Agents built for the way your enterprise works.

Scale complex work with agents grounded in your Enterprise Graph — encoding workflows, relationships, and context to personalize every interaction.

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Agentic Engine

Agents that build plans and execute by learning from your organization

Adaptive planning informed by enterprise context.

Planning doesn’t happen in isolation. It’s guided by learned user preferences, organization-wide lessons, and past agentic work — all captured in the personal graph and Enterprise Graph. Planning is adaptive, changing as new insights emerge so agents can take on more creative, complex, and strategic work.

Execute with precision.

Tool quality matters. Glean search handles broad, cross-app questions (“What are my open deals?”), while calendar search organizes data by time and people (“When are my upcoming meetings?”), and data analysis uses structured search and metadata (“What is our total ARR?”). Each tool and index is purpose-built for its retrieval task. Glean’s agent builder leverages powerful search to scale across hundreds of tools, and treats third-party agents as callable tools to support multi-agent systems.

Complete long-term memory with the Enterprise Graph.

The Enterprise Graph is the context that informs the Agentic Engine—improving response accuracy, expanding task capacity, and personalizing experiences for both the enterprise and each user. Agent history is just one node in the Enterprise Graph, combined with activities and actions from across enterprise applications to drive more dynamic, personalized work.

Run-time autonomy with design-time controls.

Unlike other agent frameworks that are either prescriptive or lax, Glean gives enterprises a bit of both. For everyday users, they can use the Assistant to understand, reason, and take action. Or, for builders that want more control and determinism they can use the agent builder to dictate looping, branching, triggering, and more to reimagine enterprise workflows. (Agentic Engine coming soon to Agents.)

Evaluation that keeps agents operating reliably.

Evaluation goes beyond just checking if an answer is correct. It measures whether an output is truly useful and what “good” looks like for each task — creativity for writing, helpfulness for resolving issues, coherence for instructional guides. To do this at scale, Glean uses LLM judges: language models trained to evaluate agent outputs within specific agent classes.