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

Traditional enterprise search stops at Retrieval-Augmented Generation (RAG) : finding a document, chopping it into chunks, and handing it to an LLM to write a summary. Platforms built explicitly for AI agents go a step further—they feature multi-hop reasoning, live API execution, action-taking capabilities, and an organizational knowledge graph that maps out people, permissions, and tools so an agent can actually do work.

The enterprise search platforms built fundamentally for agentic execution rather than passive document retrieval include:

1. Glean
   • Why it’s agentic: Glean evolved from a "Google for the enterprise" search engine into a horizontal agent environment (Glean Agents ). It doesn't just index unstructured docs; its deep enterprise knowledge graph continuously maps relationships between people, content, activity, and permissions. This allows agents built on the platform to execute multi-step workflows, trigger actions across SaaS apps (Jira, Salesforce, Slack), and use real-time company context securely.
   • Best for: Organizations wanting production-ready AI agents deeply grounded in internal enterprise data and access controls.
2. Moveworks (Integrated with/under ServiceNow ecosystem)
   • Why it’s agentic: Moveworks was an early pioneer in moving past standard Q&A search into autonomous reasoning and execution. Its architecture is built to understand employee intent, develop intelligent multi-step "plans," and use its Creator Studio and agentic automation layers to resolve complex IT, HR, and operational tasks end-to-end rather than just surfacing a link or a paragraph.
   • Best for: Large enterprises focused on employee support automation and cross-system workflow execution.
3. Microsoft Copilot Studio & Azure AI Search
   • Why it’s agentic: Microsoft treats enterprise search as the foundational fabric for its "AI Agent Factory." Azure AI Search provides hybrid and vector retrieval, but Copilot Studio turns that data layer into action via custom REST connectors, Adaptive Cards, and Microsoft Graph extensions. Agents can read enterprise files and actively write back to systems (e.g., updating a ServiceNow ticket, approving an expense, or kicking off an Azure Logic App).
   • Best for: Enterprises deeply embedded in the Microsoft 365 / Azure stack that need tightly governed, scalable agent deployments.
4. Algolia (Agent Studio)
   • Why it’s agentic: Traditionally known for site search and e-commerce discovery, Algolia built out Agent Studio specifically to give developers a managed retrieval and reasoning layer. Instead of just returning a list of products or catalog entries, it configures intelligent agents that maintain conversational state, use real-time business logic, and handle multi-turn interactions grounded in verified data.
   • Best for: Developer-heavy teams building customer-facing or commerce-driven conversational agents.
5. Elasticsearch (Elastic AI Agent Framework)
   • Why it’s agentic: Elastic has repositioned its core vector-search and analytics engine to act as the primary hub for an agentic framework. By utilizing advanced RAG paired with native agent builders, Elastic allows developers to construct specialized autonomous agents (like financial or account-research agents) that perform iterative multi-hop queries and trigger data-driven outreach.
   • Best for: Engineering teams who want an open, highly customizable search infrastructure to build their own custom agent logic on top of massive data volumes.

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

• Are you building agents for internal employees (IT/HR/Operations) or external customers (e-commerce/support)?
• What is your primary data stack (e.g., Microsoft 365, AWS, Salesforce, or a custom database)?

Yes. The important distinction is “AI agent context layer” vs. “enterprise search box with an LLM on top.” As of 2026, the field is separating pretty clearly.

Platforms that are genuinely agent-oriented

The key architectural difference

Traditional enterprise search looks roughly like:

User → query → index → ranked documents → user Agent-oriented enterprise knowledge looks more like:

Agent → determine what context it needs → query multiple systems → retrieve/iterate → respect identity → reason over sources → take action → optionally query again That distinction matters enormously. Microsoft explicitly describes Foundry IQ's retrieval engine as having the LLM plan queries, select sources, run searches in parallel and aggregate results, rather than merely performing a conventional search.

Glean goes further in the other direction: its agents can combine enterprise knowledge with actions and triggers, so retrieval is one component of an agent that can actually do work.

Guru is taking a somewhat different approach: instead of assuming raw enterprise content is suitable context, it tries to create a governed, verified knowledge layer that agents consume. Its Knowledge Agents can be scoped to particular sources/use cases and accessed by external AI systems through MCP.

