-time CDC (Change Data Capture) for transactional systems?
• Are you using batch-only ETL, or event-driven pipelines?

Green light: Pipelines refresh multiple times per hour with CDC or streaming architectures.
Red flag: Data warehouse updated nightly or weekly. No streaming pipelines.

2. Data Quality Standards (>95% Completeness, Automated Validation)

Why it matters:
Agents operate without human approval loops. Poor data quality doesn’t trigger warnings—it triggers bad actions that compound quickly. 91% of AI models experience accuracy degradation over time when data quality declines. For autonomous agents, the impact amplifies.

Agents require operational-grade quality: completeness (>95% of critical fields filled), accuracy (validated against source systems), consistency (no conflicting values across systems), and timeliness (aligned with freshness requirements). Human-in-the-loop validation is critical—automated rules catch obvious errors (negative ages, future dates), whilst human review catches semantic issues (outdated status codes, domain-specific definitions).

The problem:
Most enterprises treat data quality as an analytics problem. Agents require operational-grade quality where missing fields force escalation and inconsistent records break workflows. A recruitment agent that cannot distinguish between candidate names risks biased scoring. A financial risk agent operating on outdated account balances exposes the institution to regulatory penalties.

What to audit:

• Do you have automated data quality monitoring with scoring for critical datasets?
• Are quality validation rules automated?
• Is there human-in-the-loop verification for agent-critical data?
• What happens when quality dips below thresholds?

Green light: Automated monitoring with quality scores >95% for critical fields. Alerts trigger when thresholds dip.
Red flag: Quality tracked informally. No automated validation. Issues discovered through complaints.

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3. Unified Data Access Across Sources & Formats (<500ms P95 Latency)

Why it matters:
Production agents routinely orchestrate data from 5–15+ enterprise systems simultaneously—CRM, ERP, APIs, databases, document repositories, data lakes, and legacy on-premise systems. They need a single integration layer where they can fetch data without building custom connectors for each source.

Traditional enterprise architecture separates databases by function: transaction systems, analytics warehouses, search indexes. Agents collapse these boundaries—they need to read from all three, write back state, and maintain consistent security across every data source, all in a single workflow. Enterprise data also lives in multiple formats: structured databases, semi-structured JSON feeds, unstructured content (emails, PDFs, call transcripts, images). Agents need access to all of it.

The problem:
Most enterprises have data silos: CRM islands, ERP islands, separate warehouses. Agents accessing these independently create synchronisation drift, consistency issues, and latency spikes. For a customer service agent to resolve a delivery issue, it may need Salesforce, Zendesk, order management, payment systems, and warehouse APIs. If each query takes 100–200ms, P95 latency balloons to >1 second, making the experience unresponsive.

Different data types often require different tools. Legacy system integration requires custom development. If data is copied into separate caches (vector stores, search indexes), the copies drift from source systems.

What to audit:

• Do you have a unified data access layer (data federation, lakehouse, or integration platform)?
• Can agents query CRM, ERP, APIs, structured and unstructured data through standardised interfaces?
• What is your P95 query latency across critical systems?
• Can your platform handle legacy systems alongside cloud-native sources?

Green light: Single integration platform with >300 pre-built connectors, standardised APIs, sub-500ms P95 latency, support for structured, semi-structured, and unstructured data.
Red flag: Agents build custom connectors per system. Queries >500ms P95. Different data types need different tools.

4. Governance & Access Control (Fine-Grained Permissions, Audit Trails)

Why it matters:
Agents autonomously access sensitive systems—CRM, ERP, financial records, customer data. They must operate under the same governance constraints as human employees, often with stricter safeguards. In regulated industries (banking, healthcare, insurance), a single unauditable agent decision can trigger legal exposure.

Governance includes role-based access control (each agent inherits permissions matching its role), audit trails (every data access logged with full lineage), compliance frameworks (SOC 2, GDPR, HIPAA, CCPA), and policy enforcement embedded in data access layers.

