Live webinar Nov 5: save your seatLive webinar Nov 5: Building Golden Records & Entity Resolution with Fluree. Save your seat
Best Data Platforms for AI Agents in 2026: Governed, Auditable Access
An AI agent is only as safe as the data it can reach. A security review asks three blunt questions: what can this agent read, whose permissions is it using, and can we prove what it saw when it answered?
This guide compares eight platforms that show up in 2026 evaluations for governed data access for AI agents: Snowflake, Databricks, Microsoft Fabric, Google Cloud, AWS, Fluree, Palantir AIP, and Glean. Fluree is our product. It’s listed sixth, and every other profile says plainly where that vendor wins.
Every vendor claim on this page is sourced and was last verified October 2026.
What is governed data access for AI agents?
Governed data access for AI agents means every agent query is checked against policy at the data layer, under a known identity, and recorded, so you can show what the agent was allowed to read, what it did read, and what the data said at that moment.
A system prompt that says “don’t show salaries” is a request, not a control. The control is a policy the platform enforces on rows, columns, attributes, or individual facts before results reach the model. When the check runs in the data engine, no prompt can widen it. When it runs only in the agent’s instructions, a clever prompt can.
The platforms below differ on three things: where policy is enforced (the data engine, a tool gateway, or a search index), whose identity the agent carries (the end user’s or a shared service account), and how long the evidence lasts (access logs, traces, and the ability to query data as it stood in the past). For the architecture behind this, see the AI agent governance guide; for the broader category, see context layer platforms.
Which kinds of platforms give AI agents governed access?
Four architectures answer the question. Knowing which one a vendor is shortens the evaluation more than any feature list.
Warehouses and lakehouses with built-in agents
Policy lives in the platform’s catalog as row filters and column masks, and the vendor’s own agents query inside it. Strongest when the data already lives there. Snowflake, Databricks, Microsoft Fabric, and Google Cloud.
Cloud agent toolkits
Managed building blocks for agent runtime, identity, tool gateways, and tracing. Data policy comes from the services the agent calls, so you assemble the governance story. AWS Bedrock AgentCore with Lake Formation.
Ontology and knowledge-graph platforms
Data is modeled as entities and relationships, and permissions attach to those objects. Agents reason over meaning, not raw tables. Palantir AIP inside its own operating platform; Fluree as a governed graph over data where it lives.
Permission-aware enterprise search
An index of SaaS apps and documents that inherits each source’s access rules, with agents built on top. Widest reach into everyday work content, but an index, not a system of record. Glean.
What should you evaluate in a data platform for AI agents?
Six capabilities. They set the comparison table’s columns and the fields inside every profile below.
Policy at the data, for agent identities
Row, column, attribute, or fact-level rules applied to every agent query, under the end user’s identity or the agent’s own. Ask where the check runs: in the data engine, at a tool gateway, or in a search index.
Audit trail of what an agent read
A record of which tables, columns, or documents each agent request touched, tied to an identity and kept long enough for an investigation. A query log shows what was asked; you also need what was read.
Point-in-time replay
Can you query the data as it stood when the agent answered? Most platforms keep history for days or weeks. Check the window before you promise an auditor anything.
MCP and agent interfaces
Managed MCP servers, agent APIs, and framework support, and whether each one is generally available or still in preview.
Structured and unstructured coverage
Tables and documents under one policy model, or separate systems with separate rules. And whether the platform reads data where it lives or needs it copied in.
Deployment options
Which clouds and regions, whether it can run in your own account or on your own infrastructure, and what exists for government and sovereign workloads.
Data platforms for AI agents at a glance
One row per platform: what it’s best for, plus the three capabilities that most often decide a security review. Interfaces, coverage, and deployment are answered inside every profile.
Short explanations rather than checkmarks — the remaining criteria are answered in full inside each profile below. Verified October 2026.
The best data platforms for AI agents
Every platform here can put an agent in front of company data under some policy. They differ in where that policy runs, whose identity the agent carries, and how long you can prove what it saw. Fluree is sixth, it’s our product, and the disclosure is in its card.
Snowflake
Policy, agent identity, and audit in one engine, for data that lives in Snowflake.
Snowflake runs its agents inside the platform: Cortex Agents, a managed MCP server, and Snowflake CoWork (formerly Snowflake Intelligence). Its 2026 work went into agent identity, so policies and logs can now tell an agent from a person.