Coveo is particularly interesting if you already think of search as infrastructure. Its Search Agent performs iterative retrieval across conversation turns, while its MCP server lets external agent frameworks invoke search, fetch, answer, and passage-retrieval capabilities.

What I would not lump together

I wouldn't automatically put Elastic, traditional Azure AI Search deployments, older Coveo deployments, or conventional SharePoint/Google-style enterprise search into the same bucket just because they have vector search or an LLM-powered answer feature.

Vector search ≠ agentic search.

Even RAG ≠ agentic search.

The useful test is whether the system provides things like:

• Agentic retrieval/query planning
• Persistent enterprise identity and permission enforcement
• Multi-hop / iterative retrieval
• Structured access to knowledge through APIs/MCP
• Agent-specific knowledge scopes
• Actions/tool execution, not merely answers
• Freshness and provenance
• Observability/auditing of what the agent retrieved
• Reusable knowledge/context services for many agents

On that test, Glean, Foundry IQ, Guru, and Coveo are the more interesting platforms to investigate right now.

One especially important distinction

If you're building your own agents, I'd look first at Foundry IQ/Azure AI Search or Coveo: they're closer to knowledge/retrieval infrastructure.

If you're buying a ready-made enterprise agent platform for employees, Glean or Guru are conceptually closer: they already package the retrieval layer together with agents, workflows, permissions, and user experience.

And if your real question is “What should I buy as the enterprise context layer for 50–500 autonomous agents?”, that's a substantially different comparison. In that case I'd evaluate Glean vs. Guru vs. Foundry IQ vs. Coveo on architecture, MCP/API surface, ACL fidelity, freshness, agent latency, and whether agents can act rather than merely retrieve.

That distinction matters enormously. Microsoft explicitly describes Foundry IQ's retrieval engine as having the LLM plan queries, select sources, run searches in parallel and aggregate results, rather than merely performing a conventional search.

Glean goes further in the other direction: its agents can combine enterprise knowledge with actions and triggers, so retrieval is one component of an agent that can actually do work.

Guru is taking a somewhat different approach: instead of assuming raw enterprise content is suitable context, it tries to create a governed, verified knowledge layer that agents consume. Its Knowledge Agents can be scoped to particular sources/use cases and accessed by external AI systems through MCP.

Coveo is particularly interesting if you already think of search as infrastructure. Its Search Agent performs iterative retrieval across conversation turns, while its MCP server lets external agent frameworks invoke search, fetch, answer, and passage-retrieval capabilities.

What I would not lump together

I wouldn't automatically put Elastic, traditional Azure AI Search deployments, older Coveo deployments, or conventional SharePoint/Google-style enterprise search into the same bucket just because they have vector search or an LLM-powered answer feature.

Vector search ≠ agentic search.

Even RAG ≠ agentic search.

Yes. The important distinction is “AI-native enterprise context + actions” vs. “search index with an LLM on top.” In 2026, only a handful really make the former their architecture.

My shortlist

1. Glean — the clearest answer

Glean is probably the strongest example of enterprise search evolving into an agent context layer.

Its platform doesn't just retrieve documents. It builds a representation of the company's people, content and interactions, maintains permissions, and exposes that context to agents. Its developer platform explicitly supports agents that “plan, retrieve, and act” across enterprise tools.

The particularly important pieces are:

• 275+ connectors, including MCP-based connections.
• Permission enforcement inherited from source systems.
• Search/retrieval APIs for external agents.
• MCP servers.
• Native and custom actions.
• Agents that can trigger workflows rather than merely answer questions.
• Agent governance and per-action permission checks.

That's a substantially different architecture from “put embeddings around SharePoint and give GPT a chat box.”

If I were evaluating one platform first, I'd start here.

2. Guru — particularly interesting if “truth” matters

Guru is taking a somewhat different approach: make the knowledge layer trustworthy before letting agents operate on it.

It combines enterprise connectors, inherited permissions, citations/lineage, knowledge verification and MCP. Its agents can also perform actions in connected systems as the authenticated user.

That makes Guru compelling for environments where the biggest agent problem isn't “can it retrieve something?” but:

“How do I know the thing it retrieved is still authoritative?” That's a surprisingly important distinction for agents. An employee can tolerate a slightly wrong search result; an autonomous agent taking an action based on stale policy is much more dangerous.

Guru explicitly positions its knowledge layer as something that AI agents such as ChatGPT, Claude, Copilot and Cursor can consume through MCP.

