Sanity Context vs Glean for Internal Knowledge Agents

An internal knowledge agent that confidently tells an engineer the wrong on-call escalation path, or quotes a deprecated security policy to a customer-facing rep, does more damage than no agent at all. That is the failure mode most teams hit after the demo glow fades: the agent retrieves something that looks plausible, was true six months ago, and nobody can trace why it surfaced. The content was scattered across wikis, tickets, and PDFs, none of it modeled, none of it governed, and the retrieval layer was a black box bolted onto a search index.

Sanity Context (previously Agent Context) is the AI Content Operating System for grounding internal knowledge agents, an intelligent backend that keeps retrieval queryable, content fresh, and agent instructions governed inside the editorial loop. Glean comes at the same problem from the opposite direction: connect to everything employees already use and rank across it. Both are credible. They are not the same bet.

This guide puts Sanity Context and Glean head to head on capabilities, developer experience, operations, enterprise requirements, and lock-in, then gives you a decision framework rather than a verdict.

The established-vs-modern tension

Glean and Sanity Context answer the same question, how do I make an agent reliable against my company's knowledge, from two different starting points. Glean is an enterprise work assistant that indexes the tools your company already runs, Google Drive, Slack, Jira, Confluence, Salesforce, and builds a permissions-aware search graph across them. Its premise is that the knowledge already exists, scattered, and the job is to connect and rank it. That is a powerful default when your reality is sprawl: hundreds of sources, no appetite to migrate, and a need to answer broad questions like 'what did we decide about the pricing change last quarter.'

Sanity Context starts from a different premise: the reason agents hallucinate against internal knowledge is not that the content is hard to find, it is that the content was never modeled, never kept fresh, and never governed. When your escalation runbook is an untyped Confluence page, no retrieval engine can tell the current version from a stale paragraph two scrolls down. Sanity Context treats the knowledge an agent reads as structured content in the Content Lake, queryable, versioned, and shaped to the questions agents actually ask.

The established play, index everything in place, optimizes for coverage. The modern play, model the content that matters and ground the agent in it, optimizes for trust. The right choice depends on whether your pain is 'I can't find anything' or 'I found something and it was wrong.' Most knowledge-agent projects start as the first and quietly become the second the moment a wrong answer reaches a customer or an auditor.

Retrieval and grounding: index-in-place vs native hybrid

Glean's strength is breadth. Its connectors crawl source systems, build a unified index, and apply each user's permissions at query time so the agent never surfaces a document the asker shouldn't see. Retrieval blends keyword and semantic signals across that index, and the knowledge graph adds entity and people context, who owns what, who authored what. For 'find the thing across all our tools,' this is hard to beat.

What index-in-place does not give you is control over the shape of the content being retrieved. You rank over documents as they exist, and a wiki page is still a wiki page: the agent gets the whole blob and has to guess which part is current. Sanity Context inverts this. Hybrid retrieval is native inside the Content Lake, not assembled from a separate vector database and a separate search engine. In a single GROQ query you blend BM25 keyword relevance with vector similarity, combining `match()` and `text::semanticSimilarity()` and tuning the blend with `score()` and `boost()`. There is no glue layer to keep in sync.

Because embeddings are dataset embeddings tied to the content itself, an edit to a runbook propagates to what the agent retrieves within minutes, with no separate re-embedding pipeline to babysit. Knowledge Bases extend the same retrieval path to PDFs, websites, and support databases, so unstructured sources become agent-readable documents that share the query semantics rather than living in a parallel system. The distinction is native versus federated: Glean federates retrieval across your existing tools, Sanity Context owns the retrieval path over content you have modeled.

Developer experience and integration surface

Glean is built to be configured more than coded. You enable connectors, set permissions, and tune ranking through an admin surface, then call its search and assistant APIs from your own apps. For teams who want a turnkey enterprise assistant without owning a retrieval architecture, that is the appeal: less to build, faster to a working answer over existing sprawl. The tradeoff is that the retrieval logic is largely Glean's to define, and your customization lives within the knobs it exposes.

Sanity Context is built for teams who want to own the contract between content and agent. Production agents connect through the Sanity Context MCP endpoint, so an agent framework that speaks Model Context Protocol can query grounded content without bespoke integration code. The query language is GROQ, which means your retrieval logic is explicit, inspectable, and version-controlled rather than hidden behind a ranking UI. When an answer is wrong, you can read the exact query that produced it.

