Artificial Intelligence (AI) agents are rapidly transforming how enterprises operate, automating complex tasks, delivering insights, and driving smarter decision-making. However, their true power lies not just in their algorithms, but in the context they operate within.

Without trusted context, AI agents risk making inaccurate or irrelevant decisions, which can erode user trust and lead to costly errors.

Here, we explore the critical role of trusted context in building effective AI agents, and the context necessary for AI to function optimally, and highlight how Informatica Intelligent Data Management Cloud (IDMC) enables enterprise IT teams to provide AI agents with the right, qualified information when needed, all while abstracting away the complexity of context engineering.

Context is the relevant information and environmental factors that enable AI agents to:

• Interpret data correctly
• Make informed decisions
• Perform tasks accurately

Trusted context means this information is verified, reliable and aligned with business rules and policies. For AI agents, several types of context are essential:

1. Data Context: Details about the data itself, including its source, format, lineage, relationships, and quality.
2. Business Context: The operational rules, workflows and processes that define how data is used within the organization.
3. User Context: Information about the user’s role, preferences and intent – which guides personalized AI responses.
4. Governance Context: Policies, compliance requirements and security constraints that regulate data access and usage.

Without these layers of trusted context, AI agents may misinterpret data, violate compliance rules or provide irrelevant or misleading outputs.

Traditionally, embedding context into AI agents – context engineering – has been a complex and manual process. It involves integrating diverse data sources, codifying business rules, maintaining data quality, and enforcing governance policies – all of which can be time-consuming and error-prone.

For enterprise IT teams, this complexity can slow down AI adoption and limit the scalability of AI solutions, resulting in the question: How can organizations provide AI agents with trusted context efficiently and at scale?

Informatica Intelligent Data Management Cloud (IDMC) offers a unified platform that simplifies the creation and management of trusted context by integrating these core capabilities:

1. Metadata Catalog

The backbone of data context – the metadata catalog automatically discovers, catalogs and organizes data assets across the enterprise, capturing critical metadata such as data lineage, schema and usage statistics. The richer the technical metadata extracted, the better the context can be leveraged by the agent.

Example: An AI agent tasked with analyzing sales trends can query the metadata catalog to identify the latest validated sales datasets, understand their structure, and trace their origin to ensure accuracy.

2. Data Integration

Data integration capabilities enable seamless, real-time access to data from diverse sources, ensuring AI agents have a holistic and current view of enterprise data. AI agents can access integrated data streams without manual data wrangling, improving responsiveness and accuracy.

Example: An AI agent monitoring supply chain risks integrates data from inventory systems, supplier databases and external market feeds to provide timely alerts.

3. Data Quality

Data quality tools within IDMC continuously monitor and assess data for accuracy, completeness, consistency, and timeliness. They provide quality scores and flag anomalies, enabling AI agents to weigh the reliability of data sources. Before making decisions or providing recommendations, AI agents can consult data quality metrics to avoid using flawed or outdated data.

Example: An AI agent automating customer segmentation checks data quality scores to ensure the underlying customer data is complete and accurate, preventing misguided marketing campaigns.

4. Master Data Management (MDM)

MDM ensures a single, trusted view of critical business entities such as customers, products and suppliers by consolidating and reconciling data from multiple sources. AI agents rely on MDM to avoid confusion caused by duplicate or conflicting data, enabling consistent and accurate insights.

Example: An AI agent generating personalized customer offers uses MDM to access a unified customer profile, ensuring recommendations are based on the most complete and current information.

5. Data Governance

Governance applications enforce policies around data privacy, security and compliance, managing access controls, audit trails and regulatory requirements. Governance ensures AI agents operate within legal and ethical boundaries, protecting sensitive data and maintaining compliance.

Example: An AI agent handling data access requests consults governance rules to verify user permissions and automatically logs access for audit purposes.

By leveraging Informatica IDMC’s integrated capabilities, enterprise IT teams can provide AI agents with a rich, trusted context layer without the need for manual context engineering. IDMC with Cloud Data Governance and Catalog provide several capabilities to automatically enrich metadata by applying data classification and association with business glossaries. When an AI agent needs to perform a task such as generating a report or answering a query, it can reference the metadata catalog to understand what data is available, where it came from, and how it relates to other data. It uses enrichments to better translate and ground the request often formulated in natural language in the enterprise context. This ensures the AI agent accesses the most relevant and trustworthy datasets. This approach offers several benefits:

• Faster Time to Value: AI agents can quickly access verified metadata, quality scores and governance policies, accelerating deployment.
• Scalability: As data volumes and complexity grow, the platform scales to maintain trusted context without additional engineering overhead.
• Improved Accuracy and Compliance: AI agents make better decisions based on reliable data and adhere to governance policies automatically.
• Enhanced User Trust: Consistent and accurate AI outputs build confidence among users and stakeholders.

Informatica’s embedded AI engine, CLAIRE, demonstrates how trusted context can empower intelligent AI agents – such as Discovery and Data Quality Agents – without requiring complex context engineering. By leveraging metadata, data quality metrics, master data management (MDM), and governance rules within IDMC, CLAIRE automates and improves data management tasks like metadata curation and data cleansing.

CLAIRE in Action

In the first of two demonstrations of how Informatica’s embedded AI agent can be leveraged, CLAIRE discovery agent assists data stewards in managing data assets and ensuring compliance:

Task: The AI agent receives a request to identify datasets containing sensitive information subject to new privacy regulations.

Trusted Context in Action:

• The agent queries the metadata catalog to locate datasets tagged with sensitive data classifications.
• It uses data quality metrics to prioritize datasets with the most reliable information.
• Governance policies guide the agent on access restrictions and compliance requirements.

Result: The AI agent generates a prioritized list of datasets requiring review, helping data stewards focus their efforts efficiently and maintain compliance.

Another example is CLAIRE AI automating data cleansing with the Data Quality Agent:

Task: The agent identifies and corrects anomalies in product data before it is used for inventory forecasting.

Trusted Context in Action:

• Metadata provides schema and validation rules.
• Data quality tools highlight inconsistent or missing values.
• MDM ensures corrections align with the master product records.

Result: The AI agent improves data quality autonomously, enhancing forecasting accuracy and operational efficiency.

For more information about the Agentic data management implementation with CLAIRE, make sure to read Samiran Karmakar’s blog on the topic.

Integrating metadata cataloging, data quality, master data management, and governance within Informatica IDMC empowers enterprise AI agents to access the right, qualified information when required, without the complexity of manual context engineering. This trusted context not only enhances AI accuracy and compliance but also accelerates AI adoption and builds user confidence.

As AI continues to reshape enterprise operations, investing in robust data management platforms to create trusted context will be key to unlocking AI’s full potential.