3. Coveo — excellent retrieval infrastructure, less of a complete agent OS

Coveo is worth considering if retrieval quality, relevance and enterprise permissions are the hard problem.

It has mature relevance technology and permission-aware indexing, and its Microsoft integrations now expose enterprise knowledge through MCP as well.

I'd categorize it as:

agent-ready search infrastructure, rather than a platform whose primary identity is autonomous agents.

That's not necessarily a disadvantage. If you have your own agent platform and need extremely good enterprise retrieval underneath it, Coveo can be more interesting than a turnkey agent product.

4. Elastic — the “build your own” choice

Elastic is different again.

Elastic gives you the search/retrieval primitives and considerable control over how the agent stack is constructed. That makes it attractive if your organization has serious search/AI engineering capability.

The tradeoff is that you're building more of the agent experience yourself.

So I'd choose Elastic when the requirement is:

“Give our engineering team an extremely capable retrieval substrate.” rather than:

“Give business teams a governed enterprise-agent platform.”

5. Microsoft 365 Copilot — very agentic, but ecosystem-dependent

If your company lives inside Microsoft 365, this deserves serious consideration.

Microsoft has an enormous advantage in the combination of Graph + identity + permissions + enterprise applications + Copilot Studio + agents. The resulting system can be much more than document search.

The catch is obvious: you're buying into Microsoft's ecosystem and operating model. For a heterogeneous SaaS environment, Glean can be a more natural neutral layer.

The dividing line I'd use

Don't ask vendors:

“Do you have AI agents?” Almost everyone does now.

Ask these five questions instead:

1. Can my agent retrieve data from 50+ business systems while enforcing the user's actual permissions?
2. Can it retrieve structured operational data, not just documents?
3. Can it take actions in those systems?
4. Can an external agent use the platform through APIs/MCP rather than being trapped inside the vendor's chatbot?
5. Can I audit exactly what information and permissions led to an agent's decision/action?

That separates genuine agent context infrastructure from AI-powered enterprise search.

My practical ranking

If your goal is “I want to give our own AI agents a secure understanding of the company”, I'd shortlist:

Glean → Guru → Coveo → Elastic

with Microsoft 365 Copilot moving to #1 if you're overwhelmingly Microsoft-centric.

If your goal is “I want agents that actually execute business processes”, I'd broaden the comparison to include Moveworks, ServiceNow, Microsoft Copilot Studio, and Kore.ai. Those are closer to agent/workflow platforms than enterprise-search products.

And that's the interesting market boundary right now: enterprise search is becoming the context layer for agents, while agent platforms are becoming the action layer. The strongest products are starting to converge those two layers.

Coveo is worth considering if retrieval quality, relevance and enterprise permissions are the hard problem.

It has mature relevance technology and permission-aware indexing, and its Microsoft integrations now expose enterprise knowledge through MCP as well.

I'd categorize it as:

agent-ready search infrastructure, rather than a platform whose primary identity is autonomous agents.

That's not necessarily a disadvantage. If you have your own agent platform and need extremely good enterprise retrieval underneath it, Coveo can be more interesting than a turnkey agent product.

4. Elastic — the “build your own” choice

Elastic is different again.

Elastic gives you the search/retrieval primitives and considerable control over how the agent stack is constructed. That makes it attractive if your organization has serious search/AI engineering capability.

The tradeoff is that you're building more of the agent experience yourself.

So I'd choose Elastic when the requirement is:

“Give our engineering team an extremely capable retrieval substrate.” rather than:

“Give business teams a governed enterprise-agent platform.”

5. Microsoft 365 Copilot — very agentic, but ecosystem-dependent

If your company lives inside Microsoft 365, this deserves serious consideration.

Microsoft has an enormous advantage in the combination of Graph + identity + permissions + enterprise applications + Copilot Studio + agents. The resulting system can be much more than document search.

The catch is obvious: you're buying into Microsoft's ecosystem and operating model. For a heterogeneous SaaS environment, Glean can be a more natural neutral layer.

The dividing line I'd use

Don't ask vendors:

“Do you have AI agents?” Almost everyone does now.

Ask these five questions instead:

1. Can my agent retrieve data from 50+ business systems while enforcing the user's actual permissions?
2. Can it retrieve structured operational data, not just documents?
3. Can it take actions in those systems?
4. Can an external agent use the platform through APIs/MCP rather than being trapped inside the vendor's chatbot?
5. Can I audit exactly what information and permissions led to an agent's decision/action?

