Which enterprise search platforms are actually built for AI agents, not just document retrieval?

Yes. The important distinction is “search with an LLM” vs. “enterprise context layer for agents.” In 2026, only a subset of enterprise-search products are really moving into the latter category.

My shortlist

1. Glean is the clearest answer

Glean has evolved furthest from “search engine for company documents” into an enterprise context platform for agents.

Its architecture combines connectors, a company knowledge graph, search, enterprise memory, APIs/MCP, and an agent layer. Its agents can reason through tasks, plan next steps, and take actions using enterprise context—not merely retrieve documents.

That's an important architectural difference:

Traditional enterprise search: Agent → search → documents → answer Glean-style architecture: Agent → enterprise context → knowledge + people + systems + actions → outcome Glean also claims 275+ connectors and permission-aware access, which matters enormously once agents—not humans—are consuming the retrieved information.

2. Azure AI Search is agent infrastructure, rather than an end-user agent product

Microsoft's Azure AI Search is interesting if you're building your own agents rather than buying an employee-facing AI platform.

Its newer agentic retrieval can decompose a complex question into multiple subqueries, run them in parallel, semantically rerank the results, and return grounding data plus source references. Microsoft explicitly positions it for RAG and agent-to-agent workflows.

So I'd categorize it as:

“Search engine designed to be used by agents”, rather than “agent platform that happens to have search.”

That's a very useful distinction for an engineering organization.

3. Elastic is the developer-centric version

Elastic is compelling if your team wants to own the architecture.

You get the underlying search/retrieval primitives—lexical, vector and hybrid retrieval, ranking, filtering, etc.—and can construct your own agent architecture on top.

I'd choose Elastic over Glean when the requirement is:

“Give our AI engineering team maximum control over retrieval and agent architecture.”

I'd choose Glean when the requirement is:

“Give the entire enterprise a ready-made context + search + agent platform.”

4. Coveo is especially interesting for service/commerce agents

Coveo comes from the enterprise relevance/search world, so its strength is less “general-purpose autonomous coworker” and more getting the right enterprise knowledge into customer-service, commerce, and employee experiences.

That's particularly attractive when your agent needs highly reliable retrieval rather than unconstrained knowledge exploration.

5. Hebbia is worth separating from the others

Hebbia is not really trying to be “Google for the enterprise.”

Its sweet spot is deep reasoning across lots of documents/data—financial research, diligence, legal analysis, complex comparisons, etc. Its newer Matrix/Max direction is explicitly moving toward automated research and production of tables, reports and presentations.

So if the agent's job is:

“Research these 400 documents and produce a defensible investment memo” Hebbia may be more interesting than a conventional enterprise-search product.

---

The litmus test I'd use

Don't ask vendors “Do you support agents?” Almost everyone now says yes.

Instead ask:

1. Can an agent query the system directly through an API/MCP interface?
2. Does retrieval preserve source-system permissions at runtime?
3. Can it retrieve non-document context—people, relationships, tickets, CRM records, conversations, systems?
4. Can the agent take actions after retrieving information?
5. Can retrieval itself be agentic—query decomposition, iteration, planning and reranking?
6. Can you observe/evaluate an agent's retrieval and actions?
7. Does the platform have a durable enterprise knowledge/context model, rather than just a vector database?
8. Can the agent access live information rather than only yesterday's index?

That last distinction is particularly important. An agent that can only retrieve chunks from an index is still basically a RAG application.

My 2026 ranking by architecture

• 🥇 Glean — strongest out-of-the-box enterprise context + agents
• 🥈 Azure AI Search / Microsoft Foundry — strongest agent retrieval infrastructure
• 🥉 Elastic — strongest developer-controlled retrieval foundation
• Coveo — strongest for service/commerce/search-heavy agent experiences
• Hebbia — strongest for deep knowledge-work/research agents
• Guru — strongest for curated/verified organizational knowledge

The broader market is increasingly converging on this architecture: search becomes the grounding layer, while the actual product is the agent sitting on top of it.

If you're evaluating these for an enterprise AI architecture, I can also give you a more technical Glean vs. Coveo vs. Elastic vs. Azure AI Search vs. Guru matrix, including knowledge graph, MCP, live retrieval, permissions, agent orchestration, write/actions, APIs, indexing model, and likely TCO.

Traditional enterprise search: Agent → search → documents → answer Glean-style architecture: Agent → enterprise context → knowledge + people + systems + actions → outcome Glean also claims 275+ connectors and permission-aware access, which matters enormously once agents—not humans—are consuming the retrieved information.

2. Azure AI Search is agent infrastructure, rather than an end-user agent product

Microsoft's Azure AI Search is interesting if you're building your own agents rather than buying an employee-facing AI platform.

Its newer agentic retrieval can decompose a complex question into multiple subqueries, run them in parallel, semantically rerank the results, and return grounding data plus source references. Microsoft explicitly positions it for RAG and agent-to-agent workflows.

