Trusted Data for AI Agents: Enterprise Foundation for Governance, Quality and Scale

Enterprise interest in AI agents is accelerating, driven by the promise of autonomous decision-making, continuous task execution and scalable intelligence embedded directly into business workflows. Yet early results are uneven. Industry analysis suggests that nearly 7 in 10 enterprise AI initiatives struggle to move beyond the pilot stage, most often due to data reliability challenges rather than model limitations. This gap between ambition and production reality is becoming more pronounced as organizations move from experimental chatbots to autonomous, multi-agent systems operating across critical business domains.

AI agents place fundamentally different demands on enterprise data. Unlike traditional analytics or dashboards, agents must reason, act and adapt in near real time. When data is incomplete, inconsistent, poorly governed, or lacking context, agents produce unreliable outcomes like hallucinations, contradictory responses, stalled workflows and decisions that cannot be explained or audited. In regulated enterprises, these failures translate directly into operational risk and compliance exposure.

The root cause is not the maturity of large language models or agent frameworks. Most organizations invest heavily in LLM selection, orchestration layers and tooling, while treating data readiness as an afterthought. Instead, data integration and data quality are prerequisites for AI agent success, not post-deployment fixes. Traditional data architectures, designed for reporting, batch analytics, or narrow AI use cases, are not built to support autonomous agents that depend on continuous access to trusted, contextual and governed data.

Trusted data for AI agents requires a different foundation. It demands four core capabilities delivered as a unified platform rather than fragmented point solutions: unified data integration across all sources and latencies, proactive data quality management before agents consume data, comprehensive governance with security and auditability built in and master data management to ensure consistent entity understanding across agent interactions.

This article examines why conventional data approaches fail for AI agents, outlines the four-pillar framework for building a resilient data foundation, explores enterprise architecture patterns for scaling AI agents and provides practical guidance on implementation and success metrics for CIOs and data and AI leaders.

Why AI Agents Need a Different Data Approach

Enterprise AI agents represent a shift from analytical systems that inform decisions to autonomous systems that make and execute decisions. This shift fundamentally changes what “good data” means and exposes the limitations of traditional data infrastructure built for reporting, dashboards and traditional analytics. Cloud data warehouses and cloud data lakes remain valuable, but on their own they are not sufficient to support AI agents operating in production at enterprise scale.

The Autonomous Agent Data Challenge

AI agents differ from traditional batch ML models or BI dashboards, in both data consumption and data dependency. Instead of operating on pre-curated datasets or static feature stores, agents reason continuously, call tools dynamically, maintain memory across interactions and adapt their actions based on context. This requires real-time, multi-source data access, often within a single task execution.

A single agent interaction may require simultaneous access to transactional systems, operational platforms, unstructured content and historical context. 

At scale, one agent can query ten or more systems—CRM, ERP, inventory, ticketing platforms, policy repositories and knowledge bases—within seconds. They cannot rely on overnight data refreshes, delayed pipelines, or manually prepared datasets.

Consistency becomes critical. An agent must receive the same answer about a customer, product, or supplier regardless of which system it queries. Without consistent entity resolution and up-to-date data, agents produce conflicting responses that erode trust and reduce adoption.

For example, a customer service AI agent responding to a delivery inquiry must correlate customer history from CRM, order status from ERP, inventory availability, support ticket history and policy exceptions, in real time, for the most accurate answer. If any source is stale, incomplete, or inconsistent, the agent may hallucinate, offer incorrect commitments, or provide answers that contradict other channels. This results in damaged customer trust, operational reworks and decisions that fall short of human-level effectiveness.

Where Traditional Data Architecture Falls Short

Traditional data architectures were built to support reporting, analytics and human-led decision-making, not autonomous AI agents operating in real time. As a result, many enterprise approaches fall short when applied to agent-driven workflows, even when they address part of the problem well.

Catalog-centric governance is necessary, but incomplete

Point solution data catalogs emphasize metadata management, stewardship, lineage, and policy definition — all foundational elements of governance. These catalog-driven capabilities are essential for establishing enterprise trust and compliance. However, a catalog’s role is descriptive: it tells you about the data, but it does not deliver the data itself. AI agents, by contrast, cannot reason over metadata alone; they require direct, timely access to curated data streams. A catalog can guide an agent to where approved data resides, but it does not integrate data across systems, normalize conflicting representations, enforce quality at runtime, or assemble task-specific context. 