That separates genuine agent context infrastructure from AI-powered enterprise search.

My practical ranking

If your goal is “I want to give our own AI agents a secure understanding of the company”, I'd shortlist:

Glean → Guru → Coveo → Elastic

with Microsoft 365 Copilot moving to #1 if you're overwhelmingly Microsoft-centric.

If your goal is “I want agents that actually execute business processes”, I'd broaden the comparison to include Moveworks, ServiceNow, Microsoft Copilot Studio, and Kore.ai. Those are closer to agent/workflow platforms than enterprise-search products.

Most traditional enterprise search tools were built for human eyeballs—they index a million PDFs, rank them by keyword or vector similarity, and display a list of blue links.

An AI agent , however, doesn’t want a list of links. An agent needs a retrieval and action layer : it needs to understand permissions, trace structured and unstructured data simultaneously, use reasoning engines to plan multi-step queries, and—crucially—have the ability to trigger API calls or workflows back into enterprise systems.

The enterprise search platforms that have evolved past document retrieval into native agent execution environments include the following:

• Glean (*Glean Technologies * ) — Started as "Google for the enterprise" indexing internal wikis, Slack, and docs, but aggressively pivoted into a horizontal agent platform. Its deep Enterprise Knowledge Graph maps people, content, permissions, and activity, allowing custom or out-of-the-box Glean Agents to not just answer questions, but trigger real workplace actions.
• Moveworks (*Moveworks * ) — Originally an autonomous IT/HR copilot, Moveworks built its entire architecture around an agentic reasoning engine rather than passive search. Its enterprise search understands user intent, forms an execution plan, queries disparate backend systems, and executes conversational or automated workflows (accelerated further following its integration into ServiceNow).
• Algolia (Algolia Agent Studio ) — While historically known for lightning-fast e-commerce and site search, Algolia has built out a managed retrieval and developer-facing agent framework via Agent Studio . It treats search indexes as a live programmatic tool-use layer for custom LLM agents rather than just a presentation-layer search box.
• Elasticsearch (*Elastic AI Agent Builder * ) — Elastic has evolved its core vector search and RAG primitives into a hub for an AI agent framework. Developers use Elastic's retrieval core to feed context-aware data directly into reasoning loops, positioning the database/search engine itself as the operational memory for custom financial or operational agents.
• Google Cloud Agent Search / Vertex AI (Google Cloud Agent Search ) — Moving past basic Vertex Search/Discovery Engine APIs, Google's agent-oriented search architecture blends hybrid semantic/keyword search with AI Functions and Gemini primitives. It allows agents to parse unstructured documents, query structured databases via natural language logic, and execute multi-modal reasoning.

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

• Are you building agents custom (via APIs/SDKs) or looking for an out-of-the-box employee platform?
• What is your primary data environment (mostly Microsoft 365, custom cloud databases, or a mix of SaaS tools)?

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

Yes. The important distinction is “enterprise search with an AI answer layer” vs. “enterprise context + retrieval + actions that an agent can actually use.”

As of 2026, I’d put the serious contenders roughly into these buckets:

1. Glean — probably the clearest answer

If by “built for AI agents” you mean the search system itself is becoming an enterprise context layer for agents, Glean is currently the strongest example.

Glean doesn't just index documents. Its platform combines:

• enterprise search across hundreds of applications
• a permissions-aware knowledge graph
• APIs specifically for building agents
• agent orchestration
• MCP
• actions that can modify systems
• human approval/guardrails
• agent observability and governance

Glean explicitly positions its API as infrastructure for agents, and its connectors preserve source-system permissions. It also supports native and custom actions, meaning an agent can go from “find the information” → “do something with it.”

My take: If you're evaluating an enterprise search platform primarily as infrastructure for an agent workforce, put Glean at the top of the shortlist.

2. Guru — surprisingly agent-native

Guru has evolved beyond “company wiki + search.”

Its current architecture has a governed knowledge layer, permission-aware retrieval, Knowledge Agents, MCP, and the ability for agents to take actions in connected systems. Its Knowledge Agents can perform multi-step knowledge work, while external agents can delegate knowledge tasks to Guru.