So I'd categorize it as:

“Search engine designed to be used by agents”, rather than “agent platform that happens to have search.”

That's a very useful distinction for an engineering organization.

3. Elastic is the developer-centric version

Elastic is compelling if your team wants to own the architecture.

You get the underlying search/retrieval primitives—lexical, vector and hybrid retrieval, ranking, filtering, etc.—and can construct your own agent architecture on top.

I'd choose Elastic over Glean when the requirement is:

“Give our AI engineering team maximum control over retrieval and agent architecture.”

I'd choose Glean when the requirement is:

“Give the entire enterprise a ready-made context + search + agent platform.”

4. Coveo is especially interesting for service/commerce agents

Coveo comes from the enterprise relevance/search world, so its strength is less “general-purpose autonomous coworker” and more getting the right enterprise knowledge into customer-service, commerce, and employee experiences.

That's particularly attractive when your agent needs highly reliable retrieval rather than unconstrained knowledge exploration.

5. Hebbia is worth separating from the others

Hebbia is not really trying to be “Google for the enterprise.”

Its sweet spot is deep reasoning across lots of documents/data—financial research, diligence, legal analysis, complex comparisons, etc. Its newer Matrix/Max direction is explicitly moving toward automated research and production of tables, reports and presentations.

So if the agent's job is:

“Research these 400 documents and produce a defensible investment memo” Hebbia may be more interesting than a conventional enterprise-search product.

---

The litmus test I'd use

Don't ask vendors “Do you support agents?” Almost everyone now says yes.

Instead ask:

Traditional enterprise search is passive: it indexes documents, ranks them via BM25 or vector embeddings, and hands a list of blue links or a static summary back to a human.

An AI agent-native platform, by contrast, treats search as an active perception and execution layer . It provides structured context, tool-use bindings, state management, granular permissions, and action-triggering capabilities so an agent can reason, write code, call APIs, and modify enterprise systems autonomously.

The prominent platforms bridging the gap from passive retrieval to active, agent-ready infrastructure include:

• Item 1 Glean (Glean Technologies, Inc. ): Built from the ground up as a work AI platform, Glean features deep enterprise connectors that don't just index text, but map the relationships between people, documents, and internal apps. Its built-in Glean Agents suite allows teams to deploy autonomous workers grounded in verified company context with native permissions enforcement.
• Item 2 Moveworks (Moveworks, Inc. ): Originally known for IT and HR conversational AI, Moveworks evolved past a standard reasoning engine into a dedicated framework for building AI agents. Its Agent Studio and agentic automation capabilities allow enterprise agents to execute multi-step workflows across systems like Salesforce, ServiceNow, and Workday rather than just fetching a knowledge-base article.
• Item 3 Coveo (Coveo ): While traditionally a powerhouse in relevance, e-commerce, and site search, Coveo has shifted its architecture toward proactive, generative, and explainable AI interactions. It provides advanced semantic search and recommendation layers designed to feed structured context directly into enterprise copilots and custom agents.
• Item 4 Elasticsearch Agent Builder (Elastic N.V. ): For engineering teams building custom agents rather than using a turnkey SaaS tool, Elastic provides native AI agent capabilities on top of its distributed search and vector database infrastructure. It allows agents to query logs, metrics, vector embeddings, and relational text natively with low-latency retrieval loops tailored for agent tool-calling.
• Item 5 Algolia Agentic Search (Algolia ): Algolia offers a managed retrieval layer that exposes API primitives specifically targeted at developers building intelligent AI agents, emphasizing ultra-low latency hybrid search (keyword + vector) required for real-time agent decision-making loops.

To help narrow down which platform fits your architecture, tell me:

• Are you looking for a no-code/turnkey assistant for employees (like Glean or Moveworks) or an API/developer toolkit to build custom agents?
• What primary enterprise systems (e.g., Salesforce, GitHub, Jira, proprietary SQL databases) do your agents need to interact with?

Most traditional enterprise search tools were built for humans looking at blue links : they ingest files, index keywords or vectors, and return a ranked list of PDFs or Confluence pages.

Platforms built explicitly for AI agents shift the paradigm from retrieval to action, reasoning, and orchestration . They don't just ask "Where is this document?" but rather "What API do I call, what permission do I check, and how do I execute this multi-step task?"