Policy at the data: Row access and masking policies (Enterprise Edition) apply wherever a column is referenced. Cortex Agents take permissions from the querying user’s default role. The IS_AGENT_ACTIVATED function lets a policy withhold data whenever an agent is in the session, even if the user’s role could see it.
Audit of agent reads: ACCESS_HISTORY (Enterprise Edition) records the objects and columns each query read and the policies enforced, keeps 365 days, and names the chain of invoking agents. It can lag up to three hours. Agent monitoring captures planning, tool calls, and generated SQL for each step.
Point-in-time replay: Time Travel queries data as of a past moment: one day by default, up to 90 days on Enterprise Edition for permanent tables. The seven-day Fail-safe is for recovery by Snowflake Support, not for queries.
Agent interfaces: A Snowflake-managed MCP server, generally available since November 2025, plus the Cortex Agents REST API and Snowflake CoWork for business users.
Structured and unstructured: Cortex Analyst writes SQL that follows established access controls; Cortex Search handles hybrid vector and keyword retrieval over text. Iceberg tables can keep data in your own cloud storage.
Deployment: SaaS only: Snowflake can’t be installed locally or on private cloud infrastructure. It runs on AWS, Azure, and Google Cloud, with government regions on AWS and Azure.
Pricing: Cortex Agents and CoWork bill in AI Credits per million tokens; direct Cortex Analyst calls bill per 1,000 messages (pricing docs). Rates are in the Service Consumption Table, plus warehouse compute for the SQL agents run.
Key features
- Agent-aware policies through IS_AGENT_ACTIVATED
- ACCESS_HISTORY with the invoking agent chain, 365 days
- Time Travel up to 90 days on Enterprise Edition
- Generally available managed MCP server
Best for
- Organizations whose governed data already lives in Snowflake
- Security teams that want agent reads in SQL-queryable logs
- Teams that need rules for agents, not just for user roles
Where it falls short: The governance is strongest inside Snowflake. Data has to live in or be reachable from it, there’s no self-hosting, and replay stops at the Time Travel window. Two documented gaps to plan around: Cortex Search runs with owner’s rights, so searchers can see indexed rows the service owner can read, and semantic view sample values aren’t masked. For how semantic views relate to a context layer, see context layer vs. semantic layer.
Verified: October 2026
Databricks
One catalog governs tables, documents, indexes, MCP tools, and agents, with column-level lineage.
Databricks puts agents under Unity Catalog. Agent Bricks is its agent developer platform, Genie Agents answer questions in plain language, and Unity Gateway governs models and MCP tools. At Data + AI Summit 2026, attribute-based row filters and column masks went generally available.
Policy at the data: Row filters and column masks are evaluated at query time, and ABAC policies on governed tags went GA in April 2026. Genie Agents evaluate access as the end user’s own identity. For custom agents, on-behalf-of-user auth (Public Preview) runs with the requester’s permissions; the default passthrough mode uses the deployer’s.
Audit of agent reads: System tables hold audit logs (Public Preview) and table- and column-level lineage (GA), each with 365 days of retention. MLflow Tracing records every agent step’s inputs and outputs, and traces can be stored in Unity Catalog tables.
Point-in-time replay: Delta time travel by version or timestamp. Deleted files are kept seven days by default, VACUUM removes older versions, and Databricks advises against using table history as a long-term backup. Time travel isn’t supported on tables with table-level row filters or column masks; with ABAC policies it’s in Beta.
Agent interfaces: Databricks-managed MCP servers are Unity Catalog securables called through Unity Gateway; the Genie MCP server is GA, and several others need the Unity Gateway Beta. External and custom MCP servers are supported too, and Agent Bricks works with any framework or model.
Structured and unstructured: AI Search (formerly Vector Search) indexes text under Unity Catalog, and ai_parse_document (GA) extracts PDFs, images, and Office files. Lakehouse Federation gives governed, read-only access to Snowflake, BigQuery, Oracle, Postgres, and more.
Deployment: AWS, Azure, and Google Cloud, with serverless compute in Databricks’ account or classic compute in yours. AWS GovCloud supports FedRAMP High and DoD IL5, though several agent features aren’t available there.
Pricing: Pay-as-you-go DBUs, billed per second, with committed-use discounts. Agent Bricks is billed at the rates of the underlying Databricks products.