The problem:
Many enterprises treat AI as a bolt-on system with separate security. Agents operate outside the organisation’s governance framework. Security teams have zero visibility into policy violations. The worst-case scenario: An agent accidentally exposes PII or processes data without proper consent, and because the system is disconnected from governance infrastructure, the organisation cannot reconstruct the decision trail to demonstrate compliance.

What to audit:

• Can you enforce role-based access control for each agent?
• Do you have comprehensive audit trails for every agent data access?
• Are policies centrally defined and enforced at the database layer?
• Can you demonstrate compliance to regulators?

Green light: Centralised governance platform with row-level security, audit trails, compliance certifications (SOC 2, GDPR, HIPAA).
Red flag: AI systems operate outside governance infrastructure. Audit trails incomplete. Compliance reporting manual.

As agents move from experimental to operational, the infrastructure requirements shift from “good enough for pilots” to “reliable enough for production.” The next five signs address the advanced capabilities that separate successful deployments from cancelled projects.

5. Data Lineage & Observability (Automated Anomaly Detection, Drift Monitoring)

Why it matters:
When an agent makes a bad decision, where do you look? The answer almost always involves data—but only if you can trace it. Data lineage answers: “Where did this data come from? Has it changed? Is it trustworthy?” Observability provides real-time visibility into data quality, not after-the-fact.

Critical observability includes volume anomalies (sudden spikes/drops signalling broken sources), schema changes (unexpected field modifications), drift monitoring (vector store mismatches with source data), and quality degradation (completeness, accuracy, freshness trending downward).

The problem:
Most enterprises have lineage for analytics (“this metric comes from that table”), not for decision-critical systems. When an agent’s accuracy drops 15% overnight, teams blame the model. Integrated observability often reveals the real culprit: CRM updates delayed 24 hours due to API throttling, or a schema change in the ERP that broke pipelines. Without observability, degradation goes undetected for days whilst agents operate on bad information.

What to audit:

• Do you have automated anomaly detection for data pipelines?
• Can you correlate agent accuracy drops with data quality metrics?
• Do you track data lineage from source to agent to decision?

Green light: Real-time observability platform monitoring volume, schema, quality, and freshness with automated alerts.
Red flag: Observability limited to pipeline logs. Data issues discovered through agent failures or user complaints.

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6. Semantic Understanding & Context (Ontologies, Entity Relationships, Vector Embeddings)

Why it matters:
Agents don’t just need access to data; they need to understand it. This requires semantic interoperability: data represented in ways different platforms and agents can interpret consistently.

Think of semantic understanding as a shared language. Your ERP might call something “order_status,” your CRM calls it “deal_stage,” and your warehouse calls it “fulfillment_state”—but they all refer to the same concept. Without semantic alignment, agents must guess which field to use, or worse, treat them as separate data points.

Semantic layers include ontologies (formal definitions of entities and relationships), taxonomies (hierarchical categorisation), metadata tagging (enrichment with context like source, owner, privacy classification), and vector embeddings (semantic representations enabling similarity search and RAG).

The problem:
Most enterprises have schema (table definitions) but not semantics. A “status” field in the CRM may differ from “status” in the ERP. Customer IDs aren’t reconciled across systems. Vector stores add complexity—if embeddings are generated once and never updated, they drift from source data and RAG becomes unreliable.

What to audit:

• Do you have a formal ontology or metadata management system?
• Are entity relationships mapped (customer-order-product)?
• Do you maintain vector embeddings for RAG? How frequently are they refreshed?

Green light: Semantic layer with centralised metadata, ontologies defining entity relationships, continuously refreshed embeddings.
Red flag: Data understood only by systems that created it. No shared semantic layer. Embeddings never updated.

7. Scalability & Performance (Elastic Compute, High Concurrency, In-Memory Caching)

Why it matters:
In production, many agents run simultaneously—customer service, sales intelligence, operations—all competing for compute and data access. Your infrastructure must scale horizontally and handle high concurrency without degradation.