Governance without integration and quality enforcement leaves a critical execution gap for AI agents.

Monitoring-only quality approaches address symptoms, not causes

Point solution data quality and observability solutions focus on detecting hallucinations, drift, or poor outputs after agents are deployed. While valuable for evaluation, this approach is reactive by design. By the time issues are detected, agents have already acted on flawed data, potentially impacting customers, operations, or compliance. In multi-agent workflows, these errors compound rapidly, snowballing into far more serious outcomes. For enterprises, trusted data for AI agents- contextual, curated and governed data across structured and unstructured sources - requires proactive quality management before data enters RAG pipelines or agent workflows, not just post-deployment monitoring.

Point solution integration patterns do not scale for agents

Fragmented point solution tools are too slow for real-time agent interactions and point-to-point integrations quickly become unmanageable as agents multiply. Each new agent or tool creates additional integration paths, increasing fragility and total cost of ownership (TCO). Without a unified integration layer, the same data is accessed through different data pipelines with different rules, leading to inconsistent outcomes and creating compliance vulnerabilities.

Governance and consistency break down across fragmented systems.

When integration, quality, governance and monitoring are handled by separate point solution tools, policies cannot be enforced uniformly. Audit trails fragment, lineage becomes incomplete and master data conflicts persist. Agents querying multiple systems receive different answers about the same customer, product, or supplier, undermining trust and explainability.

What’s missing is a unified and trusted data foundation for AI agents. Without this foundation, scaling AI agents remains brittle, expensive and risky, regardless of how advanced the agent framework or model may be. This shift does not require replacing existing data warehouses, lakes, but consolidating legacy and point solution tools to enable real-time access, quality enforcement and governance for AI agents.

A unified data integration layer provides:

• Real-time data access and CDC-enabled data integration across all sources 

• RAG ingestion to enable seamless ingestion of data into LLMs to enhance AI agent knowledge, ensuring more relevant and contextual agents

• Consistent, proactive data quality enforcement before it reaches agents 

• Centralized governance that travels with the data

• Master data management for entity resolution and consistency

The Four Pillars of Trusted Data for AI Agents

Building trusted data for AI agents in the enterprise requires more than incremental upgrades to existing data stacks. Autonomous agents surface failure modes such as hallucinations, inconsistent decisions, or stalled workflows at scale. The root cause is almost always foundational: fragmented integration, reactive quality controls, inconsistent governance and unresolved master data.

A durable solution requires four foundational capabilities working together. Each pillar addresses a distinct but interdependent failure point. Implemented as a unified foundation rather than stitched point solutions, these pillars enable AI agent governance, AI agent data quality and scaling AI agents with confidence across hybrid enterprise environments.

Pillar 1: Unified Data Integration: Breaking Down Silos

The challenge

AI agents routinely need to access dozens, or even hundreds of enterprise data sources in a single task. Traditional point-to-point integrations quickly become brittle and unmanageable, while batch ETL pipelines cannot meet real-time agent requirements. Hybrid environments add further complexity, with critical data spread across SaaS applications, cloud platforms and on-premises systems.

The solution

An enterprise AI data integration platform. A modern AI agent data architecture starts with unified data ingestion and transformation across any source, pattern and latency. 

• Unified connectivity across SaaS, cloud and on-premises systems

• Multiple access patterns including REST APIs for real-time agent queries, streaming for event-driven workflows and batch for training and analytics.

• Intelligent routing with optimization of data access paths based on latency, cost and freshness requirements.

• Change Data Capture (CDC) ensures agents operate on current state rather than stale snapshots. 

• Native support for RAG ingestion for document processing, chunking and embedding generation enabling agents to reason over semi-structured and unstructured content as well as handle Open Table Formats (OTFs)

• Seamless handling of semi-structured and unstructured data (documents, text, JSON, videos, XML, images), not just structured databases.