Key features
- Unity Catalog governs data, indexes, models, and MCP tools
- ABAC row filters and column masks, GA April 2026
- Column-level lineage and audit in system tables
- Genie Agents query as the end user
Best for
- Lakehouse-standardized teams building agents on Databricks
- Platform teams that want lineage from table to agent call
- Multi-cloud estates that want one catalog across clouds
Where it falls short: Replay is the weak spot: history defaults to seven days, VACUUM removes it, and time travel doesn’t work on tables with table-level row filters or masks. Several identity and audit pieces (per-user agent auth, the audit system table, parts of Unity Gateway) are still in preview, and agent features vary by region.
Verified: October 2026
Microsoft Fabric
One permission model across Spark, SQL, Power BI, and data agents, wired into Microsoft 365.
Fabric’s agent story runs through OneLake and Fabric IQ. Data agents answer questions over lakehouses, warehouses, semantic models, and ontologies, and they plug into Copilot Studio and Azure AI Foundry. OneLake security reached general availability in 2026; Fabric IQ and its ontology item are still in preview.
Policy at the data: OneLake security roles reach table, folder, row, and column, and apply across Fabric engines, including data agents. A data agent uses the requesting user’s credentials and permissions and is read-only. Caveat: OneLake roles don’t restrict workspace Admins, Members, or Contributors.
Audit of agent reads: Purview audit records for Copilot interactions list the resources accessed, with sensitivity labels. Purview support for Fabric data agents is in preview. OneLake diagnostics logs who accessed what and when, optionally immutable, but for Fabric engines it records the access grant, not the rows.
Point-in-time replay: Warehouse time travel queries as of a timestamp, with 30 days of retention by default, configurable from 1 to 120, and security rules still enforced. Lakehouse Delta tables support time travel too, but VACUUM removes files after seven days by default.
Agent interfaces: A published data agent can act as an MCP server, and data agents became generally available in Copilot Studio in September 2026. Fabric also lists remote MCP servers for core, warehouse, Power BI, and ontology workloads, some in preview.
Structured and unstructured: Shortcuts read data in place across ADLS, S3, Google Cloud Storage, Iceberg, SharePoint, and on-premises sources. Data agents don’t support unstructured files such as .pdf, .docx, or .txt; Microsoft routes documents to Foundry IQ instead.
Deployment: Runs on Azure capacities in Azure public regions. A GCC High offering is in preview, without Fabric IQ items or Power BI Copilot.
Pricing: Capacity-based: F2 to F8192 SKUs, billed per second, with optional reservations. Data agent usage draws capacity per token (100 CU-seconds per 1,000 input tokens), and the queries an agent runs are billed separately.
Key features
- OneLake security enforced across Fabric engines
- Data agents that run as the requesting user
- Purview audit, eDiscovery, and retention
- Data agents as MCP servers and Copilot Studio tools
Best for
- Microsoft-standardized enterprises already running Purview
- Teams putting governed data in front of Copilot and Teams users
- Analytics estates centered on Power BI semantic models
Where it falls short: The agent audit trail is still filling in: Purview coverage for data agents is preview, and OneLake diagnostics doesn’t record row-level reads by Fabric engines. Data agents return at most 25 rows by 25 columns, skip documents, and fail when source and agent capacities sit in different regions. Replay tops out at 120 days on Warehouse.
Verified: October 2026
Google Cloud (BigQuery + Gemini Enterprise)
Agents query as the end user, and table reads land in audit logs you can’t switch off.
Google pairs BigQuery’s fine-grained security with the Knowledge Catalog (formerly Dataplex) and the Gemini Enterprise Agent Platform (formerly Vertex AI). The renames landed at Next ’26: Agent Engine is now Agent Runtime, and Vertex AI Search is now Agent Search.
Policy at the data: Row-level security, column-level security with policy tags, and dynamic masking applied at query time. The Conversational Analytics API queries data with the asking user’s credentials, and Agent Identity gives agents their own principal that can act for a user. Standard edition has no fine-grained security controls.
Audit of agent reads: BigQuery Data Access audit logs can’t be disabled and record table reads. INFORMATION_SCHEMA.JOBS keeps 180 days of query history with referenced tables. When an agent acts for a user, logs show both identities. Caveat: MCP-layer data access logs are off by default.
Point-in-time replay: Time travel covers a window of two to seven days (seven by default), plus a seven-day fail-safe you can’t query. On tables with row-level policies, only principals with an override permission can read history. Table snapshots preserve a moment for longer.
