One multinational enterprise is already moving beyond basic AI agent deployment. In an initiative in active rollout, the company is building the operational foundation required to make AI agents trustworthy at scale: AI context delivered from across the organization. Flexor’s AI Context Engine ACE powers the underlying unstructured AI-ready data architecture, enabling that effort.

The speed of AI operationalization means that forward-thinking enterprises are writing the rules as they go. What began as employees experimenting with public tools evolved into sanctioned internal chatbots. Then chatbots became workflows, and workflows became agents. Now, agents have become proprietary enterprise infrastructure. Suddenly, the question shifts from whether to adopt AI to how to govern, optimize, and trust what is being built.

One multinational, publicly-traded technology and services company understood this early. With tens of thousands of employees across multiple continents, they made a deliberate choice to treat AI not as an innovation initiative but as long-term infrastructure. Rather than limiting adoption to pilots or isolated use cases, they built an internal conversational AI agent designed to support HR, IT, Finance, and Legal.

For employees, the agent quickly became a new interface to organizational knowledge. Instead of navigating fragmented intranets, opening support tickets, or waiting on internal teams, users could ask natural-language questions and receive immediate answers. Adoption accelerated quickly, with thousands of employees actively relying on the system as part of their day-to-day work.

At the same time, the organization recognized a second challenge: understanding how well the agent was actually performing at enterprise scale. They needed visibility into agent outputs to identify inaccurate answers, missing business context, weak knowledge areas, and opportunities for continuous improvement. Optimizing AI operations quickly became just as important as deploying the AI itself.

The more employees depended on the agent, the higher the stakes became. In risk-sensitive environments, an incomplete or incorrect answer is not simply a usability issue. A wrong response about legal policy, financial procedures, employee entitlements, access controls, or regulatory obligations can create material business consequences. These may include compliance violations, operational disruption, financial exposure, audit failures, data handling mistakes, or employees acting on inaccurate guidance at scale.

This creates a new category of enterprise risk: employees confidently making decisions based on information that sounds authoritative, but may be incomplete, outdated, or wrong.

Planning for what comes after agentic AI deployment

The company’s technology and data leadership understood from the outset that deploying AI agents at scale was only the first phase. They intentionally planned for what would come next: building visibility into how the agent performs:

• Where answers succeed or fail
• What business context is missing
• How the organization’s underlying knowledge could improve continuously

This would ensure the agent could be trusted for decision-making.

They needed a solution that could analyze agent outputs to optimize the operation of the agent itself: improving answer quality, identifying knowledge gaps, reducing hallucinations, and ensuring responses remained aligned with current company policies, systems, and workflows. This would ultimately determine whether the agent could be trusted for enterprise decision-making and long-term adoption.

The initial instinct was to build a monitoring solution internally. But that effort quickly expanded into a data science initiative of its own, requiring dedicated resources, ongoing model tuning, custom evaluation frameworks, and continuous maintenance. Since it diverted attention from the core mission, the CTO looked for an out-of-the-box solution.

The importance of context for AI agent optimization

After evaluating multiple alternatives, the global enterprise chose Flexor as its unstructured AI context layer. The key differentiator was Flexor’s ability to ensure business context and domain intelligence were built into the AI architecture. Flexor ACE unifies, and contextualizes unstructured data (emails, calls, docs, notes and more) across the enterprise, while providing a Domain Intelligence Hub for company-specific terminology, policies, workflows, relationships, and evolving operational realities that make AI work specifically for each enterprise.

This matters, because evaluating whether an AI-generated answer is accurate requires understanding the business context behind the question itself.

Consider the following example: if an employee asks whether a supplier can be approved for payment, the correct answer may depend on information spread across multiple systems and unstructured sources: contract clauses stored in PDFs, invoice discrepancies discussed over email, procurement policies in internal documents, vendor risk assessments, and recent exceptions approved in finance or legal conversations.

A generic monitoring layer can detect that the agent produced an answer. But without connecting and contextualizing fragmented information across emails, contracts, documents, and operational records, it cannot determine whether the answer is actually correct in the company’s specific context, current, and aligned with how the business operates today. 

Under the hood of unstructured AI context

In this case, the company’s AI agent outputs flow into Snowflake, their cloud data platform of choice. Flexor runs directly on top of that infrastructure on AWS: no rip-and-replace, no new data warehouse or data residency issues, no disruption to existing workflows. Analysis is delivered within minutes of each interaction cycle, across three layers:

1. Agent-level analysis: Flagging hallucinations, inconsistencies, and policy violations at the agent output level.
2. Knowledge insights and gaps: Helping teams understand which topics are generating weak answers, which internal systems are under-documented, and where institutional knowledge has decayed.
3. Organizational insights: Providing the leadership with a view of where employees are consistently struggling and what knowledge and context the agent is missing to bridge that gap.

Human-in-the-loop workflows let the team validate Flexor’s outputs, so knowledge teams can trace issues back to their source. Every interaction is logged with full AI explainability and audit trails, which are increasingly critical as regulatory frameworks impose new accountability requirements on enterprise AI systems.

The business impact of adding context to AI agents

This initiative, in active rollout, reflects a broader shift in how enterprises operationalize AI. Instead of treating AI agents as standalone applications, the organization is building an AI context layer that it can monitor, improve, govern, and trust over time.

Adding Flexor as this context layer for unstructured data gives internal teams visibility into how the agent performs, where knowledge gaps exist, which answers require improvement, and how business context evolves across the organization.

This enables:

• Continuous improvement of agent output and knowledge quality
• Greater confidence from risk-sensitive departments supporting AI adoption
• Traceability, provenance, and auditable records for regulated environments
• Visibility into how employees interact with and rely on AI systems at scale

Trust is not a future consideration

The first generation of enterprise AI deployments concentrated on a single question: Can we build it? For organizations with sufficient resources, the answer was yes, and agents proliferated.

But generating answers is not the same as providing business impact. As AI moves from productivity tooling into operational infrastructure, the limiting factor is no longer the model. It is context, which leads to trust.

Enterprise knowledge does not live in a single database. It exists across emails, contracts, PDFs, tickets, policies, meeting notes, and internal conversations. Without the ability to prepare and contextualize that fragmented unstructured data, AI agents lack the organizational understanding required to produce reliable, trustworthy outputs at scale.

This is why context is becoming a foundational layer of enterprise AI architecture. Not only to improve responses, but to make AI systems governable, explainable, adaptable, and aligned with how the business actually operates in real time, so they can become trustworthy and provide tangible business impact.

While deploying AI has become table stakes, the real competitive advantage belongs to organizations that can build systems that understand their specific context. The future belongs to those who build AI the enterprise can actually trust.

About the author:

Or Zabludowski is Co-founder and CEO of Flexor, bringing more than 15 years of experience in unstructured data management and enterprise-grade AI systems. He has led large-scale initiatives focused on transforming fragmented enterprise data into actionable knowledge, and during the pandemic, he headed the national COVID data analysis effort.

Today, Zabludowski helps global enterprises turn unstructured data into trusted AI context through Flexor’s AI Context Engine, ACE.