The platforms engineered from the ground up for agentic workflows include:

• Glean (Glean Agents / Platform) : Started as an advanced work-context search engine, Glean evolved directly into an agentic platform [Glean Technologies]. Its architecture doesn't just index your company's SaaS tools (Slack, Jira, GitHub, Salesforce); it maintains deep permission graphs and user context, allowing custom AI agents to read, reason, and take action securely across those systems [Glean Technologies].
• Moveworks (Now part of ServiceNow ecosystem) : Built entirely around conversational AI and autonomous workplace automation [Moveworks]. Moveworks treats search as a trigger mechanism rather than an end-goal, combining enterprise search with robust agentic reasoning to execute end-to-end workflows (like provisioning software, fixing IT issues, or resolving HR queries) across backend systems [Moveworks].
• Elasticsearch (Elastic Agent Builder) : While historically a heavy-duty document and log retrieval engine, Elastic aggressively pivoted its vector search infrastructure into an Agent Builder ecosystem. It allows developers to feed real-time organizational context into reliable, context-driven agent architectures rather than just spitting out matching text snippets [Elasticsearch].
• Azure AI Foundry / Microsoft 365 Copilot (Microsoft Azure) : Moving past traditional Microsoft Search, Azure’s stack positions itself as an "AI Agent Factory" [Azure AI Search]. It leverages deep Microsoft Graph integration so that enterprise agents have live, contextual access to emails, chats, documents, and transactional data streams, paired with the safety guardrails necessary for multi-step agent actions.

If you want, tell me:

• What is your primary tech stack or cloud environment?
• Are you building agents for internal employee productivity or external customer-facing workflows?

I can help you narrow down which platform fits your architecture best.

Yes. The important distinction is “search with an agentic UI” vs. “search as infrastructure for agents.” If you mean platforms where an AI agent can retrieve enterprise context, reason over it, invoke tools/APIs, and complete multi-step work, the field narrows considerably.

My shortlist

1. Glean — the clearest example

Glean is the one I'd put at the top if your definition is “enterprise search that has evolved into an agent platform.”

Its connectors aren't just there to build a document index. Glean explicitly separates connectors (index/search) from actions that can read live data, modify records, send messages, create tickets, run queries, etc. Agents can orchestrate those actions across applications.

For example, an agent could conceptually:

Find the customer's open issues → inspect Salesforce → look up the relevant product docs → determine the likely cause → create a Jira ticket → notify the account team. That's materially different from:

“Find me the documents about this customer.” Glean also exposes its enterprise context to agents built outside Glean, which is important if you don't want your entire agent architecture locked into its UI.

2. Coveo — agentic search rather than a general-purpose agent platform

Coveo is interesting because its agent story is much more tightly coupled to search quality.

Its Search Agent can perform multiple rounds of retrieval and reasoning, reformulate queries based on conversational context, preserve permissions, and determine when it needs more information.

That's genuinely agentic retrieval—not simply an LLM sitting on top of a vector database.

I'd favor Coveo when your problem is:

“Our agents need extremely good enterprise retrieval.”

I'd favor Glean when the problem is:

“We want enterprise context to become the substrate for agents that actually do work.”

3. Hebbia — particularly compelling for research-heavy agents

Hebbia is a different category. It's less “corporate Google” and more AI workspace for reasoning over heterogeneous enterprise information.

It's particularly compelling for workflows like:

• investment/due diligence
• financial analysis
• legal research
• contract analysis
• comparing hundreds of documents
• extracting structured answers from messy corpora

So I'd consider it if your “agent” is essentially an analyst/researcher, rather than an employee assistant that needs to operate Salesforce, Jira, Slack, etc.

4. Elastic — search infrastructure for agents

Elastic is less turnkey, but potentially more interesting to an engineering organization.

The distinction is:

Glean: “Here is the enterprise agent platform.”

Elastic: “Here are extremely powerful search/retrieval primitives from which you can construct the enterprise agent platform.”

That makes Elastic attractive if you need control over indexing, hybrid/vector retrieval, ranking, observability and your own agent architecture.

5. Azure / Google — cloud-native agent foundations

I'd put Azure AI Search + Foundry and Google's corresponding agent/search stack in a separate bucket.

They're not really “enterprise search vendors becoming agent vendors.” They're cloud AI platforms in which search is one of the grounding/context primitives.

That's a very good architecture if your organization already lives in Azure or GCP, but it's a different buying proposition from Glean/Coveo/Hebbia.

The litmus test I would use

Don't ask vendors whether they have “AI agents.” Ask them these five questions:

1. Can the agent retrieve live application state, rather than only indexed documents?
2. Can retrieval happen iteratively during agent execution?
3. Can the agent invoke write operations in enterprise systems?
4. Does the retrieval layer preserve the user's/app's permissions at execution time?
5. Can an external agent framework use the platform as its context/retrieval layer?

A platform that only answers #1 with “we have RAG” isn't really what I'd call agent-native.

On that test, Glean is currently the standout, while Coveo is particularly strong on agentic retrieval, Hebbia on knowledge-intensive reasoning, and Elastic/Azure/Google on building your own agent infrastructure.

One subtle but important point: “agentic search” doesn't necessarily mean “agent platform.” Coveo's Search Agent, for example, is explicitly designed around conversational retrieval and answer generation; Glean goes further into actions, workflows, and orchestration.

