AI Agent Engineering: Why Enterprise Data Infrastructure Determines Agent Success

AI agent frameworks are multiplying fast, each promising autonomous execution, task delegation, and multi-step reasoning. Yet enterprise adoption tells a different story. More than 40% of agentic AI initiatives are projected to be canceled by 2027 due to rising operational costs, unclear ROI, and insufficient risk controls. At the same time, 72% of organizations expect to increase LLM spending in 2025—even as nearly 44% list data privacy and security as their biggest barrier to scaling AI. The result is a widening gap between executive ambition and what teams can reliably deploy.

If you lead data engineering, AI, or architecture, you’ve likely seen the pattern. Most technical conversations focus on agent frameworks, toolkits, prompts, and orchestration workflows. Yet more than 80% of AI projects still fail—twice the rate of traditional technology programs. 

The differentiator between proof-of-concept agents and production-grade autonomous systems isn't the frameworks. It’s whether agents can consistently access high-quality, governed, real-time data. With 91% of AI models experiencing quality degradation over time due to stale, incomplete or fragmented data, the make-or-break factor is the data infrastructure behind the agent.

Successful agentic AI engineering requires treating data infrastructure as a first-class architectural component, not an afterthought. Agents need unified access to trusted data from diverse systems—CRM, ERP, APIs and databases—with governance, quality checks, and observability built in from day one.

This practical guide puts the lens on AI agent engineering from the data infrastructure layer. This layer determines whether agents operate as reliable enterprise-grade systems or remain limited experiments. You’ll find guidance on the data bottlenecks that restrict agent autonomy, architectural patterns for multi-agent environments, implementation steps for agents production readiness, and methods how to measure agent performance through data quality metrics.

What Makes AI Agent Engineering Different from Traditional AI Agent Engineering?

Building enterprise AI agents is not an incremental evolution of chatbot or ML model development. It is an architectural shift, specifically from the data engineering perspective. This shift is embodied in agentic data engineering—an innovative approach where AI agents autonomously reason, learn, and act across the entire data lifecycle, transforming traditional pipelines into intelligent, proactive systems that require minimal human intervention.

Traditional AI projects focus on training a model, enriching a single knowledge source, or deploying an application that interacts with users in a controlled way.

Agents, on the other hand, are autonomous systems designed to act, retrieve information from multiple environments, make decisions, and trigger downstream workflows. Agentic systems differ from traditional automation by leveraging AI agents with comprehensive system context to autonomously orchestrate, adapt, and optimize data pipelines, resulting in more resilient and efficient data engineering processes.

This distinction matters because conventional enterprise data architectures are optimized for analytics or reporting. Agents need an infrastructure optimized for continuous decision-making, real-time context gathering, and strict governance controls.

Autonomous Decision-Making Requires Real-Time, Multi-Source Data

Unlike traditional ML models that are trained once and periodically refreshed, AI agents make continuous decisions, in near real-time.  Those decisions depend on data that reflects the state of the business right now. This is where most agent implementations fail.

While chatbots and basic RAG systems rely on one curated knowledge base, production-grade agents routinely orchestrate data from 5, 10, or even 15+ enterprise systems at once. Agentic AI autonomously manages complex data workflows across these systems, planning, executing, and optimizing data ingestion, transformation, and delivery to ensure seamless operations.

They must blend operational data—CRM updates, support tickets, order management queues—with analytical and historical context, such as dashboards, reports, forecasts, and performance trends.

This combination directly drives agent “decision velocity,” the ability to act in seconds or minutes instead of hours or days.

Consider a customer service agent resolving a delivery issue. In under a second, it may need to pull order status from the ERP, past interaction history from the CRM, product details from a documentation portal, and current stock availability from the warehouse system. If even one of those feeds is stale, incomplete, or unavailable, the agent’s recommendation is compromised.

Batch-processed data cannot meet these expectations. As agents rely on constantly refreshed information, the delays inherent in traditional pipelines break the feedback loop required for safe autonomy.

Compounding the challenge, data teams already spend up to 40% of their time on data quality tasks—effort that becomes unsustainable as increasing data volumes and agent-driven processes exponentially increase data touchpoints and workload. With agentic AI, organizations can enable data engineering at greater scale, efficiently managing larger and more complex data environments than traditional approaches allow.