Real-world example

A manufacturing supply-chain agent. The agent coordinates production schedules (ERP), equipment status (IoT), supplier availability and logistics updates through a unified API—without bespoke integrations—allowing decisions to be made in seconds, not hours.

The Informatica Advantage

Informatica IDMC delivers a unified integration layer to replace fragile custom code with a resilient trusted data layer for AI agents.

• Cloud-native data integration architecture scales as agent volumes grow and adapt automatically as source schemas evolve.

• Hybrid and multi-cloud support (AWS, Azure, GCP, on-premises) enables contextual access without vendor lock-in, unlike platform-specific solutions.

• Support for Open Table Formats (Apache Iceberg tables, Delta Lake, Apache Hudi) for flexible data lake architectures

• Informatica Serverless capabilities to handle the scale needed for AI and agentic deployments

Pillar 2: Proactive Data Quality: Preventing Hallucinations at the Source

The challenge

AI agents amplify data quality issues at scale. Incomplete, inconsistent, or outdated data is a primary driver of hallucinations and unreliable outcomes. Monitoring-only approaches identify problems after deployment, when agents have already acted on bad data. In multi-agent systems, these issues compound rapidly.

The solution

A quality-first architecture with:

• Proactive quality rules: Validate, standardize and enrich data before agents access it

• Real-time quality scoring: Every data element is tagged with quality metrics that agents can evaluate

• Automated remediation: Automated remediation, guided by intelligent recommendations, resolves common quality issues upstream 

• Quality lineage: Track data quality from source through transformation to agent consumption

Critical quality dimensions for agents

• Completeness: Missing contact data prevents task completion. For example., missing customer email means an agent can't send a confirmation.

• Consistency: Prices, statuses and identifiers must align across systems. For example,  product price must match across CRM, ERP, e-commerce systems

• Timeliness: Stale inventory or account data leads to lost business. For example. taking orders for unavailable products may result in order cancellations.

• Accuracy: Incorrect attributes cause operational failures. For example.,  a wrong customer address causes delivery failures.

• Conformity: Standard formats ( such as phone numbers and addresses) ensure agents can reliably parse and reason.
  

Real-world example

A healthcare AI agent for patient scheduling. A healthcare scheduling agent quality checks and validates patient records, including demographics, insurance and medical history in real time, preventing downstream safety risks and costly rescheduling.

The Informatica Advantage

• Applies quality rules proactively at the integration layer, reducing failures before agents act. (Unlike post-deployment monitoring Galileo approach)

• ML-powered data quality recommendations learn from patterns and integrates with CLAIRE AI for intelligent quality automation

• Defining consistent quality SLAs for agent-consumed data directly improves reliability and trust as agents scale.

Pillar 3: Comprehensive Governance: Security, Compliance and Audit Trails

The challenge
Autonomous agents access sensitive data continuously, opening up privacy and security risks as agents might inadvertently expose confidential information in responses. Regulatory requirements (GDPR, HIPAA, SOC 2) demand strict access controls, masking, lineage and auditability. In multi-agent systems, agent-specific accountability quickly becomes complicated without centralized governance.

The solution

Governance by design. Effective AI governance for AI agents enforces policies at the data layer. 

• Policy-based access control:  Determine what each agent can access based on role, context and sensitivity.

• Data masking and tokenization: Automatically protect PII, PHI, financial data before agents access.

• Complete audit logging: captures who accessed what data, when and for what purpose.

• Consent management: Honor customer data preferences across all agent interactions

• Data classification: Automatic tagging of sensitive data elements

Key Enterprise Governance Requirements 

• Role-based access for agents: Just like human users, agents get permissions based on roles. 

• Contextual access controls: Differentiate data access based on function. For example., sales agent gets different customer data than support agent

•  End-to-end lineage enables explainability: When an agent makes a decision, you can trace back to governed data sources and access permissions

• Compliance reporting: Demonstrate regulatory compliance across agent ecosystem

Real-world example

A Financial services AI agent for loan processing. A loan-processing agent automatically accesses only authorized applicant data, masks sensitive identifiers in logs, maintains a complete audit trail for regulators and enforces data retention policies.