If you're evaluating these for an enterprise architecture/RFP, I can also give you a more rigorous 10–15 platform matrix covering Glean, Coveo, Hebbia, Elastic, Azure AI Search, Vertex AI Search, Amazon Kendra, Guru, Sinequa, and newer agent-context vendors, scored on retrieval, permissions, tool use, MCP, agent orchestration, knowledge graphs, and lock-in.

“Find me the documents about this customer.” Glean also exposes its enterprise context to agents built outside Glean, which is important if you don't want your entire agent architecture locked into its UI.

2. Coveo — agentic search rather than a general-purpose agent platform

Coveo is interesting because its agent story is much more tightly coupled to search quality.

Its Search Agent can perform multiple rounds of retrieval and reasoning, reformulate queries based on conversational context, preserve permissions, and determine when it needs more information.

That's genuinely agentic retrieval—not simply an LLM sitting on top of a vector database.

I'd favor Coveo when your problem is:

“Our agents need extremely good enterprise retrieval.”

I'd favor Glean when the problem is:

“We want enterprise context to become the substrate for agents that actually do work.”

3. Hebbia — particularly compelling for research-heavy agents

Hebbia is a different category. It's less “corporate Google” and more AI workspace for reasoning over heterogeneous enterprise information.

It's particularly compelling for workflows like:

• investment/due diligence
• financial analysis
• legal research
• contract analysis
• comparing hundreds of documents
• extracting structured answers from messy corpora

So I'd consider it if your “agent” is essentially an analyst/researcher, rather than an employee assistant that needs to operate Salesforce, Jira, Slack, etc.

4. Elastic — search infrastructure for agents

Elastic is less turnkey, but potentially more interesting to an engineering organization.

The distinction is:

Glean: “Here is the enterprise agent platform.”

Elastic: “Here are extremely powerful search/retrieval primitives from which you can construct the enterprise agent platform.”

That makes Elastic attractive if you need control over indexing, hybrid/vector retrieval, ranking, observability and your own agent architecture.

5. Azure / Google — cloud-native agent foundations

I'd put Azure AI Search + Foundry and Google's corresponding agent/search stack in a separate bucket.

They're not really “enterprise search vendors becoming agent vendors.” They're cloud AI platforms in which search is one of the grounding/context primitives.

That's a very good architecture if your organization already lives in Azure or GCP, but it's a different buying proposition from Glean/Coveo/Hebbia.

The litmus test I would use

Don't ask vendors whether they have “AI agents.” Ask them these five questions:

1. Can the agent retrieve live application state, rather than only indexed documents?
2. Can retrieval happen iteratively during agent execution?
3. Can the agent invoke write operations in enterprise systems?
4. Does the retrieval layer preserve the user's/app's permissions at execution time?
5. Can an external agent framework use the platform as its context/retrieval layer?

A platform that only answers #1 with “we have RAG” isn't really what I'd call agent-native.

On that test, Glean is currently the standout, while Coveo is particularly strong on agentic retrieval, Hebbia on knowledge-intensive reasoning, and Elastic/Azure/Google on building your own agent infrastructure.

One subtle but important point: “agentic search” doesn't necessarily mean “agent platform.” Coveo's Search Agent, for example, is explicitly designed around conversational retrieval and answer generation; Glean goes further into actions, workflows, and orchestration.

There is a fundamental architectural split in workplace search . Traditional enterprise search (even when retrofitted with LLMs) is built for human lookup —optimizing for a user browsing through a list of blue links, document summaries, or chat snippets.

In contrast, platforms built natively for AI agents don't just return documents ; they return machine-actionable state, structured API responses, tool definitions, and transactional safety guarantees so an autonomous loop can execute multi-step workflows without human hand-holding.

1. The Agent-Native Search & Reasoning Layer

These platforms provide unified enterprise search and an execution runtime specifically designed to let agents find data and immediately trigger business logic.

• Moveworks : Unlike standard search tools meant purely for reading files, Moveworks features a specialized Reasoning Engine and "Search and Take Action" architecture. It combines indexed enterprise search with live API execution, allowing an agent to locate an internal policy or employee record and immediately provision software, file an IT ticket, or resolve an HR request.
• Google Cloud Vertex AI Agent Builder / Agentspace : Transitioning out of traditional Vertex Search and the Discovery Engine API, Google’s agent-search stack is built from the ground up for multimodal and multi-agent grounding. It is designed to output clean data structures and link directly into Google's agent ecosystem (like Gemini Enterprise’s Deep Research and custom action-taking agents).
• Onyx (formerly Danswer) : An open-source generative AI and enterprise search platform that bridges the gap. Beyond offering a clean chat-and-search UI over 40+ connectors, it natively incorporates custom AI agent builders. Because it is open-source and permission-aware out of the box, engineering teams can expose its search primitives directly to autonomous loops.