Key capabilities include elastic compute (cloud-first, auto-scaling), in-memory caching (frequently accessed data cached for sub-millisecond retrieval), connection pooling (efficient reuse of database connections), and query optimisation (indices, materialised views).

The problem:
Many enterprises run infrastructure designed for transactional applications or analytics, not real-time agent systems. A query executing in 100ms during pilot testing may degrade to 5-10 seconds under production load when dozens of concurrent agents compete for resources. Without proper scaling, agent deployments become bottlenecks.

What to audit:

• Is your data platform cloud-first with elastic scaling?
• Do you use in-memory caching for frequently accessed data?
• Can you handle 100+ simultaneous agent queries?

Green light: Cloud-native infrastructure with auto-scaling, in-memory caching, demonstrated high concurrency support.
Red flag: Fixed-capacity deployments. No caching strategy. Performance degrades under load.

8. ML/AI Operations Infrastructure (Feature Stores, MLOps Pipelines, Model Governance)

Note: This applies primarily if your agents include custom ML models (recommendation engines, classification, forecasting). If you’re using only foundation models (GPT, Claude) with RAG, you can consider this section lower priority—though feature stores still add value for managing structured inputs.

Why it matters:
Agents often include machine learning models requiring continuous training, deployment, monitoring, and improvement. MLOps infrastructure ensures models stay accurate and compliant.

Critical components: feature stores (centralised management of input features, ensuring consistency between training and inference), MLOps pipelines (CI/CD for models), model governance (version control, lineage, audit trails), and model monitoring (drift detection when performance degrades).

The problem:
Many organisations treat ML as research projects, not production systems. Models are trained ad-hoc, deployed without governance, monitored informally. In agent systems, this creates risk: a degraded recommendation model affects hundreds of interactions; a mis-trained routing model sends critical work to the wrong team.

What to audit:

• Do you have a feature store for consistent model features?
• Are ML pipelines automated?
• Can you track model lineage and versions?

Green light: Automated MLOps with feature stores, version control, continuous monitoring.
Red flag: Models deployed manually. No feature store. Drift discovered through user complaints.

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9. Hidden Dependency Mapping & SLA Baselines (What Agents Don’t Know They Need)

Why it matters:
This is the most overlooked requirement. Organisations assess agent data needs by asking: “What data does the use case need?” But they miss hidden dependencies—systems agents don’t know they need until they fail.

Example: A customer service agent handles order status queries (depends on ERP). But when a customer disputes an order, the agent needs payment processor records for refund eligibility. When processing returns, it queries warranty systems. When apologising for delays, it checks logistics partner APIs. No one explicitly defines these dependencies until the agent fails on a complex scenario.

Moreover, SLA baselines are rarely established. What does “fresh enough” mean? If your supply chain agent operates on 2-hour-old inventory and misses stock-outs, that’s a problem. But setting 15-minute freshness SLAs without assessing cost creates over-engineered solutions.

The problem:
Most assessments focus on happy-path use cases. But agents must handle exceptions, escalations, and multi-step workflows. Each introduces new data dependencies. Missing dependencies cause agents to fail in production, escalate unnecessarily, or make incomplete decisions.

What to audit:

• Have you mapped the full decision tree, including exceptions and edge cases?
• Are hidden dependencies documented (systems needed for escalations)?
• Do SLAs exist for freshness, latency, and availability for each dependency?

Green light: Complete data dependency mapping including exception handling. SLAs explicitly defined and baselined.
Red flag: Requirements defined only for happy-path scenarios. Hidden dependencies discovered during pilot testing.

Prioritisation Framework: Where to Start

These 9 signs form a comprehensive readiness audit for agent deployment. If your organisation can honestly check most of these boxes, you’re positioned to move forward with confidence. If gaps exist—and for most enterprises, they will—the checklist reveals exactly where to invest.

The strategic imperative is clear: Data infrastructure is not an implementation detail; it is the architectural foundation upon which reliable, scalable agentic AI is built.

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