Agents Need Data Trust and Governance, Not Just Access

Giving an agent access to data is not enough. Because agents act without human approval loops, the data they use must be fully trusted, verified, and monitored. Incorrect or poor-quality data leads directly to wrong actions, which in turn results in business risks. In an autonomous environment, the impact amplifies quickly if data quality issues or other quality issues are not detected and addressed proactively.

The business impact is tangible. For example, a global e-commerce company incurred over $5 million in lost revenue after its recommendation agent operated on six-month-old product data and continued promoting out-of-stock inventory. In financial services, an advisory agent must rely on accurate balances and provide recommendations that comply with SEC and local regulations.

Across these environments, compliance exposure and customer harm are real risks—not theoretical ones. This is why enterprise agents require rigorous guardrails and governance. This includes lineage to understand data origins, quality scoring to assess reliability, access controls to prevent unauthorized exposure, and audit trails to ensure traceability of every decision path. Business rules are embedded within these governance frameworks to guide agent decisions and ensure that AI-driven data transformations adhere to organizational policies.

Much of what is dismissed as “LLM hallucination” is, in reality, the consequence of inconsistent, stale, or partially replicated data sources feeding the agent. Ongoing monitoring and validation are essential to maintain data health, ensuring agent reliability and accurate outcomes. A production-grade agent is only as safe and accurate as the governance foundation beneath it. For organizations operating in regulated industries—banking, insurance, healthcare—this foundation is non-negotiable.

In practice, this means agents need unified, policy-controlled access to CRM records, ERP transactions, supply-chain feeds, financial systems, files, documents, APIs, and operational databases. They also require guardrails—governance, quality scoring, lineage, and observability—embedded from day one. Without this foundation, even the most advanced agent will hallucinate, stall, or make unsafe decisions.

The Enterprise Data Architecture for AI Agent Systems

Standalone integrations, scattered pipelines, and inconsistent governance do not support autonomous systems that need to pull information from dozens of sources in real-time. 

An intentional enterprise-grade agent AI architecture unifies data access, delivers fresh context with low latency, and ensures every decision is backed by trusted data. 

Unified Data Access Layer for Multi-Agent Orchestration

A unified data access layer is the anchor for any multi-agent system. Instead of each agent building custom connectors to CRMs, ERPs, data lakes etc., all agents consume data through a single integration layer. A unified cloud-native data integration platform:

• Functions as middleware between agents and source systems—exposing standardized APIs and reusable data services that every agent can consume.

• Supports both structured sources (databases, data warehouses, operational systems) and unstructured content such as documents, call transcripts, emails, and Slack messages.

• Enables agent-to-agent coordination: a shared semantic layer and catalog allow agents to understand data definitions, locate the right datasets, and pass context between one another. Multi-agent collaboration networks further enhance this by allowing specialized agents to work together efficiently, sharing context and coordinating actions to solve complex data engineering problems.

• Eliminates the ‘integration spaghetti’ that occurs when individual agents maintain their own point-to-point pipelines. It also ensures consistent semantics, metadata, and access controls across all use cases. Within this unified access layer, specialized agents can be deployed to handle specific data engineering tasks, improving scalability and automation.

For example, Informatica Intelligent Data Management Cloud’s (IDMC) unified integration fabric provides over 300 pre-built connectors that reduce the need for custom development for each agent. For data leaders, the benefit is speed and consistency. Each new agent can be onboarded without rebuilding integrations, governance rules, or authentication logic. A centralized access layer also simplifies governance through a single point for lineage, audit logs, and role-based access controls—ensuring agents always operate on authorized, traceable data.

Real-Time Data Pipelines for Agent Responsiveness

Agents cannot rely on batch-oriented data architectures. Their decisions are time-sensitive, and their effectiveness depends directly on how current the underlying data is. Change Data Capture (CDC) pipelines provide the low-latency data synchronization required for high-frequency agent tasks, while event-driven architectures allow agents to act immediately when key business conditions change. Intelligent automation enables adaptive, context-aware data operations by leveraging AI-driven systems to streamline data ingestion, data transformation, validation, enrichment, and orchestration, reducing manual effort and improving efficiency.

Research continues to show that 91% of ML models experience accuracy degradation over time when fed stale or slow-moving data. The same risk applies to agents, amplified by their autonomy and decision velocity.

For example, a sales intelligence agent monitoring lead scores must be triggered the moment new enrichment, behavioral signals, or intent data arrives. Operating on outdated information makes its recommendations irrelevant.