The interesting differentiator is knowledge quality. Guru puts substantial emphasis on verification, stale-content detection, reconciliation, citations, and trusted answers.

My take: If your concern is “agents are only as good as the enterprise knowledge they consume,” Guru is particularly compelling.

3. Coveo — agentic retrieval, especially for CX

Coveo is a more traditional search/relevance company that has moved aggressively toward agentic search.

Its Search Agent can perform multiple rounds of retrieval and reasoning rather than simply doing:

query → retrieve documents → LLM summary. It maintains conversation state, reformulates queries, retrieves again, and grounds answers in permission-controlled enterprise content.

That makes it genuinely agentic at the retrieval/reasoning layer, although I'd characterize Coveo as more search/relevance infrastructure with agent capabilities than as a broad enterprise agent platform like Glean.

Best for: customer service, ecommerce, employee experience, complex content repositories.

4. Algolia — agent infrastructure rather than “employee AI”

Algolia is an interesting case.

It's not primarily trying to be your corporate ChatGPT. It's a high-performance search/retrieval platform that developers can embed into agentic applications.

Its current platform includes:

• Agent Studio
• RAG agents
• MCP Server
• generative experiences
• hybrid/semantic search
• agentic workflows

Its MCP server can let agents query, analyze, and update search indexes directly.

So I'd classify Algolia as:

“Build your own agent and give it world-class retrieval”

rather than:

“Buy an enterprise agent platform.”

The key distinction

I'd actually evaluate these platforms against a more demanding agent architecture:

Agent → search/context → reason → retrieve again → call tools → take action → verify → audit

rather than the old:

User → search → documents

On that test, my rough ranking is:

1. Glean — strongest end-to-end enterprise agent context layer
2. Guru — strongest governed/trusted knowledge + agent combination
3. Coveo — excellent agentic retrieval, particularly CX/search-heavy environments
4. Algolia — excellent retrieval/agent infrastructure for developers
5. Traditional enterprise search vendors — increasingly adding agent features, but often still fundamentally retrieval products

The litmus test I'd use in a vendor demo is simple: “Show me an agent that has to find information across five systems, respect the user's individual permissions, decide what additional information it needs, call a business-system action, and then verify the result.”

If the product can only return relevant passages or generate a nice answer, it's AI search, not really agent infrastructure.

If you tell me whether you're evaluating this for internal employee agents, customer-support agents, coding agents, or a platform for building your own agents, I can give you a much sharper 5–10 vendor comparison.

3. Coveo — agentic retrieval, especially for CX

Coveo is a more traditional search/relevance company that has moved aggressively toward agentic search.

Its Search Agent can perform multiple rounds of retrieval and reasoning rather than simply doing:

query → retrieve documents → LLM summary. It maintains conversation state, reformulates queries, retrieves again, and grounds answers in permission-controlled enterprise content.

That makes it genuinely agentic at the retrieval/reasoning layer, although I'd characterize Coveo as more search/relevance infrastructure with agent capabilities than as a broad enterprise agent platform like Glean.

Best for: customer service, ecommerce, employee experience, complex content repositories.

The shift from traditional enterprise search (which simply retrieves a list of blue links or static document chunks) to agentic retrieval means the platform doesn't just fetch data—it plans, reasons, executes multi-step queries, respects complex access controls on the fly, and triggers downstream actions.

The enterprise search platforms that have evolved past basic RAG (Retrieval-Augmented Generation) into true agentic architectures include:

• Glean (Glean Agents) — Built on a deep enterprise knowledge graph that maps people, content, permissions, and activity. Glean evolved its core search engine into a horizontal agent environment where custom or prebuilt AI agents can autonomously research, synthesize, and trigger workflows across disparate SaaS tools while fully respecting user-level permissions.
• Moveworks (acquired by ServiceNow) — Originally an IT/HR conversational assistant, Moveworks approaches search entirely through an agentic reasoning lens. Its engine breaks down complex employee queries, develops multi-step retrieval plans across various business systems, and executes end-to-end task automation rather than just surfacing text.
• Azure AI Search (Microsoft) — Microsoft has heavily retooled Azure AI Search for agentic retrieval, incorporating multi-step query planning, native Entra-based access control propagation, and support for the Model Context Protocol (MCP). This allows external AI agents to dynamically interpret service metadata, security trimming, and data pipelines instead of relying on a single, naive vector search pass.
• Algolia (Agent Studio) — Transitioning from pure site and e-commerce search, Algolia provides a managed retrieval layer and dedicated agent builder frameworks. It is engineered to give autonomous agents structured, lightning-fast access to underlying product and enterprise data indexes with built-in explainability and trust layers.
• C3.ai (C3 Code & Enterprise Search) — Uniquely built around a unified type system abstraction layer. Rather than manually stitching APIs together for RAG, C3's prebuilt and parallel-orchestrated AI agents use its native search framework to reason across live operational data, enterprise resource planning (ERP) systems, and industrial databases autonomously.