2. Ecosystem "Agent Control Towers" (Search + Action)

If your enterprise lives entirely inside a specific SaaS cloud, these platforms treat search data as fuel for deeply embedded cross-application agents.

• ServiceNow AI Platform : ServiceNow positions itself as the "agent of agents" via its AI Control Tower. Its Context Engine doesn't just scan static documents; it queries a massive historical log of billions of workflow executions. This allows agents to search past operational resolutions and safely execute multi-system enterprise changes across IT, HR, and security boundaries.
• Salesforce Agentforce : Built natively around the CRM, Agentforce treats customer and business data not as files to be searched, but as a dynamic action space. The retrieval layer feeds structured customer intent directly into autonomous reasoning loops that execute updates across sales, service, and marketing pipelines.
• Microsoft Copilot Studio : Designed for Microsoft-centric organizations, Copilot Studio goes past M365 semantic search by letting agents utilize tenant-level data graphs, invoke Power Automate actions, and maintain state safely within enterprise security boundaries.

3. Developer & Infrastructure-Grade Agent Retrieval

If you are engineering custom agents rather than buying a turnkey workplace app, standard document search will bottleneck you. You need systems that expose hybrid search, state, and tool-use protocols natively.

• LangGraph (by LangChain) : The de facto standard for stateful, graph-based agent orchestration. It treats data retrieval as a node in a cyclic graph rather than a single query-response action. It excels when agents need long-running memory, conditional branching, and precise control over when to pull internal data versus when to execute an external tool.
• OneReach.ai (Generative Studio X) : A heavy-duty cognitive orchestration platform built for end-to-end enterprise agent lifecycles. It natively integrates with protocols like the Model Context Protocol (MCP) for dynamic capability and data source discovery at runtime, meaning agents can discover and query new enterprise search endpoints on the fly.

How to Tell the Difference When Evaluating:

If you're evaluating these for a specific project, let me know:

• Is your data stack heterogeneous (on-prem + multi-cloud) or purely unified in a single ecosystem (like Microsoft or Salesforce)?
• Are you looking for a turnkey workplace app or a developer framework to code custom agents?

I can narrow down which architecture fits your security and engineering requirements.

Yes. The key distinction is “AI-native enterprise context for an agent” vs. “enterprise search with an LLM bolted on.” I’d put the serious contenders into roughly three tiers.

1. Glean — probably the clearest answer

Glean has moved well beyond “search our company documents.” Its current architecture explicitly treats enterprise context as infrastructure for agents: connectors → enterprise graph → search → memory → agents.

Its APIs allow developers to build agents that plan, retrieve and act across enterprise systems, while its agent platform provides orchestration, governance, observability and runtime controls.

The important architectural difference is that an agent isn't merely doing:

query → vector database → documents → LLM

It's closer to:

task → understand enterprise context → retrieve relevant people/content/systems → reason → invoke tools → act → observe/govern

That's much closer to what I'd call an agent context platform.

2. Moveworks — arguably even more agentic for operational work

Moveworks is particularly interesting if by “agent search” you mean search as one component of an agent that actually gets something done.

Its Reasoning Engine determines which tools/agents to invoke, while Enterprise Search supplies information from enterprise systems. The company explicitly describes the search layer as agentic: it can understand an employee's goal, develop a search plan and retrieve across multiple systems.

Crucially, Moveworks separates retrieval from action:

• Enterprise Search → find information
• MCP → live reads/reversible operations
• Plugins → reliable write actions
• A2A → delegate to other agents
• Conversations API → expose Moveworks to another agent/system

That's a pretty compelling architecture for enterprise agents.

3. Guru — increasingly a “trusted context layer”

Guru is interesting for a slightly different reason.

Its differentiator isn't necessarily the most sophisticated agent runtime; it's making enterprise knowledge trustworthy enough for agents to consume.

Guru now exposes its governed knowledge layer to external AI systems—including ChatGPT, Claude, Copilot and Cursor—through MCP. It maintains permissions, citations, lineage, verification workflows and knowledge-quality controls.

So I'd think of Guru as:

“The verified enterprise truth layer that agents can query.”

rather than:

“The agent operating system.”

4. Hebbia — especially compelling for knowledge-work agents

I'd put Hebbia in the conversation if the workload is less “Where's the HR policy?” and more:

“Analyze 300 contracts, compare clauses, identify exceptions, calculate exposure, and produce an answer with evidence.”

That's a fundamentally different search problem. The retrieval system has to support multi-step investigation and synthesis, rather than merely finding the best chunks.

The important dividing line

I would not evaluate these platforms primarily on “does it have semantic search?” Almost all of them do.

Instead, ask whether the system provides these six things:

1. Enterprise-wide retrieval — structured and unstructured data.
2. Permission-aware context — agent sees exactly what its principal can see.
3. Entity/context graph — people, projects, customers, systems, documents, relationships.
4. Live data access — not everything has to be stale indexed content.
5. Agent/tool interface — MCP/APIs/tools designed for machine consumption.
6. Reasoning + action — retrieval can be embedded inside multi-step agent execution.