Real-time data pipelines with ELT architecture for AI give agents flexible access to raw, schema-late data when they need broader context or new feature signals, and ETL pipelines work for high-performance, pre-structured datasets where faster loading and deterministic transformations are essential. This works alongside streaming systems such as Kafka for continuous ingestion and near-real-time CDC to keep operational systems synchronized.

Informatica’s streaming and CDC capabilities fit naturally into this layer, providing unified orchestration without piecing together multiple tools.

Ultimately, data freshness is the foundation of agent accuracy. The degree may vary, depending on the type of agents. For instance, micro-batch processing can support lower-latency analytical tasks when full streaming is not needed.

But without fresh data, downstream reasoning, planning, and task execution degrade rapidly, forcing teams to spend even more time on data prep instead of optimizing agent behavior.

Data Quality Monitoring and Observability

Traditional data quality checks were designed for analytics dashboards, not autonomous systems making real-time decisions. Agents require continuous, automated monitoring that validates whether the data feeding their workflows meets freshness, completeness, accuracy, and consistency thresholds, and must proactively detect and prevent bad data from impacting downstream decisions.

Agent reliability directly correlates with the underlying health of AI agent data pipelines. Data leaders need visibility into pipeline health and early signals of drift. Observability frameworks that track volume anomalies, schema changes, lineage issues, and quality degradation prevent agent failures before they reach production. When data quality drops, proactive alerts should immediately surface whether an agent is at risk of making incorrect recommendations or actions due to bad data.

In practice, agent performance should be tightly correlated with data quality metrics. A customer service agent’s resolution accuracy, for instance, can be traced back to CRM data freshness SLAs and completeness of order system feeds. If either dips, service quality declines.

Informatica’s data quality and observability capabilities demonstrate how continuous scoring, anomaly detection, and automated remediation integrate with broader agent monitoring. With 93% of organizations planning increased investment in data and analytics, with quality considered the top priority, enterprises need observability not just for pipelines, but for the agents that depend on them.

Building Production-Grade AI Agents: A Three-Phase Implementation Framework

Most enterprises launch agent initiatives with impressive prototypes but struggle to operationalize them. Only 48% of AI projects make it to production, and agents raise the bar even higher, because autonomy, reliability, and compliance hinge on data foundations. 

This three-phase framework consolidates the essential steps for moving from proof-of-concept to full-scale production, with a strong emphasis on resilient data architecture.

Phase 1: Data Foundation Assessment & Platform Setup (90 Days)

The first phase determines whether agents will scale or stall. Before building any agent logic, organizations need a complete picture of the data landscape and the infrastructure required to support autonomous decision-making.

Days 1 to 30: Assessment and Discovery

• Begin with a structured assessment of all data sources the agent will require, including CRM, ERPs, operational databases, data warehouses, BI sources, document repositories, APIs, and third-party enrichment services. Be sure to consider the entire data lifecycle—assess how data is ingested, transformed, monitored, and governed across all stages.

• Look for “hidden dependencies”: overlooked systems that agents must query to complete multi-step tasks.

• Map data quality and governance gaps: missing lineage, poor data quality scores, insufficient access controls, compliance standards

• Map baseline data freshness, latency, and accuracy SLAs to understand whether operational agents can rely on current pipelines.

• Evaluate real-time vs. batch data requirements for each planned use case to avoid reworks

A simple assessment checklist includes:

• What data does the agent need?

• Where does that data reside?

• How clean and trusted is it?

• How fresh must it be?

• Who owns it and who can access it?

• Does the assessment cover the entire data lifecycle, from ingestion to governance?

For example, an e-commerce brand planning a customer service agent might discover customer data spread across Salesforce, Shopify, Zendesk, Stripe, and multiple internal databases, requiring coordination across 12 integration points for a single workflow.

Days 31 to 90: Platform Deployment and Hardening

• Deploy a cloud data integration platform that exposes agent-friendly APIs and reusable data services. Platforms like Informatica Cloud Data Integration have native integrations with Snowflake, Databricks, and BigQuery, and support building core AI-assisted data pipeline development for the highest-priority agent use cases. Agents must also be able to adapt transformation logic dynamically, allowing them to automatically generate, modify, and verify data transformation processes as business requirements, data schemas, or system updates change.