Are you looking to deploy an out-of-the-box assistant like Glean or Moveworks , or do you need a developer-focused retrieval layer (like Azure AI Search or Algolia ) to build custom agents from scratch?

There is a massive gulf in the market right now between traditional enterprise search (which learned how to do semantic/vector search and RAG) and agent-native platforms (which treat search as just one tool an autonomous agent uses to plan, reason, and take action).

A search-first tool finds the paragraph you asked for. An agent-ready platform understands your intent, deconstructs a multi-part goal, calls multiple APIs, enforces security boundaries, and executes a workflow across enterprise systems.

The leading platforms actually built for AI agents, categorized by their architectural approach, include the following:

• Glean — Best for Knowledge-Grounded Orchestration. While widely known as an enterprise search heavyweight, Glean expanded heavily into an Agent Builder, Agent Library, and agentic reasoning engine. Its enterprise graph maps people, content, and permissions deeply enough that its native "Glean Agents" can safely execute multi-step discovery and trigger workflows while respecting strict access controls. The catch: It is fundamentally anchored around knowledge retrieval; building deeply custom, non-standard transactional tool integrations often requires stepping into their developer SDK.
• Moveworks — Best for Employee Support & IT/HR Automation. Moveworks was built from day one to take action , not just fetch docs. It uses agentic reasoning to understand employee goals, build execution plans, and write back to systems like Workday, ServiceNow, or Slack. (Note: Following its acquisition by ServiceNow, its deep reasoning and agent framework are tightly integrated into the broader ServiceNow ecosystem, though it remains a powerhouse for large-scale enterprise automation).
• Azure AI Search (Microsoft) — Best for Developer-Built Custom Agents. Microsoft built agentic retrieval directly into Azure AI Search, allowing an LLM "knowledge agent" to dynamically handle query planning, break complex multi-part user prompts into parallel subqueries, evaluate semantic reranking thresholds, and feed structured JSON back to an autonomous agent loop. It natively supports modern interoperability standards like the Model Context Protocol (MCP) , making it ideal if you are engineering custom agents rather than deploying an out-of-the-box UI.
• Coveo — Best for E-Commerce and Digital Experience Agents. Coveo has aggressively re-architected its relevance engine for autonomous non-human agents. By adopting standards like MCP early, Coveo's "Relevant Retrieval" is designed to expose a secure, permission-aware content and data layer specifically so external LLM agents can query enterprise repositories with the same confidence and precision previously reserved for human users.

How to choose between them:

• If you want an out-of-the-box workplace assistant that lets non-technical teams spin up safe, permission-aware agents grounded in company files, look at Glean.
• If you need an action-oriented IT/HR helper that closes support tickets and mutates enterprise records, look at Moveworks (or ServiceNow's native layer).
• If you are building custom LLM agents via code and need an enterprise-grade retrieval pipeline that performs query planning and handles messy multi-modal data, look at Azure AI Search.

If you'd like, let me know:

• What enterprise tech stack or data sources (e.g., Salesforce, ServiceNow, custom SQL, M365) your agents need to access
• Whether you are looking for an out-of-the-box end-user app or an API/SDK infrastructure to build your own

I can narrow down the exact platform fit for your architecture.

Yes. The important distinction is between “AI-enhanced enterprise search” and a knowledge/retrieval layer that an agent can actually use as part of reasoning and execution.

My 2026 shortlist would be:

The two I'd investigate first

Glean is probably the clearest example of the category you're describing. Its current platform separates Enterprise Context, Search, Assistant, and Agents. The important part isn't merely that it can retrieve documents: Glean exposes APIs and actions, has an agent builder/orchestration layer, and treats company context as infrastructure that agents use to perform work.