Informatica IDMC Advantage

Accelerate deployment with confidence and turn governance into a competitive advantage rather than a constraint. 

• Governance implemented at the integration layer mitigates risks in AI deployment at scale, without slowing agents down.

• Consistent policy enforcement, whether agents access the data via API, database query or file system, ensures a single point of control

• Integration with enterprise IAM/SSO systems

• Pre-built compliance frameworks (GDPR, CCPA, HIPAA)

Pillar 4: Master Data Management: Ensuring Consistent Entity Understanding

The challenge

Customers, products, suppliers and locations exist in multiple systems with different or conflicting identifiers and attributes. Agents querying different systems receive different answers about the same entity. In multi-agent environments, this inconsistency breaks collaboration and trust.

The solution

Golden records for agents. Real-time propagation ensures changes are immediately reflected wherever agents access data.

• Entity resolution: Master Data Management (MDM) resolves and synchronizes entities across systems, creating a single source of truth 

• Relationship mapping: Understand how entities relate with relationship (customer → accounts → orders → products) and hierarchy (corporate hierarchies, product taxonomies, organizational structures) context

• Real-time synchronization: Master data updates propagate to all agent access points

Why MDM matters for AI agents

MDM is critical for multi-agent systems where agents share entity references, but is notably absent from most data management architectures. When MDM is baked into the data foundation, it delivers:

• Consistency: Every agent gets the same view of customer/product regardless of source system

• Completeness: Master record aggregates all information about entity from all systems

• Relationships: Agents understand "this customer owns these accounts at these locations"

• Change management: When a customer merges with another, all agents immediately use the correct entity

Real-world example

A retail engagement agent. A customer shops online (e-commerce system), calls support (CRM), visits the store (POS) and uses the mobile app (separate database). MDM ensures the agent has a complete view across all channels to provide personalized, contextually relevant service.

Examples of additional MDM-driven agent use cases

• Customer MDM: Gives customer service agents a complete customer 360° view

• Product MDM: Guarantee that a product recommendation agent uses accurate, consistent product data

• Supplier MDM: Enable a procurement agent to evaluate suppliers with a complete profile

• Location MDM: Give the logistics agent accurate facility/address information

The Informatica IDMC Advantage

• Built-in MDM: the capability is integrated with IDMC, not a separate tool that has to be integrated

• Intelligent, AI-powered matching and merging with CLAIRE AI

• Need-based support for multiple MDM styles (registry, consolidation, coexistence) 

• Industry-specific solutions: pre-built data models for healthcare, financial services, manufacturing and many other industries.

Enterprise Architecture Patterns for Scaled AI Agent Deployment

Moving from experimental pilots to enterprise-scale AI agents requires more than strong models and agent frameworks. CIOs and data architects need a reference architecture that operationalizes trusted data for AI agents. One that separates data responsibilities from agent logic, scales predictably and works across hybrid and multi-cloud environments. This section bridges strategy and execution by outlining practical architecture patterns and phased deployment approaches used by enterprises scaling AI agents responsibly.

Reference Architecture: The Trusted Data Layer

At scale, AI agents should not connect directly to dozens of operational systems. Instead, enterprises need a trusted data layer that acts as a control plane between agents and underlying data sources. This layer centralizes data integration, quality, governance and master data management while exposing consistent, governed access to agents through standard interfaces.

Core Architecture Components

A unified platform such as Informatica IDMC helps you build a data foundation once and serves many agents over time and at any scale.

Data Integration Layer

The integration layer connects to all enterprise cloud and on-premises data sources and provides unified access for agents. It supports real-time APIs for agent queries, streaming for event-driven agents and batch pipelines for training and analytics. 

Beyond connectivity, this layer handles security, transformations, CDC-based synchronization, unstructured and semi-structured data processing and RAG ingestion workflows required for contextual agent reasoning.

Data Quality Layer

Quality rules validate, standardize and enrich data before agents consume it. Quality scores and metadata are attached to data elements, automated remediation workflows address recurring issues and monitoring alerts surface anomalies early, shifting quality management upstream rather than reacting after agent failures.