Agent interfaces: A fully managed remote BigQuery MCP server, the open-source MCP Toolbox, the Conversational Analytics API (GA for BigQuery and Looker), and Agent Runtime for ADK, LangGraph, LangChain, and other frameworks.
Structured and unstructured: Object tables put Cloud Storage files under BigQuery governance, and ObjectRef columns place files next to rows. Agent Search access controls respect end-user permissions on documents (Preview). BigQuery Omni queries S3 and Azure Blob in place.
Deployment: Google Cloud SaaS in single regions or multi-regions; the EU Data Boundary covers BigQuery and Gemini Enterprise.
Pricing: BigQuery: $6.25 per TiB on demand, first TiB free each month, or slot-based editions. Data agents: $3 per million input tokens and $20 per million output tokens, plus query costs. Agent Runtime bills per vCPU-hour.
Key features
- End-user credentials for agent queries
- Table-read audit logs that can’t be disabled
- Row, column, and masking policies in the engine
- Agent Identity with both identities in the logs
Best for
- BigQuery-centric organizations putting agents on governed tables
- Security teams that want agent identity separate from user identity
- Teams building agents with ADK or LangGraph on Google Cloud
Where it falls short: Replay is the shortest on this list: seven days at most, and restricted on tables with row-level policies. Fine-grained controls need an Enterprise edition, MCP-layer logs stay off until you turn them on, and document access controls in Agent Search are still preview.
Verified: October 2026
AWS (Bedrock AgentCore + Lake Formation)
The builder’s toolkit: any framework, any model, with identity, gateway, policy, and tracing as managed parts.
AWS doesn’t sell one agent data platform; it sells the parts. Bedrock AgentCore (GA October 2025) provides runtime, identity, a tool gateway, policy, memory, and observability. Lake Formation governs the data underneath. You connect the two.
Policy at the data: Lake Formation data filters give row-, column-, and cell-level security on reads. AgentCore Identity supports on-behalf-of token exchange, so a token carries both the agent’s and the user’s identity. AgentCore Policy (GA March 2026) evaluates Cedar rules on every tool call through the Gateway. It governs tool calls, not rows.
Audit of agent reads: CloudTrail logs Lake Formation API calls and each time data credentials are vended. AgentCore Observability sends OpenTelemetry traces and spans to CloudWatch. Model invocation logging captures full prompts and responses, but it’s off by default.
Point-in-time replay: Athena runs Iceberg time travel by timestamp or snapshot. S3 Tables expire snapshots older than 120 hours by default, so replay depth is about five days unless you change it.
Agent interfaces: AgentCore Gateway turns OpenAPI specs, Lambda functions, and existing MCP servers into MCP tools. AgentCore Runtime runs LangGraph, Strands, CrewAI, or custom agents with any model, and supports MCP and A2A.
Structured and unstructured: Bedrock Knowledge Bases cover documents through managed connectors (S3, SharePoint, Confluence, Google Drive) and tables through natural-language-to-SQL on Redshift and the Glue Data Catalog. Athena Federated Query reads other sources in place.
Deployment: AWS commercial regions plus GovCloud (US-West), where AgentCore became generally available in May 2026; some Policy features aren’t available in GovCloud.
Pricing: Consumption-based per AgentCore component, with no minimums: Gateway at $0.005 per 1,000 invocations and Policy at $0.000025 per authorization request, for example. Data services bill separately.
Key features
- Framework- and model-agnostic agent runtime
- Cedar policy on every tool call, GA March 2026
- Row, column, and cell filters in Lake Formation
- On-behalf-of tokens carrying agent and user identity
Best for
- AWS-native teams that want to build, not buy, the agent stack
- Builders mixing frameworks and models
- Organizations already governing S3 and Iceberg data with Lake Formation
Where it falls short: Two policy planes. Cedar governs tool calls at the Gateway; Lake Formation governs rows. Tying an agent’s end user to the right Lake Formation permissions is your work. AWS’s own guidance calls Knowledge Base metadata filtering application-layer enforcement, and its docs describe managed Knowledge Base ACL awareness as filtering, not authorization.
Verified: October 2026
Fluree
The enterprise AI context layer: a governed knowledge graph that enforces policy on every fact before data reaches a model.