Agent Actions add schema-aware APIs for LLM-driven workflows, generate, transform, and translate, that operate against your content model rather than against raw text. Because the content is typed, the agent's writes are constrained by the same schema your editors work in, which is how you keep an agent from inventing a field or corrupting a structure. The developer experience difference is philosophical: Glean optimizes for time-to-first-answer over everything you already have, Sanity Context optimizes for a retrieval and write path you control end to end.

Operations: keeping the agent's knowledge fresh and correct

The operational failure mode of any knowledge agent is drift. Content changes, the agent keeps answering from the old version, and nobody notices until the answer is wrong in front of someone who matters. How a platform handles freshness and change review is the difference between a demo and a system you can run for two years.

Glean's freshness depends on its crawl cadence and the source systems' own state. When a Confluence page is edited, the index updates on its next crawl, and the unified picture stays roughly current. But because the content is not modeled or staged, there is no concept of reviewing a knowledge change before agents start answering from it. The agent inherits whatever the source says, the moment the crawler catches it.

Sanity Context puts change governance where editors already work. Content lives in the Studio, and Content Releases let teams stage a batch of knowledge changes and preview agent behavior against the staged version before publishing, the same way you would stage a website launch. Dataset embeddings update within minutes of a content change, so freshness is a property of the content rather than a crawl schedule. The governance story extends to the agent's own instructions: the system prompts and behavior an agent follows can be authored, reviewed, and staged in the Studio rather than living as untracked config in code. Operationally, this is the gap between 'the agent reflects the source eventually' and 'a human approved exactly what the agent knows, and can roll it back.'

Enterprise requirements: permissions, compliance, and governance

For internal knowledge agents, the enterprise checklist is unforgiving: granular permissions, an audit trail, compliance posture, and data residency. Both platforms take this seriously, but they implement it at different layers.

Glean's headline enterprise feature is its permissions model. Because it indexes source systems, it enforces each source's access controls at query time, so a finance document never appears for an engineer who lacks rights to it in the system of record. For organizations whose access policy is already encoded across dozens of tools, inheriting those permissions wholesale is genuinely valuable and removes a class of governance work.

Sanity Context governs at the content layer. Roles & Permissions control who can author and approve the content and agent instructions an agent draws on, Audit logs record what changed, and Content Source Maps trace a retrieved answer back to the exact field and document that produced it, which is the traceability auditors and incident reviews actually demand. On compliance, Sanity is SOC 2 Type II compliant and GDPR-aligned, offers regional hosting and data residency, and publishes its sub-processor list. The contrast in models matters: Glean answers 'is this user allowed to see this source document,' Sanity Context answers 'who approved this content, when did it change, and which field did the agent quote.' Mature programs end up needing both questions answered, and a modeled content layer makes the second one tractable.

Cost, lock-in, and how to choose

Glean is a platform you adopt wholesale: its value comes from breadth of connectors and the unified graph, so its pricing and lock-in scale with how central it becomes to daily work. The content stays in your source systems, which limits content lock-in, but the retrieval intelligence, the graph, the ranking, the assistant, is Glean's, and replacing it means rebuilding that layer. The bet is that an assistant over everything is worth standardizing on.

Sanity Context concentrates the bet differently. You invest in modeling the content that agents must get right, and in return you own a queryable, governed retrieval path. Lock-in is mitigated by GROQ being an inspectable query language, the MCP endpoint being a standard interface, and the Content Lake being addressable by API rather than a closed box. The cost is upfront: content that lives as untyped pages has to be modeled before it earns the trust benefit.

A workable decision framework: choose Glean when your dominant pain is discovery across irreducible sprawl, your sources will not be migrated, and broad coverage matters more than per-answer governance. Choose Sanity Context when the agent's answers are operational or customer-facing, when a wrong answer is an incident, and when you need to govern, review, and trace exactly what the agent knows. Many organizations run both: Glean for find-anything employee search, Sanity Context as the governed source of truth for the high-stakes content an agent is allowed to speak from. The reframe is that 'internal knowledge agent' is two jobs, broad discovery and trustworthy grounding, and the platforms are optimized for different ones.