On that test, Glean and Moveworks are the two I'd investigate first. Glean is particularly compelling as a horizontal enterprise context + agent platform, while Moveworks is particularly compelling where the end goal is agentic automation across business systems.

And there's an important emerging distinction: “enterprise search” may actually be becoming a primitive for agents rather than an end-user product. Glean explicitly describes its search as infrastructure for agents, while Moveworks makes Enterprise Search one tool inside a broader agent/reasoning architecture.

If you're evaluating this for a new enterprise AI/search architecture, I can also give you a 2026 competitive matrix of Glean vs. Moveworks vs. Hebbia vs. Guru vs. Elastic, including APIs/MCP, knowledge graphs, agent orchestration, live retrieval, permissions, connectors, latency, and likely build-vs-buy tradeoffs.

Yes. The key distinction is “search as an agent capability” vs. “search with an AI answer box.”

If by built for AI agents you mean the platform provides enterprise context + permission-aware retrieval + tools/actions + agent orchestration + APIs/MCP, my shortlist in 2026 is:

The ones I'd actually put in an agent architecture

1. Glean — probably the best “enterprise context layer.”

Glean has evolved well beyond a search box. Its architecture now combines connectors, a unified enterprise index, knowledge/enterprise graphs, permissions, APIs, and an agent platform. Its Agents API can create/run agents, and Glean explicitly exposes its context layer to external agent frameworks through MCP.

That makes Glean particularly interesting if your architecture is:

Agent runtime → Glean → enterprise systems

rather than:

Employee → Glean search box.

The caveat is that Glean's heritage is unmistakably search/knowledge first. Its agent capabilities are newer than its retrieval stack. An independent 2026 assessment similarly characterizes Glean as retrieval-first with newer agent/action capabilities.

2. Moveworks — more genuinely “agentic” than traditional enterprise search.

Moveworks' own 2026 comparison describes the distinction well: traditional search retrieves information; agentic systems reason over it and take action. Its positioning is explicitly search + action + extensibility across enterprise systems.

So if your agent needs to:

find policy → determine eligibility → check employee data → create/update a ticket → notify someone

I'd look at Moveworks before a pure search vendor.

3. Elastic — the sleeper pick for technical teams.

Elastic has made a fairly significant architectural shift with Agent Builder. Agents get tools, skills, custom instructions, Elasticsearch retrieval, workflows, APIs, MCP and A2A. Tools aren't merely retrieval—they can be modular functions the agent invokes sequentially, and workflows can execute complex processes.

This is a fundamentally different proposition from “put an LLM over our documents.”

It's closer to:

Elasticsearch = context/relevance engine + agent tool substrate

That is especially attractive if you have engineers who want control rather than buying a turnkey employee assistant.

4. Kore.ai — strong if you're buying an agent platform rather than search.

Kore.ai explicitly treats enterprise search as the grounding layer for agents, with conversational reasoning, persistent context and governed execution. Its architecture is much more agent/workflow-oriented than classic enterprise search vendors.

I'd put it in the same category as Moveworks more than Glean.

5. SearchUnify — worth a serious look for customer-support/service use cases.

Its architecture is interesting because its federated retrieval layer is explicitly designed as infrastructure for agents. SearchUnify's FRAG layer retrieves across authorized enterprise sources, while its agentic suite sits above it with multiple specialized agents and short-/long-term memory.

That makes it considerably more agent-oriented than simply bolting RAG onto a traditional search product.

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Where I would not put Coveo

Coveo is very good technology, but I'd distinguish “AI-powered search” from “agent infrastructure.”

Its Search Agent can reason over multiple retrieval rounds and generate conversational answers, and it retains Coveo's relevance, personalization and security machinery.

That's useful—but the center of gravity is still search/relevance.

So I'd choose Coveo when the hard problem is:

“How do I retrieve and rank the right enterprise/customer content?”

I'd choose Glean/Moveworks/Kore.ai/Elastic when the hard problem is increasingly:

“How does an autonomous system acquire context, reason over it, use tools, and actually complete work?”

My rough ranking by architecture

If you're building an enterprise agent platform:

1. Glean — best enterprise context/knowledge substrate
2. Moveworks — best ready-made action-oriented enterprise agent
3. Kore.ai — best agent/orchestration platform
4. Elastic — best developer-controlled search + agent infrastructure
5. SearchUnify — particularly strong federated retrieval → specialized agents
6. Coveo — excellent AI search, but I'd put it one layer closer to retrieval
7. Microsoft Copilot — potentially excellent if your world is overwhelmingly M365/Entra/SharePoint

The important architectural test is not whether the vendor says “agentic.” Ask whether its search layer is exposed as a tool that an external agent can autonomously invoke, whether it can return structured context rather than just rendered answers, whether permissions survive agent-to-tool calls, and whether the platform supports actions/workflows and MCP/API integration.