Disclosure first: Fluree is our product, and this is our page. Every competitor claim above and below links to that vendor’s own docs, and each profile says where the rival wins. Fluree isn’t a warehouse and doesn’t ask you to replace one. It’s one product, Fluree AI, the context layer for enterprise AI, with three capabilities: taxonomy and ontology management, entity resolution, and document extraction. FlureeDB, the verifiable knowledge graph database engine underneath, is where policy runs.
The knowledge model matters for accuracy, too. In Fluree’s 2024 study, GraphRAG for GenAI Accuracy, answers over raw relational tables scored 8–15% zero-shot; a governed knowledge graph reached 95%+ once enriched and joined. How the database governs agents is covered in Fluree’s governed AI agent database.
Policy at the data: Policy is enforced inside the database on every fact, before any data reaches a model, so a prompt can’t widen what an agent may read. Details on the security page.
Audit of agent reads: Every write lands as a signed commit, so the facts behind an answer carry a verifiable history. Combined with time travel, you can reconstruct the graph an agent queried.
Point-in-time replay: Time travel is built in: query the graph as of any past commit, so an agent’s answer can be replayed against the facts that existed when it answered.
Agent interfaces: A bundled MCP server, plus SPARQL, openCypher, GraphQL, and JSON-LD on one engine. See the platform overview.
Structured and unstructured: Lakehouse tables (Iceberg, Delta) and SQL sources (through Trino/Starburst or a SQL bridge) are read where they live; only Iceberg can keep an optional synced copy. Documents become graph facts under the same policy. There’s no change data capture, and SaaS connectors exist only in hosted Fluree AI.
Deployment: Hosted as Fluree AI, free to start, or run FlureeDB yourself. FlureeDB is source-available under the Business Source License 1.1: free in production, including inside your own product; a commercial license is needed only to offer it as a hosted database service. Each release converts to Apache 2.0 on its fourth anniversary.
Pricing: Fluree AI is free to start. FlureeDB is source-available under BUSL 1.1. Taxonomy and ontology management, entity resolution, and document extraction are priced for enterprise deployments; see pricing or book a demo.
Key features
- Policy enforced on every fact, inside the database
- Time travel: query the graph as of any past commit
- Signed commits on every write
- Bundled MCP server; SPARQL, openCypher, GraphQL, JSON-LD
Best for
- Regulated teams that must replay what an agent could see
- Agents that need tables and documents under one policy model
- Stacks that keep their lakehouse and add a governed context layer across it
Where it falls short: Fluree isn’t a warehouse, a BI tool, or a SaaS search index. There’s no change data capture, SQL sources need Trino/Starburst or a SQL bridge, only Iceberg can keep a synced copy, and SaaS connectors live only in hosted Fluree AI. The real investment is a knowledge model of your business, and that model is what lets agents answer accurately.
Verified: October 2026
Palantir AIP
The ontology-driven operating platform, where agents read, act, and write back under object-level security.
Palantir’s agents work on the Ontology: objects, links, and actions modeled inside Foundry. AIP Logic, AIP Chatbot Studio (renamed from Agent Studio in April 2026), and pro-code agents built with the Claude, OpenAI, or Google agent SDKs all reach data through it.
Policy at the data: Object and property security policies give row-, column-, and cell-level read security on ontology objects, and markings are mandatory controls that propagate to derived data. AIP Logic runs with the invoking user’s permissions by default, and Ontology MCP tokens can be scoped to the user’s permissions.
Audit of agent reads: Audit logs record data loaded to be returned to a user, for delivery to a SIEM or a Foundry dataset. AIP observability traces prompts, tokens, and errors across 30 days of execution history; user-scoped Logic logs are kept 24 hours.
Point-in-time replay: Dataset Compare diffs a dataset against an older transaction, and dataset Time Travel entered Beta in July 2026. Object edit history records every edit once it’s turned on for an object type, but not edits made before.
Agent interfaces: Ontology MCP (GA June 2026) lets agents read objects, run actions, and run queries; Palantir MCP serves builders. Pro-code agent templates cover the Claude Agent SDK, OpenAI Agents SDK, and Google ADK.
Structured and unstructured: Connectors for warehouses, databases, ERP, CRM, and streaming; virtual tables query Snowflake, Databricks, and BigQuery without copying. Media sets handle documents, imagery, audio, and video.
Deployment: Rubix deploys AIP and Foundry on AWS, Azure, Google Cloud, Oracle Cloud, or on-premises, meeting FedRAMP High and DoD IL5 and IL6.
Pricing: Sales-led and negotiated. An AIP Developer Tier trial account is available for building.
