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Report: Validation of controversial Glean Agent Builder claims

5 min read
11/14/2025
Regenerate

Summary

This report examines three controversial claims about Glean's Agent Builder and weighs supporting evidence against limitations found in documentation, community reports, and related technical sources.

The three claims validated here:

  • The Agent Builder is a true no-code visual builder that lets non-engineers compose multi-step agents.
  • The Agent Builder supports per-step model selection across multiple LLM providers via a Model Hub.
  • The Agent Builder automatically enforces enterprise permissions and governance when agents access company data.

Claim 1 — "No-code visual builder lets non-engineers compose multi-step agents"

What proponents point to

  • Glean markets the Agent Builder as a no-code, drag-and-drop visual interface that accepts natural‑language descriptions and generates multi‑step workflows and templates for common use cases. "Turn ideas into enterprise-ready agents. Create agents without code. Start with a natural language input or quickstart agent template, then bring your agent to life with a drag-and-drop builder." (product page)
  • Docs and marketing describe features that lower the barrier: natural‑language step generation, visual workflow nodes (branching/looping), templates, and sandbox previews for testing. (docs)

What critics and operational experience reveal

  • Multiple sources and community reports show that while routine and search‑grounded agents are buildable without code, highly customized, bidirectional, or deeply integrated multi‑system workflows typically require engineering effort (custom connectors, APIs, or the Agent Toolkit). "For intricate workflows or highly customized agents, Glean recommends using the Direct API or the Glean Agent Toolkit, which may require programming knowledge." (developer guide)
  • Debugging complex agents and error cases often requires understanding underlying SDK concepts and occasionally writing glue code (approval request wiring, custom connector error handling). Community bug reports and docs note UI and observability gaps when agents get complex. (debugging docs)

Bottom line

  • True for many common internal assistants and retrieval workflows: non‑engineers can assemble useful multi‑step agents using templates, NL input, and visual nodes.
  • NOT fully true for high‑complexity automation (bidirectional system writes, custom connector logic, complex data transformations) — engineering and SDK use are commonly required.

Claim 2 — "Per-step model selection across providers via a Model Hub"

What proponents point to

  • Glean documents a Model Hub and Model configuration UI where users can select models and tune settings; the platform references support for many models (OpenAI, Anthropic, Google/Gemini, and others). "The Glean Model Hub enables you to experiment with and select the most suitable model for each agent and its respective steps." (LLM admin docs)
  • Marketing and product notes emphasize per‑step configuration like temperature, token limits, and choosing different models for different tasks to optimize cost and capability.

What critics and operational experience reveal

  • Documentation and operational limits introduce practical constraints: rate limits on agent runs, MCP environment restrictions (some agent features not allowed on MCP), and gaps in debug traces for certain step types make multi‑model orchestration fragile in complex uses. (rate limits; MCP notes)
  • Public sources do not uniformly confirm dynamic model switching during live execution at large scale; some evidence is limited to step configuration time (i.e., pick the model per step in the editor) rather than fully dynamic runtime routing across providers.

Bottom line

  • Glean provides model selection tooling and a Model Hub that lets builders choose models per step in the editor — this is supported and useful for optimization.
  • Practical limits (rate limits, MCP/tool collisions, debug/observability gaps) reduce reliability for heavy-duty multi‑provider, per‑step orchestration in very large or latency‑sensitive deployments. Expect engineering work and operational testing when you rely on many models/providers.

Claim 3 — "Automatic enforcement of enterprise permissions & governance"

What proponents point to

  • Glean explicitly states agents enforce enterprise security and permissions when accessing company data and provides RBAC for who can create/share/configure actions in the Agent Builder. "Connect agents directly to live company data, enforce permissions automatically..." (product page; RBAC docs)
  • The platform advertises active data and AI governance (scanning for overshared sensitive data across connected apps) and admin controls for sharing and publishing agents.

What critics and operational experience reveal

  • Permission enforcement depends heavily on correct connector configuration and source-system permissions; misconfigurations (missing scopes or API limitations) can result in incomplete indexing or unintended exposure. Example: some Egnyte fields are unavailable to index via API, meaning their permissions/content may not surface correctly. (Egnyte connector notes)
  • In complex enterprises, inconsistent governance across sources, dynamic permissions, and the opaque behavior of agentic workflows can create gaps that require manual audits, policy tuning, and additional tooling. Exhaustive prevention of oversharing requires repeated audits and design-time governance. (governance blog)

Bottom line

  • Glean builds permission enforcement and RBAC into Agent Builder; for typical setups this provides good protection and sensible defaults.
  • Not a silver bullet: cross‑system misconfigurations, API limitations, and the inherent complexity of agentic workflows mean governance can fail in edge cases. Operational controls, audits, least‑privilege practices, and testing remain necessary.

Practical recommendations

  • Treat the no-code promise as "accelerates non‑technical development for common use cases" rather than "replaces engineers for all complex agents." Plan for engineering time for integrations, custom connectors, or complex error handling.
  • Use per-step model selection in development and testing to profile cost/latency; for production, validate cross-provider latency, rate limits, and error behavior under load.
  • Validate connectors and permissions before deploying agents that surface sensitive data. Run focused governance audits, enable least‑privilege connector scopes, and test edge cases where API limitations can hide fields or metadata.

Representative citations (selected)

Inline deep-dive topics you could inspect next: Does Agent Builder require code for advanced connectors? Does Glean support dynamic model switching at runtime? How reliable are MCP deployments for multi-model agents?

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