In Part 1 of this series, we explained why AI projects take too long to deliver. We introduced the concept of enterprise context and explained how this context is fragmented. Today, teams manually stitch together the enterprise context that is required for AI Agents to generate reliable and accurate outputs. However, this process is slow and inefficient.
In this post, we’ll walk you through the framework for unifying your enterprise context, its structure, and discuss potential pitfalls when managing the context at scale. The goal is to make it accessible for any AI agent to make them more intelligent and grounded for your needs. We will also briefly touch upon Trust3 IQ that takes this idea into something you can apply today in your projects.
The Framework for Enterprise Context
Enterprise context can be understood as a set of interconnected layers. Each layer plays a unique role. Together, they enable AI agents to deliver accurate & reliable results.
| Name | Definition | Purpose | Sources |
|---|---|---|---|
| Technical Metadata & Provenance | This layer captures the physical structures like tables, schemas, indexes and tracks lineage: where data comes from, how it’s transformed, and how it’s used.
For example, if an AI agent recommends a sales forecast, provenance ensures the numbers are pulled from the latest dataset, not an outdated extract. |
Developers and auditors can trace results back to the source. | CatalogsDocuments, ERWin docs, SMEs |
| Business Semantics | Captures standardized definitions of metrics, KPIs, and taxonomies. Take “Active User” or “Monthly Recurring Revenue.” | Define these terms precisely and version them, so their meaning is consistent across business units and time periods. | Semantic models, Analytical or BI systems. |
| Business Ontology, Synonyms and Mapping | This layer defines how entities like customers, products, or accounts relate to one another. For example, in retail it lets AI connect customer transactions with product hierarchies, enabling cross-category buying behavior. It also translates everyday language to enterprise-specific concepts. For example, “prospects” maps to the Leads table, and “clients” to Customers. | Defines business glossary of terms, synonyms and relationships. | Data governance, Catalog and Semantic layers, SME |
| Institutional Knowledge | This layer encodes patterns of data usage, query history, and SME guidance. For instance, analysts when doing churn analysis have learned to use the “Events_Data” table instead of Customer_Activities because it lacks mobile usage. This isn’t codified anywhere but is learned by experience. | Understand why things are always done in a certain way. It captures the enterprise practices. | SMEs, Reports, Dashboards, Documents, Tickets. |
| Governance Context | Finally, the governance layer enforces access policies, privacy classifications, and compliance rules. For example, HIPAA regulations may restrict access to patient data; embedding those rules ensures the AI doesn’t surface restricted information. | To ensure security & legal compliance | Coded as SQL routines in data sources and governance systems. |
Building and Managing the Context
Now building an enterprise context isn’t a one-time exercise. It requires ongoing maintenance to support integrations, version control, inheritance, branching, and lifecycle management. Versioning ensures historical accuracy and updated standards. Meanwhile, inheritance allows for consistent global definitions with regional flexibility. Branching provides specialized contexts for specific projects or regulatory needs. Additionally, lifecycle management treats context like a product with creation, validation, rollout, monitoring, and updates. This structured approach ensures context remains reliable, auditable, and adaptable. These aspects are crucial for scaling AI agents in complex organizations.
This can quickly become an IT nightmare if not done right.
Introducing Trust3 IQ
Because we fundamentally believe that the only path forward for enterprises in the Agentic world is this enterprise context, we at Trust3 are making it our mission to operationalize this framework. Trust3 IQ is a new offering from Trust3 that is built on this framework. It provides an automated, governed, universal context engine for your enterprise. It connects to your data sources to unify the metadata, semantics, ontologies, governance, and institutional knowledge into a coherent whole.
- Knowledge Graph: At the heart of Trust3 IQ is a knowledge graph that organizes business entities, their relationships, and definitions in a way that’s machine-readable and easy to integrate. This graph serves as the backbone for the context engine to traverse through the different layers and retrieve and present the right context for AI agents to reason accurately across domains.
- Connectivity & Enrichment: Out-of-the-box connectivity with various fragmented data sources of enterprise knowledge including Snowflake, Databricks, dbt etc. to ingest and enrich the metadata extracted from these sources.
- AI-Native: IQ is built with AI for AI. Leverages fine-tuned domain models to appropriately map the information and also to contextualize the requests from users or AI agents. Whether healthcare, finance, or retail, these models ground AI responses in the correct business context. Additionally, easily integrate IQ into your AI Agents with MCP (Model Context Protocol).
- Security & Governance: Combined with the governance layer, Trust3 IQ ensures that outputs are not only accurate but also compliant and auditable.
For developers, Trust3 IQ acts like a knowledge SDK. Instead of rebuilding definitions and mappings for each new AI agent, they can connect to a standardized layer. This shortens development cycles, reduces errors, and ensures every agent starts with the same foundation of trusted knowledge.
With the release of Trust3 IQ now available as Limited Beta, we are excited to embark on this journey. Our goal is to codify enterprise knowledge and empower businesses to spring forward into the age of AI.
Practical Applications
Some common use cases and how you can apply enterprise context to increase the effectiveness of AI agents.
Horizontal Use Cases
- AI/BI Analytics: Building upon the use case that we discussed in Part1 about self-service analytics agents, imagine equipping your product, strategy and FP&A (financial planning & analysis) teams to directly chat with the data. Anyone ever involved in the annual planning process knows how slow and painful it can be, with teams often having to wait months for IT teams to provide the reports and data that they need. Using AI Agents powered by enterprise context, equip business teams to quickly retrieve accurate and grounded data with the organization’s definitions & context. AI Agents do the grunt work of crunching the numbers and creating the plan for review.
- Intelligent Sales Accelerator Agent: Sales reps spend disproportionate time chasing low-priority or stalled opportunities due to lack of timely access to historical patterns or buying signals and knowledge about sales strategies in similar accounts. With AI Agents and the enterprise context, you can make this information readily available and bring it right inside the rep workflows with recommended actions & nudges.
Vertical Use Cases
- Clinical Trials Enrollment (Healthcare): Timely enrollment of patients is critical for pharma and life sciences companies to ensure their drug development programs stay on track and meet regulatory milestones. Traditional methods are time-consuming due to the manual integration of data from various sources. Reporting is retrospective, meaning recruitment delays are already in effect by the time insights become apparent. With AI Agents, this can be real-time with data pulled in as they come and Agents can provide context-aware recommendations instead of static charts and take actions to notify site managers and coordinators of recommendations.
- Loan Underwriting and Processing (Banking & Finance): Speeding or automating the loan underwriting process enables banks to scale their lending processes without sacrificing security & compliance. A loan underwriter AI agent automatically collects real-time financial data from banking APIs, queries historical credit records, and ingests supporting documents like tax filings and pay stubs using intelligent document processing. With the enterprise context, the AI Agent contextualizes this information with underwriting policies, regulatory rules, and past loan performance data highlighting risks and providing recommendations for human review. This reduces time while keeping the risks low.
Build specialized agents using your unique enterprise context, making them act reliably and accurately.
Request a Trust3 IQ demo today to bring your enterprise use case to life.