Governance Layer

Policy engines enforce access controls, masking, encryption and consent consistently across all access paths. Classification, tagging, lineage and audit logging ensure explainability and compliance for every agent interaction.

Master Data Layer

Golden records resolve customers, products, suppliers and locations across systems. Entity matching, relationship modeling and hierarchy management ensure agents operate with a consistent, enterprise-wide understanding of core entities.

Agent Access Layer

Standardized interfaces such as REST APIs, streaming endpoints, vector database integrations for RAG and flexible query interfaces decouple agents from data complexity and support multiple agent frameworks.

Key architectural principles

This architecture allows enterprises to scale AI agents without locking into a single stack, while building on existing data investments rather than replacing them.

• Separation of concerns: Data operations are independent of agent logic.

• Reusability: Build once, serve many agents and use cases.

• Scalability: Cloud-native services scale with agent adoption.

• Observability: Monitor data operations separately from agent performance.

• Flexibility: Platform-agnostic support for any LLM or agent framework (LangChain, AutoGen, CrewAI, proprietary).

AI Agent Deployment Patterns: Pilot to Production

Scaling AI agents safely requires a phased approach that balances speed with risk management.

Phase 1: Pilot (30–60 days)

Agent scope: One use case, typically read-only, with 3–5 data sources.

Data approach:

• Implement core integration for pilot data sources

• Basic quality rules for critical fields

• Essential governance (PII masking, access logging)

• MDM is optional if data comes from a single system

Success metrics:

• Agent accuracy

• Data access latency

• Baseline quality scores

Informatica execution advantage: Rapid deployment with over 300 SaaS pre-built connectors accelerates validation.

Phase 2: Production Expansion (90–180 days)

Agent scope: Three to five use cases spanning 10–20 data sources.

Data approach:

• Expand integration to cover all data sources for production agents

• Comprehensive quality framework with automated remediation

• Full governance with compliance reporting

• MDM for shared entities across agents (customers, products)

Success metrics:

• Multi-agent reliability

• Cross-system consistency

• Governance compliance

Organizational considerations: Defined stewardship roles, agent operations teams and quality SLAs for agent-consumed data.

Phase 3: Enterprise Scale (12+ months)

Agent scope: Enterprise-wide deployment across 50+ sources.

Data approach:

• End-to-end hybrid deployment using Informatica IDMC

• CLAIRE AI-powered automation for quality and integration

• Advanced MDM with complex hierarchies and relationships

• Cross-functional data governance at scale
  

Success metrics:

• Agent ecosystem performance

• Total cost of ownership

• Measurable business impact

Maturity indicators:

• Reusable data services

• Self-service agent development

• Continuous optimization driven by data insights

AI Agent Deployment: Risk mitigation strategies

By combining a unified architecture with phased execution, organizations can de-risk adoption while building a scalable, governed foundation for AI agents, turning experimentation into sustainable enterprise capability. Some approaches to prove value before scaling are:

• Start with read-only agents: Lower risk than agents that write data

• Sandbox environments: Test data quality impact before production

• Gradual rollout: Percentage of traffic to agents vs. human workflows

• Fallback mechanisms: Human escalation when data confidence is low

  Deployment Patterns to Scale AI Agents: A Comparison Table

Implementation Strategy: Building Your Trusted Data Foundation

While architecture defines how AI agents operate at scale, successful deployment depends on how organizations assess readiness and sequence foundational capabilities.

Moving from strategy to execution requires a structured, honest approach. Enterprises that succeed with trusted data for AI agents treat implementation as a phased capability build, not a tooling exercise. The goal is to align integration, quality, governance and master data in a way that supports near-term pilots while laying the groundwork for scale.

Assessment: Current State and Readiness

A realistic assessment is the most important step and yet the most frequently skipped. Many organizations overestimate readiness because data debt accumulates gradually and remains invisible until agents fail in production.

Data integration maturity: key questions

• How many data sources do we have today? (Large enterprises often exceed 400)

• What integration approaches are in use? (ETL, APIs, file transfers, manual processes)

• Can data be accessed in real time, or only through batch refreshes?

• Do we operate across hybrid cloud and on-premises environments?

• What is the typical integration development cycle—weeks or months?

