Glean agents: How they work and when you need more
Davis Christenhuis
AI agents are changing how enterprise teams handle repetitive work, turning hours of manual tasks into automated workflows. Glean agents take this approach by building automation on top of enterprise search, letting teams create assistants that pull from indexed company data.
This guide covers how they work, what they're built for, where the architecture hits limits, and when teams look for agent platforms instead.
📌 TL;DR
Want the highlights? Here are the key takeaways:
- What Glean agents are: AI assistants built on Glean's enterprise search platform that automate workflows by connecting to indexed company data with runtime permission enforcement.
- Pre-built options: Agent Library offers ready-to-use templates organized by department (engineering, HR, marketing, sales, support, and IT operations).
- Custom building: Agent Builder lets teams create workflows using natural language based on five core concepts: triggers, steps, actions, flow, and memory.
- Where they hit limits: Agents require Glean's full search infrastructure, are optimized for retrieval over complex orchestration, and may need technical support for custom actions.
- When teams look elsewhere: When automation is the primary goal rather than enterprise search, or when workflows need flexible deployment, cross-system orchestration, and no-code customization.
- What Dust offers: A platform where teams build custom agents without search infrastructure, deploy them in Slack and Teams, and choose models based on workflow needs.
What are Glean agents?
Glean agents are AI assistants that automate workflows by connecting to data indexed through Glean's enterprise search platform, with runtime permission enforcement.
They extend Glean's search capabilities into action. Instead of just retrieving documents, agents perform multi-step workflows like drafting responses, generating reports, analyzing data, and updating systems.
Each agent runs with the same permission model as Glean's search, meaning users can only access information and actions they have rights to in the underlying source systems.
Agents work through a series of steps defined in Agent Builder or pre-configured in templates from Glean's Agent Library. They combine search, reasoning, and actions to complete work without manual intervention.
💡 Curious how agent platforms work differently? Discover Dust →
What can you do with Glean agents?
Glean offers two paths for using agents: pre-built templates from the Agent Library and custom agents you build yourself.
Pre-built agents (Agent Library)
Glean's Agent Library provides ready-to-use agents organized by department. You can deploy these templates as-is or customize them.
Available departments:
- Engineering
- Marketing
- Sales
- HR
- Support
- IT Operations
- All Teams (cross-functional)
Popular examples:
- Meeting recap: Generates summaries with key takeaways and action items
- Pull request review: Automates code review with context checking and feedback
- Competitive brief: Creates one-page competitor summaries from internal data
- Support ticket next steps: Suggests resolution paths based on ticket history
- SEO article evaluation: Audits content against a 21-point checklist
Custom agents (Agent Builder)
Agent Builder lets you create custom workflows using natural language or Glean's visual interface. Agents are structured around five core concepts:
- Triggers: Events that start the agent. An agent can run manually from the Library, on a schedule, or in response to content or system updates.
- Steps: Individual units of work that execute in sequence. Each step performs an action or makes a decision about which path to follow next.
- Actions: Concrete tasks like reading data, writing to data sources, drafting content, or updating external systems. Glean provides a set of pre-built actions that agents can use.
- Flow: Logic that determines how the agent moves from one step to the next. You can add branches that choose different paths based on conditions, or call sub-agents to offload parts of the workflow.
- Memory: The context an agent retains as it runs. Every step adds its outputs to memory, and later steps can read from this memory to reuse results or make decisions based on prior work.
Agent Builder supports branching logic and sub-agents that handle specialized portions of a larger workflow. Agents can also use adaptive planning for iterative reasoning on complex tasks.
Where Glean agents hit limits
Glean agents work well for workflows centered on search and retrieval, but they face constraints in certain scenarios:
- Tightly coupled to Glean's search deployment: Agents depend on Glean's enterprise search infrastructure. Teams that don't need or want full enterprise search still have to deploy it to use agents.
- Cross-system orchestration requires setup: The agents support write actions like updating tickets and logging notes, but complex workflows across multiple external systems may require custom configuration or technical implementation beyond out-of-the-box capabilities.
- Search-first architecture: The agents are optimized for retrieval workflows. Teams building automation-heavy use cases may find the agent layer is secondary to the search product rather than a standalone platform.
- Action packs have limits: Custom actions beyond Glean's first-party action packs require technical implementation. Teams with unique workflow needs may need engineering support to extend agent capabilities.
When you need something else
Some teams need AI automation as their core infrastructure, not an add-on to enterprise search. Workflows that require cross-system orchestration, flexible deployment across tools like Slack and Teams, or customization without technical overhead often need platforms designed for agents from the start.