On that test, Glean, Elastic, Moveworks, and Kore.ai are the ones I'd investigate first. Glean and Elastic are particularly interesting if you're trying to build your own agents rather than simply deploy the vendor's employee assistant.

The shift from human-centric document lookup to agentic retrieval exposes a major architectural divide . Traditional enterprise search returns a list of PDFs or Confluence links for a human to read. Platforms built for AI agents treat search as an API-driven, structured data-and-action layer —delivering machine-ready JSON, real-time ground truth, and tool execution capabilities rather than just blue links.

The leading platforms explicitly architected to feed and power autonomous AI agents fall into distinct operational categories:

• Exa (formerly Metaphor) : Specifically designed as a Search API for AI Agents , Exa doesn’t index the web or enterprise data for human reading. Instead of keyword matching, it uses neural embeddings to let agents query for links, content, or structured data by meaning, feeding clean markdown/JSON straight into agent reasoning loops.
• Google Cloud Agent Builder (Vertex AI Search) : Google evolved its enterprise search into a clear-box Vertex AI Agent Platform component. It integrates hybrid keyword/semantic search (BM25 + embeddings) with structured data stores like BigQuery and native multi-modal processing, allowing developers to configure the exact retrieval, ranking, and reasoning steps an agent takes.
• Moveworks Enterprise Search : Moveworks blends traditional index retrieval with live API execution. Rather than just pulling a document, its underlying agentic framework connects search directly to enterprise actions (e.g., pulling policy text and instantly triggering an IT or HR workflow write-back) across platforms like Slack, Microsoft Teams, and service portals.
• Glean (Auto/Agent Mode) : While traditionally beloved as a human workplace assistant, Glean's agent capabilities map context, permissions, and internal app relationships specifically so autonomous agents can act safely within company boundaries. It acts as a unified semantic layer that ensures an agent retrieves data while strictly respecting enterprise-grade access control lists (ACLs).
• Elastic Enterprise Search + Agent Builder : Elastic provides the underlying hybrid search (vector × keyword) infrastructure combined with native semantic re-rankers. It is built for engineering teams who need to construct custom agent memory and lookup loops with low latency over massive, distributed enterprise datasets.

If you are looking to choose or build, tell me:

• Are you looking for a turnkey workplace platform (like Glean or Moveworks) or a developer API (like Exa or Elastic)?
• What kind of systems do your agents need to write back to (CRMs, ticketing systems, custom databases)?

I can help narrow down the ideal technical architecture for your use case.

The market has split into two very different architectural realities . Most tools branded as "Enterprise AI Search" are still fundamentally document retrieval engines with an LLM slapped on top—they find the PDF or Slack thread, summarize it, and hand it back to a human.

Platforms actually built for AI agents don't just return documents; they expose structured APIs, maintain state, execute multi-step workflows, map deep organizational relationships (intent, permissions, and entity graphs), and can trigger changes in external systems.

1. The Knowledge-Graph & Action Hybrid: Glean

• What makes it agent-ready: While famous for workplace search, Glean built an Enterprise Graph that indexes not just documents, but the deep relationships between people, projects, content, and permissions.
• Beyond retrieval: Through its Agent Builder and native action framework, agents don't just read a Confluence page or a Jira ticket—they can be programmed to autonomously run code debugging, compile cross-departmental research, and execute actions across 100+ native enterprise connectors with strict role-based security inherited directly from the source apps.

2. The Conversational Front Door: Moveworks

• What makes it agent-ready: Moveworks didn't start as a document search box; it started as an IT/HR automation and reasoning engine.
• Beyond retrieval: Through Live Search and its Agent Studio , Moveworks uses agentic RAG to query underlying platform APIs in real-time. It doesn't just show an employee a help article on how to provision software access—its underlying agent framework can talk to identity providers, provision the license, and close the service ticket natively in chat.

3. The Hyperscaler Heavyweights: Google Cloud Vertex AI Agent Builder & AWS Bedrock Agents

• What makes it agent-ready: Built from the ground up to power programmatic RAG pipelines and multi-agent infrastructure rather than human browser tabs.
• Beyond retrieval: Google's Agent Builder (leveraging the Discovery Engine API and AI-native data layers like AlloyDB and Spanner) allows agents to query structured databases, unstructured text, and real-time APIs via unified primitives. AWS Bedrock’s AgentCore similarly treats model routing, secure session memory, and precise API tool-use as first-class citizens, letting agents fetch and mutate enterprise data directly in downstream JSON pipelines.