• Can current infrastructure scale to increasing agent demand and data volumes?

• How are schema changes and format variations handled?

• What proportion of data is unstructured or semi-structured?
  

Data quality baseline: key questions

• Is data quality measured systematically or ad hoc?

• What issues are most common (duplicates, missing values, inconsistencies)?

• How are quality problems addressed today—manual fixes, periodic cleanup, or ignored?

• Do quality SLAs exist for critical data elements consumed by agents?
  

Governance capabilities: key questions

• How is data access controlled today—database permissions, application logic, or centralized policies?

• Is data classified by sensitivity (public, confidential, regulated)?

• Can we audit who accessed what data and when?

• How are PII/PHI obligations handled—manually or automatically?

• Are governance policies consistent or fragmented across systems?
  

Master data status: key questions

• Is MDM in place for any domains (customer, product, supplier)?

• How severe is the impact of duplicate or inconsistent entity data?

• How many systems maintain their own version of master data?

• Are entity reconciliations manual or automated?

  Gap analysis framework

  Create a simple matrix mapping required capability vs. current state vs. gap severity:

• Critical gaps: Block agent deployment

• Important gaps: Limit agent effectiveness

• Nice-to-have gaps: Improve performance over time
  

Readiness signal

An honest assessment prevents false starts and costly rework later.

• Green light: Modern cloud integration, some quality and governance:  start pilot immediately

• Yellow light: Mixed legacy and modern tooling:  60–90 days of foundation work first

• Red light: Legacy ETL only, no quality or governance: 6–12 months of modernization required

Roadmap: Prioritization and Sequencing

Skipping the foundational work creates technical debt that compounds as agents scale; sequencing it correctly turns experimentation into durable enterprise capability.

Quick wins: first 90 days

Deliverable: Working agent in a production-like environment demonstrating value

• Agent use case selection: High value, moderate complexity, clear metrics

  • Good: FAQ-based customer service agent

  • Better: Sales lead-qualification agent

  • Best: Operations scheduling agent

• Data scope: Start with 3–5 critical sources

• Core integration: Deploy Informatica IDMC with essential connectors

• Basic quality: Focus on completeness and timeliness

• Minimal governance: PII masking, access logging, baseline compliance
  

Foundation building: 90–180 days

Deliverable: Production-ready data foundation supporting multiple agents

• Expand integration for additional agent use cases

• Implement comprehensive quality rules with automated remediation

• Mature governance with policy enforcement and audit trails

• Introduce MDM for one shared domain (customer or product)

• Enable intelligent automation with CLAIRE AI
  

Enterprise scaling: 12+ months

Deliverable: Self-service foundation enabling enterprise-wide agent innovation

• Complete hybrid, multi-cloud integration coverage

• Advanced quality with predictive detection and ML-driven remediation

• Federated governance and stewardship operating at scale

• Multi-domain MDM (customer, product, supplier, location, employee)

• Reusable data services and an agent center of excellence

Critical success factors

• Executive sponsorship from CIO/CDO

• Cross-functional teams spanning data, security, governance and agent engineering

• Metrics-driven execution tracking quality, reliability and business impact

• Change management through training and documentation
• Strong vendor partnership to accelerate delivery

Measuring Success: KPIs for Trusted Data Foundations

For CIOs and data leaders, the question is not whether AI agents are technically impressive, but whether investments in trusted data for AI agents are delivering measurable, repeatable value. Clear KPIs provide visibility into what is working, where risk remains and how quickly the organization can scale agents with confidence. The most effective measurement frameworks track both data foundation health and business impact, ensuring technical progress translates into enterprise outcomes.