Key features
- Row, column, and cell security on ontology objects
- Agents that act and write back through governed actions
- Ontology MCP for outside agent frameworks
- Classified and on-premises deployment
Best for
- Operational workflows where agents must act, not just answer
- Defense, government, and industrial organizations
- Enterprises ready to model operations into one ontology
Where it falls short: Agents reach only what has been modeled into the Ontology inside Foundry, so the value depends on a sizable integration and modeling program. Point-in-time features are newer and uneven: dataset time travel is Beta, edit history is opt-in per object type, and some execution logs last 24 hours.
Verified: October 2026
Glean
Permission-aware search and agents across 275+ work apps.
Glean indexes the apps where work happens, mirrors each source’s permissions, and puts search, an assistant, and agents on top. For the direct matchup, see Fluree vs Glean.
Policy at the data: Connectors ingest each source’s ACLs, groups, and roles, and agents run with the identity and permissions of the user who triggered the run. Agent Identity, listed as GA in the October 2026 release notes, lets an agent run on scoped service credentials instead.
Audit of agent reads: Customer event logs record each agent run’s steps, tools used, and citations; MCP activity logs record tool use by server, user, and client. Glean’s deployment docs list auditing of all agent access and tool history, and, as a privacy choice, no logging of individual user queries or responses.
Point-in-time replay: Not publicly documented. The index tracks the current state of each source, and deleted content drops out as connectors re-crawl.
Agent interfaces: A remote MCP server that is tenant-specific and permission-aware, an MCP Gateway for governing other MCP tools, Agent Builder, and a Platform Agents API.
Structured and unstructured: 275+ connectors, mostly documents and SaaS apps. Warehouse data is reached through live tools: Snowflake tools query with SQL or Cortex under a chosen or default role, while the Databricks connector indexes dashboards and metadata, not table rows.
Deployment: Glean Hosted SaaS, or Customer Hosted as a managed service in your GCP or AWS account. Glean describes Customer Hosted as equivalent to hosted SaaS, not traditional self-hosting.
Pricing: No public price list. The pricing model is per-user seats plus pooled FlexCredits that agent runs consume.
Key features
- 275+ connectors with mirrored source permissions
- Agents run as the user who triggered them
- Remote MCP server and MCP Gateway
- Agent run logs with tools used and citations
Best for
- Enterprises whose agent questions live in documents and SaaS apps
- Putting governed search and agents in front of every employee
- Teams that want one permission-aware MCP endpoint for work content
Where it falls short: Glean is an index, not a database of record. It has no documented point-in-time history, warehouse tables are reached through live tool calls rather than governed in the index, and permission changes and deletions apply as fast as connectors crawl.
Verified: October 2026
How to narrow your shortlist
Find the situation that sounds like yours. Each one resolves to a single recommendation.
“Our governed data already lives in Snowflake, and we want agents on it now.”
Snowflake
Agent-aware policies, ACCESS_HISTORY, and a GA managed MCP server, inside the platform you already run.
“We’re lakehouse-first and want lineage from table to agent call.”
Databricks
Unity Catalog governs data, indexes, and MCP tools together, with column-level lineage in system tables.
“We’re a Microsoft shop, and our users live in Teams and Copilot.”
Microsoft Fabric
OneLake security, data agents that run as the user, and Purview audit, reaching Copilot Studio and Foundry.
“Agents must query as the end user, and audit logs can’t be optional.”
Google Cloud (BigQuery)
End-user credentials for agent queries and table-read logs that can’t be switched off.
“We want to build our own agent stack on AWS, with any framework and any model.”
AWS (Bedrock AgentCore)
Managed runtime, identity, gateway, and Cedar policy, with Lake Formation governing the rows.
“Auditors need to replay what an agent could see, across tables and documents.”
Fluree
Policy on every fact inside the database, and a graph you can query as of any past commit, over the lakehouse you keep.
“Our agents need to take actions in operational systems, not just answer.”
Palantir AIP
Ontology actions with object-level security, and deployment options that reach classified environments.
“Most of our agent questions are about documents and SaaS apps.”
Glean
275+ connectors with mirrored permissions and a permission-aware MCP server.
Frequently asked questions
The questions buyers actually ask when evaluating this category.
Compare
See every Fluree comparison in one place.
Toplists, head-to-head guides, and alternatives — all linked from the comparison hub.
Put your knowledge to work.
Updated