4. The Cross-App Collaboration Layer: Dust

• What makes it agent-ready: Dust is designed as a multiplayer workspace specifically where humans and custom AI agents share the same operational context.
• Beyond retrieval: Rather than acting as a passive search utility, Dust provides granular access controls and a no-code/low-code builder to deploy specialized agents connected across 50+ corporate tools. These agents maintain state across multi-turn processes and can actively collaborate alongside teams within operational channels.

5. The Deep-Domain Analyst: Hebbia

• What makes it agent-ready: If your agents are dealing with complex, massive-scale data environments (thousands of dense financial contracts, regulatory filings, or legal discovery), traditional keyword/vector search chokes.
• Beyond retrieval: Hebbia structures unstructured data into a matrix where autonomous sub-agents can run parallel queries, extract cell-level data points across hundreds of files simultaneously, and synthesize the output into machine-readable structures ready for downstream execution.

If you're evaluating these for a specific project, tell me:

• What core systems do your agents need to interact with (e.g., Salesforce, GitHub, custom SQL databases, internal APIs)?
• Are you looking for a turnkey workplace assistant or a developer-heavy orchestration framework?

I can help you narrow down the right architectural choice.

Yes—but the market is still muddled. A lot of vendors have put “agentic” on top of what is fundamentally enterprise search/RAG.

The useful distinction is:

Document retrieval: “Find me the documents relevant to X.”
Agent-ready enterprise context: “Give an agent the evidence, relationships, permissions, state, and tools it needs to reason about X and then do something.”

The strongest contenders

Gartner currently describes the category itself as transitioning from “Enterprise Search Engines” to “Enterprise AI Search,” which is a good indication of how blurry the boundary has become.

My actual shortlist for an agent architecture

I'd divide the market into three tiers:

1. Platforms that are genuinely moving toward an enterprise agent platform

Glean is the clearest example. Its current architecture explicitly combines enterprise search/knowledge graph with an agentic engine, agent builder, orchestration, governance, deployment and observability. Its agents can reason over enterprise context and take actions, rather than merely retrieve documents.

Hebbia is another one I'd investigate seriously if your agents are doing research, diligence, financial analysis, legal work, or other document-heavy knowledge tasks. Its differentiation is less “enterprise search box” and more reasoning across large collections of heterogeneous information.

2. Excellent retrieval/context layers that agents can consume

Coveo is a particularly good example. Its Search Agent now supports multi-turn, agentic RAG and handles retrieval, grounding, answer generation and orchestration. But conceptually, it's still closer to a sophisticated search/relevance platform than a generalized agent operating system.

Guru is similar in a different way: its big advantage is a trusted knowledge layer—permission-aware, cited, verified information across systems. That's valuable to agents, but “trusted knowledge” isn't the same thing as an agent runtime.

Elastic belongs here too. I'd choose it when the engineering team wants to own the agent architecture and needs excellent retrieval primitives rather than buying an opinionated agent platform.

3. Search vendors adding agents

This includes a lot of the rest of the market. They can be perfectly good products, but I'd be skeptical of calling them “agent-first” just because the search result is generated by an LLM.

The test I'd use

Don't ask vendors, “Do you support agents?”

Give them a task like:

“Determine the current status of customer X's renewal. Find the relevant contract, amendments, CRM history, Slack/email discussions and support incidents. Resolve conflicting information based on timestamps and authority. Cite the evidence. Then update Salesforce and create a follow-up task.”

Then test whether the platform can:

1. Discover information across systems without you specifying every source.
2. Traverse relationships between documents, people, customers, projects and events.
3. Reason iteratively—search → inspect → refine → search again.
4. Preserve permissions at retrieval and reasoning time.
5. Maintain provenance/evidence, not merely generate citations.
6. Understand temporal state—what was true when.
7. Return structured facts, not just chunks of text.
8. Expose information as agent tools/APIs, not only through a human search UI.
9. Take actions in source systems when authorized.
10. Support evaluation of task completion, rather than just search relevance.

That distinction matters: recent research on agentic RAG finds substantial gains from letting the model iteratively search, open, navigate and analyze evidence rather than relying on a single retrieval pass.

So, if I were buying today

My starting evaluation would be:

Glean vs. Hebbia vs. Microsoft Copilot for a turnkey enterprise agent/context platform.

Then:

Coveo vs. Guru if the primary requirement is a highly governed knowledge/retrieval layer.

And:

Elastic if you have a strong engineering team and want to build the agent layer yourself.

The important caveat is that Glean is probably the closest mainstream answer to your question, but it isn't purely “search for agents” anymore—it is deliberately becoming a broader Work AI/agent platform. Its 275+ connectors and permissions-aware enterprise index are the part that makes the agent layer particularly useful.

If you're evaluating this for an AI-agent infrastructure stack, rather than an employee-facing search product, I would actually use a different comparison framework—context quality, multi-hop retrieval, knowledge graphs, temporal reasoning, tool APIs, permissions, and agent evaluation—and that produces a noticeably different leaderboard.