• Cataloging is critical as agents cannot discover or request data they do not know exists. A unified catalog gives both engineers and agents visibility into datasets, metadata, access rules, and lineage.

• Establish governance frameworks: quality validation rules, policy-based access controls, schema change tracking, and security baselines.

• Check for any hybrid cloud considerations if you run ERPs or core systems on-premise while hosting agent logic in cloud environments. A modern integration layer must bridge both reliably.

• Complete the setup with monitoring and observability capabilities. Before any agent is built, teams must have real-time visibility into pipeline health, freshness, and data quality scoring. This is the same architectural principle used to support Informatica’s production agent, CLAIRE AI, as its performance depends on a highly governed, continuously monitored data backbone.

By the end of Phase 1, organizations have a hardened data backbone ready for agent development.

Phase 2: Agent Development with Data Reliability Testing (90 Days)

With the data foundation in place, teams shift to agent development—this time with instrumented data access, observability, and failure testing built in from day one.

• Develop agents with telemetry - the automated collection of detailed, real-time information about how an AI agent interacts with data and systems during development, testing, and production. Log every data request, latency pattern, and fallback scenario, and simulate degraded conditions such as missing fields, stale values, schema shifts, or slow APIs. The aim is to validate the principle that agent reliability = data reliability × model reliability. This aligns with the modular agent system concepts published by Databricks, but here the emphasis is on the data module: ensuring data access, validation, and fallback logic are treated as first-class components alongside planning and execution.

• Embed data quality guardrails directly into the agent workflow: freshness checks, completeness thresholds, schema validation, and logic for safe fallback when critical sources become unavailable. An example is a customer service agent that detects ERP latency and seamlessly switches to cached order summaries until the system recovers.

• Execute ‘red-team testing’, where data anomalies—incorrect formats, outdated fields, dropped tables—are intentionally injected to observe how the agent behaves. This way, failover patterns such as circuit breakers, context caching, and graceful degradation are validated before production.

• Establish evaluation metrics to align directly with the data SLAs defined earlier: P95 latency, freshness windows, and completeness scores. This ensures that the agent is safe, predictable, and aligned with enterprise controls.

Phase 3: Production Deployment & Continuous Monitoring (Ongoing)

Agent deployment is not the finish line. It only marks the beginning of comprehensive observability and continuous monitoring across both the data and model layers.

• Establish SLAs for data quality standards: For instance, < 5 minutes freshness for real-time or transactional agents, and < 1 hour for analytical or planning agents.

• Automated monitoring tracks pipeline uptime (targeting 99.9%), query latency (P95 < 500ms), schema stability, and data completeness (>95%). Alerts are tied directly to agent performance thresholds to prevent silent degradation.

• Ongoing feedback loops correlate agent accuracy, action success rates, and decision errors with underlying data quality trends. Agents must be able to adapt to novel situations in production, handling unpredictable or unprecedented scenarios as they arise. Drops in agent performance often signal upstream data issues, not model problems—a critical insight for long-term reliability.

Data quality debt becomes the new form of technical debt. As agents scale, every gap in lineage, metadata management, or observability compounds. With Gartner forecasting that 33% of enterprise applications will include agentic AI by 2028, organizations must prepare for sustained investment in data operations.

Phase 3 ensures agents stay reliable, compliant, and aligned with the evolving business context—turning early prototypes into durable enterprise capabilities.

Governance and Security for Enterprise AI Agents

As enterprises operationalize AI agents, AI governance and security become non-negotiable. Agents operate with autonomy, make decisions at speed, and interact with sensitive systems—conditions that significantly elevate compliance and risk management requirements. 

In regulated sectors such as healthcare, financial services, and insurance, a single incorrect action can lead to legal exposure, customer harm, or financial loss. A robust governance framework ensures that autonomous agents remain controlled, compliant, and accountable.

Data Access Controls and Agent Permissions

AI agents must be governed with the same precision as human users—sometimes with stricter safeguards. Role-based access control (RBAC) now extends to agents, ensuring that each agent inherits permissions based on its designated role or the user context it represents. 

Apply the principle of least privilege

Qn agent should only access the minimum data necessary to perform its assigned workflow. Blanket admin privileges for “AI agents” introduce unacceptable operational risk.

