Imagine a brand-new marketing hire being handed a spreadsheet and expected to execute a campaign on day one. Would you blame them if it went badly, when no one had explained customer profiles, conversion goals, or lead qualification criteria?
AI agents may be the largest batch of new hires your company will bring on for years to come. Like any new employee, they start without the institutional knowledge their experienced colleagues have acquired on the job. They aren’t familiar with your data assets, your company goals, or the semantics that make your business unique.
Give an agent raw data without that context, and it will behave like a poorly trained employee, misinterpreting goals and concepts while bypassing required procedures. This is the context ceiling most enterprise AI pilots hit long before model intelligence becomes a limiting factor.
How you onboard agents, and the context you feed them, will determine whether they become productive members of the enterprise or merely automate mistakes at great speed.
Move beyond traditional data catalogs to onboard AI agents effectively
For many enterprises, the obvious place to start onboarding agents is their existing data catalog. Give an agent access to the company’s data and definitions, and it should have what it needs to get to work. The problem is that traditional catalogs were designed for human discovery, not the constantly changing, task-specific context that agents require. Much of that knowledge also lives elsewhere, spread across process documents, chat histories, data systems, and the accumulated know-how of experienced employees.
We’re moving from cataloging everything and searching it later to assembling context around intent.
Agents need context organized around the job they’re asked to do. If an agent is running a marketing campaign, it shouldn’t have to sift through everything the company knows to find what matters. A context layer can start with the marketing task, pull in the relevant institutional knowledge, and serve the agent only what it needs. It then has to keep learning, as definitions, processes, and business conditions change. Agents can help prune stale information and flag conflicting definitions, with people reviewing the cases that require judgment.
Treat AI context like code to keep it versioned, open, and governed
An enterprise context layer goes beyond documenting what data exists and what it means. It organizes the data, business semantics, processes, and institutional knowledge around the agent’s intent. That context may be structured or unstructured and distributed across cloud and on-premises systems.
To keep that context from scattering across teams and systems, leaders should connect these assets into a dynamic context graph that self-learns from different organizational signals. Code repositories, data assets, tribal knowledge sources such as chat threads and meeting notes, user roles, and organizational structure all provide context about how the business operates and how its entities relate to one another.
Context changes constantly as business definitions evolve and trusted sources go stale. Treating context like code means tracking versions, keeping an audit trail, and being able to roll back changes. Teams should also be able to export part of the context layer, experiment with it outside the main branch, and merge it back.
CIOs need visibility into what context is stored, how it’s used, and the identity an agent is operating under. When an agent acts on someone’s behalf, it should only see what that person is authorized to see. Autonomous agents should operate under their own identities with independently granted permissions.
For critical business metrics, verified SQL patterns known as golden queries can give agents a trusted path to the answer without asking the model to calculate it from scratch. Open standards such as the Open Knowledge Format can keep context portable as companies adopt new models and agent tools. Those technologies will change. The proprietary knowledge that teaches them how your business works is far more durable.
As enterprises move from deploying a handful of agents to potentially millions, context becomes an infrastructure problem. CIOs need a shared context layer with enterprise-grade security, scalability, and availability. Without one, context can sprawl across agents and applications, creating new silos of organizational knowledge that become increasingly difficult to govern and keep consistent.
Capture institutional knowledge to build a competitive AI moat
Frontier models arrive knowing very little about how a particular business works. Your data, definitions, processes, and operational know-how fill that gap. Capture that knowledge as reusable context, and the next agent you deploy can benefit from what the organization has already learned.
Capturing context is only useful if an agent can find the right piece when it needs it. Context retrieval should factor in the task, the role of the agent, and the operating environment to deliver the optimal slice of context, enabling agents to generate insights and take action efficiently. This helps cut unnecessary tokens and tool calls while improving the speed, relevance, and accuracy of responses.
Models and agent tools will come and go. The institutional knowledge used to onboard them can get richer with every agent you deploy, becoming an asset competitors can’t buy or replicate.
Explore how to use the Google Cloud Knowledge Catalog to unify structured, unstructured, and SaaS data into a governed, agent-ready truth.