These platforms connect directly to your existing systems without requiring search infrastructure first, offer flexible deployment across the tools teams already use, and let business users build and customize agents without technical overhead. That's when teams start looking elsewhere.
What is Dust?
Dust is an AI agent platform that lets teams automate workflows by connecting AI to company knowledge and existing systems. Business users create agents through a no-code builder, link them to internal data sources, and deploy them wherever work happens.
Key features:
- Multi-model flexibility: Choose between GPT, Claude, Gemini, Mistral, and other models based on what each workflow requires.
- No-code agent builder: Business users build and deploy agents through plain language instructions without writing code.
- Context-aware infrastructure: Agents access company knowledge across connected data sources with fine-grained permission controls and Restricted Spaces for sensitive information.
- Cross-system actions: Agents create tickets, update CRMs, send messages, query data warehouses, and execute workflows across multiple platforms.
- Native integrations: Connect agents to 50+ data sources including Notion, Slack, GitHub, Salesforce, Google Drive, and many more.
- Enterprise security: SOC 2 Type II certified, GDPR compliant, enables HIPAA compliance.
Dust agents in action
Teams across industries use Dust to automate knowledge work and scale operations:
- Profound (B2B SaaS): Post-sales team reclaimed 1,800+ hours per month by automating customer data retrieval and QBR deck generation with two agents connected to Salesforce, Pylon, product analytics, and custom MCP servers.
- Vanta (B2B SaaS): GTM team saves thousands of hours annually with agents that automate QBR prep, surface compliance updates, aggregate customer feedback, and deliver data-rich meeting decks by orchestrating domain agents across GRC, finance, and product teams.
- Persona (B2B SaaS): Hit 80% AI agent adoption by deploying specialized agents across engineering, sales, and customer support that answer technical questions in Slack, generate RFP responses, create solution requirement documents from call transcripts, and accelerate fraud analysis with SQL query generation.
💡 Interested in more customer stories? See how other teams use Dust →
Building an agent in Dust
Dust's agent builder lets you create custom agents in minutes using plain language instructions.
Watch how it works:
💡 Want to test the builder yourself? Try Dust free for 14 days →
Comparison table: Glean agents vs Dust agents
Feature | Glean Agents | Dust Agents |
Primary use case | Search-first workflows with automation on top | Agent-first platform for custom automation |
Deployment model | Requires Glean enterprise search | Standalone agent platform |
Agent builder | Natural language + visual interface | No-code builder with plain language instructions |
Data source connections | 100+ supported integrations | 50+ native connectors plus MCP support |
Permission enforcement | Runtime permissions from source systems | Permission-aware access with configurable Spaces (Open and Restricted) |
Cross-system actions | Write actions supported, complex orchestration requires setup | Read/write actions across integrated systems |
Where agents run | Glean web app, Slack, Teams, browser extension, API | Slack, Teams, Chrome, Zendesk, web |
Best for | Teams that need enterprise search + automation | Teams building custom automation workflows |
Frequently asked questions (FAQs)
Can AI agents write to systems or just read data?
AI agents can both read from and write to connected systems, but capabilities vary by platform. Most agents can update records in CRMs, create tickets in support tools, draft and send messages, log notes, and trigger workflows across platforms. The complexity of write actions depends on the platform architecture. Some agents handle simple writes out of the box but require custom configuration for complex multi-system orchestration. When evaluating agent platforms, verify which write actions are supported natively versus which require technical implementation or API development.
What's the difference between AI agents and AI chatbots?
AI agents are specialized tools built to automate specific workflows, while chatbots are general-purpose conversational interfaces designed to answer questions. Agents execute multi-step tasks like generating reports, updating CRM records, analyzing data, or creating tickets without ongoing human interaction. Chatbots primarily respond to queries and provide information through conversation. The distinction matters because agents typically require more upfront configuration but deliver hands-off automation, while chatbots are easier to deploy but require users to initiate interactions each time.
Can non-technical teams build and manage AI agents?
Yes, if the platform provides a no-code builder with plain language configuration. Modern agent platforms let business users define what an agent should do through visual interfaces or written instructions without writing code or involving engineering teams. However, complex agents that orchestrate across many systems, require custom API integrations, or need specialized security configurations may still need technical support. The best approach is choosing platforms where simple agents can be built and managed by end users while providing pathways for technical teams to extend capabilities when workflows become more sophisticated.
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