Data Foundation Metrics

Integration performance

These metrics indicate whether the underlying AI agent data architecture can support real-time, autonomous decision-making:

• Data latency: Time from source update to agent availability (target: <5 seconds for critical data)

• Integration reliability: Uptime for agent data access services (target: 99.9%)

• Coverage: Percentage of required data sources connected, tracked toward 100%

• Development velocity: Time to onboard a new data source (target: <1 week using pre-built connectors)

Data quality metrics

Because agent reliability is directly tied to data quality, these indicators should be monitored continuously:

• Quality score: Composite score across completeness, accuracy, consistency and timeliness

• Error rate: Percentage of agent-consumed data failing quality rules (target: <1%)

• Remediation time: Average time to resolve quality issues, segmented by automated vs. manual fixes

• Quality SLA compliance: Adherence to defined quality commitments for agent data

Governance effectiveness

Strong AI agent governance is measurable and auditable:

• Policy compliance: Percentage of agent data access aligned with defined policies (target: 100%)

• Audit coverage: Percentage of agent data access events logged (target: 100%)

• Security incidents: Unauthorized access or data exposure by agents (target: 0)

• Compliance reporting: Time required to generate regulatory reports (target: automated, on-demand)

Master data management impact

MDM metrics quantify improvements in consistency and trust:

• Entity match rate: Percentage of entities successfully resolved across systems

• Duplicate reduction: Decrease in duplicate customer or product records

• Data consistency: Identical results returned for the same entity across agent access points

Together, these metrics form a dashboard view of the data foundation for AI agents, reinforcing the principle that what gets measured gets managed.

Business Impact Metrics

Agent performance improvements

These metrics show whether trusted data is improving agent outcomes:

• Accuracy: Reduction in hallucinations or incorrect responses after foundation implementation

• Response quality: User satisfaction scores for agent interactions

• Task completion: Percentage of tasks completed without human intervention

• Consistency: Variance in agent responses to similar queries (lower variance indicates higher trust)

Operational efficiency

As the foundation matures, efficiency gains should compound:

• Time savings: Reduced effort to develop and maintain agents

• Cost reduction: Lower integration costs through reusable data services

• Scale economics: Decreasing cost per agent deployed

• Infrastructure efficiency: Optimized data processing and integration spend

Business outcomes

Ultimately, trusted data enables outcomes that matter to the enterprise:

• Revenue impact: New capabilities such as always-on customer service or faster sales cycles

• Cost avoidance: Issues prevented through proactive data quality rather than post-failure remediation

• Risk mitigation: Compliance violations and security incidents avoided

• Innovation velocity: Time from agent concept to production deployment

ROI framework

A complete ROI view compares investment in platform costs, implementation and operations against efficiency gains, risk reduction and revenue opportunities. Enterprises typically see positive ROI within 12–18 months, with long-term value compounding as the same foundation supports every new agent. 

From a CFO perspective, this reinforces a critical insight: a unified platform lowers total cost of ownership compared to accumulating point solutions, while enabling sustainable, scalable AI agent innovation.

Building the Foundation for AI Agent Success

Enterprise AI agents will only deliver on their promise of autonomous, intelligent execution if they are built on trusted data for AI agents. The real race is not to deploy the most agents, fastest, but to deploy agents that enterprises can trust with customer interactions, operational decisions and regulated processes. Reliability, explainability and scale are determined long before an agent is deployed, in the data foundation beneath it.

For CIOs and data leaders, this creates a clear strategic imperative. A data-first approach, addressing integration, data quality, governance and master data together, must precede large-scale agent rollout. Platform thinking matters: a unified foundation reduces complexity, lowers total cost of ownership and avoids the compounding risk introduced by fragmented point solutions. Most importantly, proactive data architecture shifts risk management upstream, preventing hallucinations, compliance gaps and security exposure rather than reacting to them after agents fail in production.

The four pillars outlined in this article—unified integration, proactive quality management, comprehensive governance and master data management—provide a practical framework for building this foundation. They are not abstract concepts, but operational capabilities that can be assessed, implemented and scaled systematically.

For CIOs and data architects, the next steps to implementation are incremental and pragmatic. Start with an honest assessment of current readiness. Identify high-value agent use cases where trusted data will make a measurable difference. Prove value through a focused pilot, then expand deliberately as maturity increases. 

Organizations that invest in the foundation today will deploy AI agents faster, more reliably and at lower risk as agentic AI moves into the enterprise mainstream.

Ready to build a trusted data foundation for your AI agents? Explore how Informatica's IDMC platform provides the integration, quality, governance and master data capabilities enterprise AI agents need to succeed.

Frequently Asked Questions About Trusted Data for AI Agents