For example, a recruitment agent may legitimately access candidate profiles, interview summaries, and scheduling information. But it must not access compensation history, medical records, or performance reviews. Similarly, a financial risk agent can evaluate transaction anomalies but should not read customer biometrics or insurance claims.

Access controls

To meet compliance obligations—SOC 2, GDPR, HIPAA, ISO 27001, and industry-specific regulations—enterprises must maintain strict access controls and enforce them programmatically. Every agent interaction with enterprise data should be logged. Comprehensive audit trails are critical for post-incident forensics, regulatory reporting, and demonstrating internal policy adherence.

A unified governance platform:

Simplify these requirements by centralizing lineage, access policies, consent management, and security controls. For example, Informatica IDMC’s enterprise certifications and governance capabilities support these patterns without requiring fragmented toolsets. For multi-agent systems data integration, this unified approach ensures consistent enforcement across all autonomous workflows.

Observability and Incident Response

Observability is essential for maintaining safe, predictable agent behavior. Traditional model monitoring is insufficient because agent actions depend heavily on the data layer. Effective agent observability integrates data observability, pipeline monitoring, and agent telemetry into a single operational view.

Continuous monitoring and integrated observability

Check pipeline health for latency, freshness, schema stability, and quality scores. Track agent-level patterns such as response times, query volumes, retry rates, and error sequences. When an agent’s performance drops, correlation with data lineage reveals whether the issue originated in the source system, a transformation job, or downstream API.

For example, if a customer service agent’s accuracy drops 15% overnight, lineage analysis may show that CRM updates were delayed by 24 hours due to API throttling. The root cause is data freshness, not model drift. This distinction is critical because without integrated observability, teams may incorrectly retrain models when the real issue lies in the data pipeline.

Proactive alerting

Ensure teams are notified when data quality standards dip below operational thresholds, preventing agents from acting on stale or incomplete context. Incident response procedures should isolate the failure domain quickly—data layer, agent logic, or model inference—and trigger automated safeguards such as fallback routines or human escalation.

Monitoring platforms such as DataDog and Grafana support metrics visualization, while Informatica’s data observability capabilities provide upstream context, quality scoring, and lineage mapping. Together, these ensure that autonomous agents remain safe, compliant, and stable—even in complex, fast-moving enterprise environments.

Real-World AI Agent Engineering Success Patterns

Effective AI agents succeed not because of sophisticated reasoning alone, but because their underlying data infrastructure consistently delivers trusted, complete, and fresh context.

These real-world patterns illustrate how unified data access, real-time pipelines, and strong governance directly translate into measurable business outcomes.

Customer Service Agents with Unified Customer 360 Data

Challenge

Customer-facing agents depend on a complete and timely understanding of each customer. In many enterprises, this information sits across 8–12 different systems, from Salesforce for CRM, Zendesk for support cases, separate order management platforms, payment processors, loyalty programs, product catalogs, and internal billing systems. 

Without data unification, agents cannot resolve even straightforward queries with confidence.

Solution

A modern enterprise AI data integration platform unifies Salesforce CRM, Zendesk support tickets, order management system, payment processor, the loyalty program, product catalog etc., to build a real-time Customer 360 layer that agents can query in sub-second timeframes.

Key success factor

Pipelines must maintain <5-minute freshness windows and deliver 99.9% uptime to keep service quality consistent. 

Outcome

Once unified, agents can handle high-volume scenarios such as order status checks, return eligibility, billing inquiries, and account updates without human escalation.

For example, e-commerce and SaaS organizations report significant improvements once fragmented data is unified. These include higher first-contact resolution, reduced average handle time, and more consistent customer experiences. The differentiator is not a more advanced LLM; it is the ability of the agent to act on a trusted, holistic customer profile powered by reliable, integrated data.

Sales Intelligence Agents with Multi-Source Lead Enrichment

Challenge

Sales intelligence agents thrive when they have access to rich context across the full revenue stack. In most organizations, the signals needed to prioritize leads are distributed across more than 10 systems—Salesforce for accounts, website analytics for digital behavior, third-party intent networks like 6sense, contact data from ZoomInfo, outreach signals, LinkedIn activity, and past win/loss records.

Solution

A real-time, event-driven architecture brings these sources together. When a buying signal changes—an intent spike, a new website visit, or re-engagement with an email sequence—a trigger notifies the agent. Freshness matters, especially for intent signals that degrade within hours. Pipelines need to refresh within <1 hour to maintain relevance.

