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Report: Is Glean a good agent builder?

4 min read
11/14/2025
Regenerate

Quick answer

Yes—Glean is a credible, enterprise-focused agent-builder when your primary goal is improving knowledge access, search-driven workflows, and building single-step or retrieval-augmented agents that surface authoritative information. It’s particularly strong for organizations that need permission-aware search, fast time-to-value, and strong data governance.

However, Glean is not a full automation/orchestration platform: if you need complex multi-step, bidirectional workflows, heavy customization, or deep writeback into multiple legacy systems, evaluate whether Glean’s search-first architecture and connector limits meet your needs.

Why Glean is a strong choice (what proponents point to)

  • No-code Agent Builder: A visual, natural-language-first interface lets non-technical users create agents without coding. Administrators can manage drafts, version history, and centralized agent libraries. (Glean Agent Builder)

"Glean's Agent Builder offers a visual interface that allows users to design and deploy intelligent agents without writing code." (Glean product docs)

  • Enterprise-grade integrations and connectors: Prebuilt connectors plus a developer platform for custom connectors let organizations index data from 100+ SaaS sources and proprietary systems, so agents have broad knowledge grounding. (Glean connectors docs)

  • Strong governance and security: Glean enforces permissions at index time, provides AI security policies (prompt-injection protection, content filters), and offers integrations with security partners (Palo Alto Networks, BigID) for runtime protection. (Glean AI security)

  • Proven productivity impact: Case studies report significant time savings (many customers cite hours saved per employee per week) and measurable ROI when Glean centralizes knowledge and powers agent responses. (TEI/Case studies)

  • Model and tooling compatibility: Glean integrates with common agent runtimes and tooling (Model Context Protocol, LangChain), enabling secure model access and retrieval-augmented generation patterns. (developers.glean.com)

Where Glean may not fit (what critics point to)

  • Search-first, not orchestration-first: Glean excels at retrieval, single-agent Q&A, and knowledge-driven tasks, but it isn’t built to orchestrate complex multi-step workflows or multi-agent choreography that perform writes back into many systems. (docs.glean.com crawling limits)

  • Indexing and file-size limits: Large files (>64 MB) are only partially indexed (metadata only), and initial crawls for big repos can take days—factors to consider if your knowledge is in very large documents or rapidly changing content. (docs.glean.com crawling limits)

  • Customization and extensibility: While Glean provides APIs and a developer toolkit, critics note it lacks a fully modular framework for deep custom agent behaviors and advanced automation; heavy customization can require engineering effort. (developers.glean.com API docs)

  • Data flow is primarily unidirectional: Glean focuses on ingesting and indexing enterprise data for retrieval; if your use case needs full bidirectional synchronization or transactional writebacks, Glean is not the primary orchestration tool. (docs on architecture)

Practical guidance: When to pick Glean

Pick Glean if:

  • Your top priority is improved knowledge discovery, search-driven assistance, and fast deployment of agents that answer questions or summarize documents.
  • You need strong permission-aware search and enterprise governance for sensitive data.
  • You want non-technical teams to build and iterate on agents quickly using a guided visual UI.

Consider alternatives if:

  • You require complex multi-step automation across ERP/CRM/ITSM with heavy writeback and orchestration.
  • You need extensive custom agent behavior beyond retrieval and summarization.
  • Your data is dominated by very large files (over 64 MB) or needs real-time bidirectional sync.

Short pilot plan (3–10 days)

  1. Pick a representative knowledge use case (support KB summarization, HR onboarding queries, or a Sales enablement assistant).
  2. Connect 2–3 primary data sources (Confluence, Google Drive, ServiceNow) and validate permission mapping.
  3. Have a business user build an agent in the Agent Builder using natural language instructions; iterate until useful.
  4. Run security checks (prompt-injection scenarios) and measure: average answer accuracy, time saved per lookup, CSAT for internal users.
  5. Evaluate gaps: does the agent need writeback or multi-step orchestration? If yes, design a hybrid solution (Glean for retrieval + iPaaS or low-code orchestrator for actions).

Bottom line

Glean is a strong, pragmatic choice for organizations that want fast, secure, and governable AI agents focused on knowledge retrieval and conversational assistance built by non-technical teams. It is less suitable as a standalone platform when your needs center on multi-step automation, bidirectional data flows, or heavy customization—those scenarios typically require pairing Glean with integration or orchestration platforms.