Glean spent its early years solving a deceptively hard problem: finding the right document, Slack thread, or Salesforce record inside a company that has too many apps and not enough memory. Glean Agents are the next step. Instead of just returning a link, they can read across those systems, reason about what matters, and take action. That shift sounds small. It changes what an enterprise AI assistant is for.
The promise is not another chatbot. It is a permission-aware teammate that can prep a renewal brief, route a support ticket, or update a project plan without a human copying and pasting between tabs.
What Are Glean Agents?
Glean Agents are configurable AI workers built on top of Glean’s enterprise search index and knowledge graph. They combine retrieval, reasoning, and tool use. You describe a job in natural language, connect the apps the agent should touch, and choose where it runs: Slack, Microsoft Teams, a browser sidebar, or an API.
Unlike a generic AI agent that starts from zero, a Glean Agent inherits the same permission model as the person using it. If an employee cannot open a compensation spreadsheet, the agent cannot summarize it either. In practice, it is the difference between a demo that wows and a deployment that survives a security review.
The naming can get a little theatrical, as I’ve argued before about AI companies naming features after human processes, but the underlying capability is real.
Why Glean Agents Are Different From a Chatbot
Most enterprise chatbots answer questions. Glean Agents are designed to complete workflows. That difference shows up in four places.
- Permission inheritance: The agent sees what the user sees. No separate data dump, no shadow index that ignores access controls.
- Cross-app context: A single request can pull from Google Drive, Slack, Salesforce, Jira, Confluence, Zendesk, and Microsoft 365 without a human stitching the story together.
- Actions, not just answers: Agents can create tickets, update records, draft messages, and trigger workflows through connected tools.
- No-code configuration: Teams can build agents from templates or prompts, then refine them with examples and guardrails.
Those four pieces matter because enterprise work rarely lives in one system. A customer renewal might involve a contract in Drive, a pricing exception in Slack, a support escalation in Zendesk, and an opportunity in Salesforce. A chatbot shows links. An agent assembles the brief and flags the risk.
What Can You Actually Automate?
Glean Agents are not going to run the company. They are best at bounded, repeatable work that currently eats 20 to 40 minutes at a time.
Sales and account research
An account executive asks for a pre-call brief. The agent pulls recent emails, meeting notes, support tickets, and CRM fields, then produces a one-page summary with open risks and suggested questions. The prep time drops from half an hour to a few minutes.
Support and IT triage
A support agent receives a bug report in Slack. The Glean Agent checks similar tickets, finds the known workaround in Confluence, drafts a response, and creates a Jira issue if the bug is new. IT can use the same pattern for access requests and laptop provisioning.
HR and onboarding
New hires ask the same 50 questions in their first two weeks. An HR agent can answer policy questions, link to benefits docs, and trigger onboarding tasks in Workday or BambooHR.
Engineering and project management
Engineering managers use agents to compile release notes from merged pull requests, summarize incident timelines, or chase missing updates across teams. The agent does not replace judgment. It removes the scavenger hunt.
Agentic AI is spreading into scientific work too. Anthropic’s Claude adds protein design to its resume, which shows how quickly the pattern is moving beyond office tasks.
The Building Blocks of a Glean Agent
Under the hood, a Glean Agent has four layers. Understanding them helps you separate a useful pilot from a science project.
Knowledge and permissions
The agent starts with Glean’s index of company content plus connectors to the apps you already use. Permissions are evaluated at query time, so the agent’s answer changes based on who is asking.
Actions and tools
Tools are the verbs. A tool might search Salesforce, send a Slack message, create a Jira ticket, or update a Google Sheet. Each tool has a schema and a permission boundary. The agent chooses which tool to use based on the request.
Triggers and surfaces
Agents can run on demand, on a schedule, or from an event. A sales agent might run when an opportunity stage changes. A support agent might run when a Slack emoji reaction is added. If the agent lives in Slack, adoption is usually faster than in a separate portal.
Guardrails and evaluation
You need a way to test whether the agent is right, safe, and cheap enough. That means sample queries, human review, and logs that show which sources the agent used. The industry is still figuring out how fast to ship these systems, a tension captured in pacing comes to the AI frontier. Enterprise buyers should be comfortable with a slower, more deliberate rollout.
Glean Agents vs. Microsoft Copilot, OpenAI, and DIY Frameworks
The comparison depends on where your work lives.
Microsoft Copilot is deeply tied to Microsoft 365. If your company runs on Outlook, Teams, SharePoint, and Excel, Copilot can be a natural fit. Its reach outside Microsoft’s ecosystem is improving, but cross-app work can still feel like a patchwork.
OpenAI and Anthropic offer powerful models and agent tooling. They can be excellent for custom workflows, especially if you have engineers who can manage prompts, tools, and data pipelines. What they do not automatically provide is an enterprise index that respects your existing permissions.
Open-source frameworks give you maximum control. They also give you maximum maintenance. That trade-off is familiar to anyone watching Meta returns to its open-source roots; open ecosystems move fast, but someone still has to run them.
Glean’s bet is that the hard part is not the model. The hard part is connecting the model to the right company context without breaking access rules.
Where Glean Agents Still Have Work to Do
Agent platforms are young. Glean Agents face the same practical hurdles as the rest of the category.
- Accuracy: Retrieval-augmented agents are better than raw chatbots, but they can still miss context or overstate confidence. Evaluation is not optional.
- Action risk: Reading a document is low stakes. Updating a Salesforce record or sending a customer email is not. Start with read-only agents and add write actions carefully.
- Cost: Agents that reason across many sources can burn tokens quickly. Set budgets and monitor usage.
- Change management: A useful agent changes job routines. Teams need training, clear ownership, and a way to report bad answers.
Autonomy without guardrails can cause real damage, a lesson the industry keeps learning. OpenAI’s escaped AI claims another victim is a reminder that agentic systems need boundaries before they get freedom.
How to Start a Glean Agent Pilot Without Wasting a Quarter
Pick one workflow with high volume, clear inputs, and a measurable output. A good first agent might draft weekly account summaries, answer IT access questions, or compile release notes. Avoid anything that requires deep negotiation or novel judgment.
Next, define what ‘good’ looks like before you build. If the agent saves 10 minutes per ticket and your team handles 500 tickets a week, that is 83 hours a month. If it saves 30 seconds, it is a toy.
Then run a two-week shadow period. The agent produces work, but a human reviews every output. Track accuracy, time saved, and the queries where the agent failed. Use those failures to refine prompts, add examples, or narrow scope.
Finally, decide where the agent lives. Slack and Teams usually beat a separate tool because they meet people in the flow of work. A browser sidebar works well for research-heavy roles. The API matters for embedding agents into custom apps.
The Real Shift: From Systems of Record to Systems of Action
For twenty years, enterprise software has been a system of record. It stored the truth. Glean Agents point toward a system of action, where the software can also retrieve, reason, and execute. That does not mean everyone gets an autonomous coworker tomorrow. It means the interface to work is changing from navigation to intent.
Instead of opening six tabs to prepare for a meeting, you describe the outcome. Instead of searching for the latest policy, you ask a question and get an answer with citations. Instead of copying data between tools, you approve an action the agent proposes.
The winners in this next phase will not be the companies with the flashiest agent demos. They will be the ones that solve permissions, evaluation, and trust at enterprise scale. Glean Agents are a credible attempt at that problem because they start with the unglamorous part: knowing what each employee is allowed to see. The rest is execution.