Introducing new self-learning context enrichment in Knowledge Catalog

Google Cloud launched self-learning context enrichment and a reimagined Knowledge Studio, both in preview, in Knowledge Catalog, its context engine for agents. The Gemini-powered enrichment analyzes agent and user interactions to find missing or outdated context and pull verified definitions from playbooks, code repositories and business documents into governed, reusable context sets; Google Cloud says internal benchmarks show Knowledge Catalog makes AI agents nearly 4X more accurate, with up to 33% lower latency and up to 75% fewer tokens. Knowledge Studio gives context engineers and data stewards a conversational way to explore their data estate, navigate a visual context graph and review proposed context changes before they are published to users and agents.

Today, we’re launching self-learning context enrichment preview and a reimagined Knowledge Studio experience preview in Knowledge Catalog https://docs.cloud.google.com/knowledge-catalog/docs/introduction , Google Cloud’s context engine for agents. Building on Knowledge Catalog's existing Gemini-powered enrichment, self-learning enrichment analyzes agent and user interactions to find gaps and keep your business context accurate and current. Knowledge Studio https://docs.cloud.google.com/knowledge-catalog/docs/knowledge-studio is the new command center for context engineers and data stewards to explore their data estate conversationally, navigate a visual context graph, and review proposed changes to the context graph before they are published to their users and agents. An agent is only as good as the organization-wide definitions, rules, and relationships it reasons over — the context, as it were. That’s why curating context has become one of the most important disciplines in enterprise agentic capabilities. Today, most of that work happens by hand. Context engineers write prompts and mappings agent by agent, while data stewards try to manually keep definitions current across thousands of assets. But this breaks down as organizations move from a handful of agents to thousands, and eventually millions. Knowledge Catalog’s Gemini-powered self-learning enrichment turns that manual upkeep into a continuous feedback loop. By analyzing agent and user interactions, it pinpoints missing or outdated context and pulls verified definitions from playbooks, code repositories, and business documents into governed, reusable context sets. Then, with optimized search, every agent gets precisely the right context it needs instead of reasoning over bloated prompts and raw schemas, and benefits from shared learning across your organization. Knowledge Catalog makes AI agents nearly 4X more accurate, with up to 33% lower latency, and using up to 75% fewer tokens, based on internal benchmarks. Consider a simple question to a Gemini agent: "What was the net revenue in EMEA last quarter?" Answering it correctly requires knowing how your company defines net revenue, which tables hold the bookings, and how they join, yet that knowledge is typically scattered across a finance playbook in Google Docs, a data model in GitHub, and a Confluence page. Static catalogs expect subject matter experts to manually capture and update business definitions, metrics, and other context upfront so that an agent can effectively query the right data. However, in reality, experts rarely have the time to document every asset, and the moment the finance team revises how partner credits are treated next quarter, static definitions fall behind and every agent with a hardcoded formula starts giving the wrong answer. Knowledge Catalog’s enrichment capability acts where you already keep your business knowledge: across your team’s every doc, repo, and table. It connects to your structured data across first-party and third-party sources and unstructured data in Cloud Storage, Google Workspace, GitHub, Confluence, and SharePoint. It finds, extracts, assembles, and maintains that business knowledge for you, linking those unstructured sources like playbooks and wikis and their context to your structured metadata in Knowledge Catalog like tables and schemas to make your structured data far more discoverable and actionable. On demand or on a schedule, it proposes business definitions, table and column descriptions, join relationships, golden queries, and task-specific agent instructions, each with source citations and a confidence score. On every run, it detects what changed at the source, retires obsolete definitions, and flags conflicts for review. It learns from both your user and agent interactions. You can configure enrichment to analyze interaction traces from Gemini agents, Conversational Analytics in BigQuery, or your custom agents while redacting PII, and it pinpoints where agents struggled, such as ambiguous column lookups, ungrounded metrics, missing definitions, or users rephrasing the same question. It then finds the authoritative answer and stages a fix, so the same mistake doesn't happen twice. That's how context engineering keeps pace with millions of agent interactions, freeing experts to review changes instead of writing them from scratch. Automation doesn't mean giving up control. You can choose to enable or disable auto-publishing at any time, and set the exact confidence score for example, 85% at which proposed changes are automatically approved. Anything below your threshold, along with sensitive updates such as deletions, become a Change Request for a steward to approve, edit, or reject. You can also provide your own evaluation set to test proposed updates end to end. Every change is logged with full provenance and can be reverted. And of course, Google never uses your data to train its models. By design, an enterprise context graph is quite broad, but for a given task, both agents and users often only need the right, trusted segment of it. Knowledge Catalog achieves that with context sets — curated, governed subsets of the context graph that contains the definitions, metrics, join logic, and golden queries for a specific domain, team, agent, or group of end users. For instance, a sales agent can be bound to the Sales context set, while a finance analyst's assistant can be bound to the Finance context set. Because "net revenue" can legitimately mean different things to each group, every domain maintains its own definitions in the same catalog. However, agents don't have to be bound to a single set; with search in Knowledge Catalog, an agent can also dynamically search and find the right context set for any given task. Context sets also provide built-in data governance and fine-grained access control. Access to each context set is managed with IAM, so stewards decide which agents and users can read, use, or edit it. Agents only retrieve context from the sets they're authorized to use, and access to the underlying data continues to follow your existing permissions. Every update is traceable, and reversible, and because context sets build on the Open Knowledge Format https://cloud.google.com/blog/products/data-analytics/how-the-open-knowledge-format-can-improve-data-sharing OKF , you can import an OKF bundle to become a context set and export it back out as OKF so your context stays portable. All of these capabilities come together in the reimagined Knowledge Catalog experience, built for context engineers and data stewards who curate and govern context for agents. The new Knowledge Studio turns Knowledge Catalog from a repository of static inventory into an interactive command center, allowing you to: These features will be rolled out to customers in the next few weeks. Sign up https://docs.google.com/forms/d/1Xsxc1j9XqHupqnvfn7hC1bHtAB90ro4GqkCZboAPW M/edit to request immediate access and visit the documentation https://docs.cloud.google.com/knowledge-catalog/docs for step-by-step guidance.