Informatica has announced major ecosystem expansions at Informatica World 2026, embedding its data management capabilities directly into Amazon Web Services and Microsoft platforms to give AI agents native access to trusted, governed enterprise data intelligence.

Headless Data Management for AWS Agentic Workflows

Informatica is expanding its partnership with AWS to embed its data management capabilities — via Model Context Protocol servers and CLAIRE agent skills — directly into AWS AI services. The integration enables organisations to access trusted, governed, and context-rich data within AI agents, addressing one of the most significant barriers to enterprise AI adoption: fragmented and unreliable data that undermines agent reliability.

The headless architecture means Informatica’s data quality, governance, and cataloguing capabilities can be consumed by AI agents running inside AWS without requiring users to navigate a separate interface — a design approach suited to agentic workflows that operate autonomously across multiple systems.

Microsoft Foundry and Fabric Integration

In a separate announcement, Informatica and Microsoft have expanded their collaboration to integrate Informatica’s data management capabilities into Microsoft Foundry and Fabric, enabling AI agents and analytics workloads to run on trusted, governed data at scale. The collaboration focuses on making enterprise data more connected, discoverable, and AI-ready — helping joint customers accelerate AI adoption with high-quality, interoperable data foundations.

Microsoft Fabric, the company’s unified analytics platform, and Microsoft Foundry, its enterprise AI development environment, become significantly more powerful when grounded in clean, governed data — an area where Informatica’s CLAIRE intelligence layer is designed to add immediate value.

Data Quality as the Enterprise AI Bottleneck

Both announcements address a broadly recognised constraint in enterprise AI deployments: AI agents are only as reliable as the data they act on. Governance failures, data fragmentation, and poor metadata quality are among the leading causes of AI project failures in production environments. By embedding data management at the infrastructure level — inside AWS and Microsoft’s own AI tooling — Informatica is positioning data quality as a prerequisite rather than a post-deployment remediation exercise.