Key success factor

Event-driven architecture triggers agents when buying signals change; fresh data (<1 hour) critical for intent signals

Outcome

With integrated enrichment, the agent operates as an “SDR teammate,” highlighting high-propensity accounts, summarizing engagement patterns, and preparing contextual outreach notes for human sellers. 

Organizations that adopt this architecture see stronger pipeline conversion because prioritization is based on complete, timely data rather than isolated signals.

Subtle but foundational to this success are real-time change data capture, multi-source integrations, and low-latency pipelines—capabilities core to unified enterprise-grade platforms like Informatica.

Measuring AI Agent ROI Through Data Quality Metrics

Enterprises often evaluate AI agents based on model accuracy or automation rates, but the true determinant of ROI is data reliability. Agents that rely on inconsistent, stale, or incomplete data produce unreliable outcomes, increasing costs instead of reducing them. 

Agent success formula: The formula is straightforward and essential for data and AI leaders: Agent ROI = (Business value delivered) × (Data reliability score)

Data freshness correlation: Data quality directly influences every component of agent performance. Research consistently shows that 91% of AI models experience temporal degradation, meaning agent accuracy declines as data ages. 

Data completeness impact: Missing or incomplete fields force agents to make assumptions or escalate to humans unnecessarily. 

Data consistency requirement: Inconsistent records across multiple systems break workflows, lead to incorrect decisions, and erode user trust. Master data management (MDM) is a crucial backbone for providing agents with reconciled, de-duplicated, and authoritative data.

Examples of business impact:  One global e-commerce company lost more than $5 million when its product recommendation agent continued operating on stale inventory data for months. Conversely, studies show that 75% of organizations that invest in data quality exceed expected AI outcomes.

Agent ROI = data infrastructure investment + AI model investment: Ultimately, agent ROI is inseparable from data infrastructure investment. High-performing models deliver their full value only when supported by fresh, complete, and consistent enterprise data.

Metrics Framework for Measuring Agent ROI

Data Infrastructure SLAs

• Data freshness: <5 minutes for operational agents; <1 hour for analytical agents

• Data quality score: >95% completeness for critical fields (order status, account balances, clinical records, etc.)

• Pipeline uptime: Target 99.9% availability for mission-critical agent data

• Query latency: P95 response time under 500ms

Agent Performance Metrics

• Task completion rate: Percentage of tasks completed without human intervention

• Decision accuracy: Alignment with ground truth or policy guidelines

• User satisfaction: Ratings from internal users or customers

• Time saved per interaction: Reduction in manual work

• Cost per transaction: Total cost for each agent-powered workflow
  

ROI Calculation Example

If an agent saves 15 minutes per customer interaction, across 1,000 daily interactions, at a labor cost of $30/hour, the value generated is:

$7,500/day → roughly $2 million annually.

With data accuracy at 98%, the agent performs reliably within acceptable risk thresholds. As data reliability improves or interaction volume scales, ROI increases proportionally.

Conclusion: Data Infrastructure as the Foundation of AI Agent Engineering

AI agent frameworks and LLMs often dominate industry conversations, but they are not what determine long-term success. The reality is more operational and less glamorous: data infrastructure is the decisive factor in whether agents produce measurable business value or remain fragile experiments. 

With more than 40% of agentic AI projects projected to be canceled by 2027 due to rising costs and unclear outcomes, the organizations deploying agents at scale are not necessarily those using the latest models, but the ones that invested in strong data foundations first.

For data and AI leaders, the strategic implications are clear.

• Treat enterprise AI data integration as a first-class architectural decision when building agents.

• Prioritize unified data platforms before expanding use cases. 

• Implement governance and quality standards that match the reliability and safety requirements of autonomous workflows. 

• Measure agent performance not only by what the model outputs, but by the health, freshness, and completeness of the data that drives those outputs.

As enterprises move toward a future where 33% of applications include agentic AI and 15% of work decisions are made by agents, competitive advantage will shift toward those with the most trusted, accessible, and well-governed data.

Organizations operating on modern platforms such as Informatica’s Intelligent Data Management Cloud (IDMC) will deploy agents faster, with greater accuracy, and at enterprise scale.

To explore how Informatica Cloud Data Integration and CLAIRE AI can provide the data backbone for reliable, production-grade agents, start here.

Frequently Asked Questions About AI Agent Data Engineering