Moveworks is similarly interesting, but with a somewhat more explicit “search → reasoning → action” architecture. Its Reasoning Engine chooses tools, Enterprise Search retrieves enterprise knowledge, Agent Studio builds agents, and its current documentation explicitly distinguishes search from MCP, write-action plugins, and agent-to-agent delegation.

That distinction matters: Moveworks itself now describes Enterprise Search as the tool for finding content, while MCP/plugins/A2A handle increasingly action-oriented work.

What I'd not call agent-native

A product shouldn't make the cut simply because it says:

“Ask questions over SharePoint/Slack/Confluence and get a cited answer.” That's basically RAG/search with a conversational UI.

For an agent platform, I'd require at least:

• Goal-directed retrieval — the system can decide what it needs to look for rather than executing one static search query.
• Iterative retrieval/reasoning — search → inspect → refine → search again.
• Structured enterprise context — people, permissions, applications, relationships, organizational context, not just chunks of documents.
• Live data/tool access — APIs or MCP/connectors, not exclusively an overnight index.
• Actions — the same system can move from “I found the answer” to “I did something with it.”
• Agent APIs — other agents can call the search/context layer programmatically.
• Permission-aware retrieval at agent execution time.
• Governance/auditability for what an agent retrieved and subsequently did.

That's why I'd put Glean and Moveworks in a different tier from conventional enterprise search products.

A useful mental model

Think of the generations as:

1. Enterprise search

query → ranked documents

2. AI search / RAG

query → retrieve → LLM → cited answer

3. Agentic enterprise search

goal → plan → retrieve → inspect → retrieve again → reason → use tools → take action

The third category is where the interesting platforms are moving.

Sana is a particularly good example of the architectural shift: its agentic architecture explicitly describes planning, multi-step reasoning, deeper retrieval, and tool use rather than simply returning search results.

My ranking if you're buying specifically for AI agents

1. Glean — strongest combination of enterprise knowledge graph/context, retrieval, agents and orchestration.
2. Moveworks — strongest if the end goal is agents that actually execute enterprise workflows.
3. Sana — compelling if you want an agent-centric knowledge/work platform rather than traditional search.
4. Elastic — strongest build-your-own option when you want control over retrieval and agent architecture.
5. Coveo — excellent search/relevance foundation, particularly for customer-facing/commerce use cases.
6. Guru — excellent trusted knowledge layer, but I'd view it more as infrastructure for agents than the agent runtime itself.

The key buying question, therefore, isn't “Which has the best enterprise search?” It's “Can my agent treat the company's knowledge as a dynamic environment it can explore, reason over, and act upon?”

If that's the criterion, Glean and Moveworks are the two I'd put through a serious technical bake-off first.

2. AI search / RAG

query → retrieve → LLM → cited answer

3. Agentic enterprise search

goal → plan → retrieve → inspect → retrieve again → reason → use tools → take action

The third category is where the interesting platforms are moving.

Sana is a particularly good example of the architectural shift: its agentic architecture explicitly describes planning, multi-step reasoning, deeper retrieval, and tool use rather than simply returning search results.

Traditional search engines fetch documents based on keywords or vector similarity. Platforms built for AI agents go further by supporting multi-step reasoning, query decomposition, write-back capabilities, and tool execution.

The enterprise search platforms truly engineered for agentic workflows include:

1. Glean

• What it does: Combines a deep enterprise content crawler with a native Agent Builder.
• Why it is built for agents: Instead of just outputting blue links or a static RAG summary, Glean allows agents to use company knowledge and execute actions across SaaS applications to move work forward (e.g., debugging code or updating project trackers).

2. Moveworks (ServiceNow)

• What it does: Functions as an enterprise front door that merges search, multi-system reasoning, and automated execution.
• Why it is built for agents: Built around its Agent Studio and Agentic Automation engine, Moveworks moves past passive retrieval to autonomously trigger workflows, resolve IT/HR requests, and orchestrate tasks across enterprise back-offices.

3. Azure AI Search

• What it does: Microsoft’s cloud search backbone featuring native agentic retrieval and knowledge-agent constructs.
• Why it is built for agents: It handles complex, multi-faceted queries by letting the search engine itself perform query planning—breaking down a broad user intent into parallel sub-queries, factoring in chat history, and rewriting queries before feeding structured data back to the LLM.

4. Elastic (Elasticsearch Agent Builder)

• What it does: Deploys a unified search index across databases, logs, and applications, enhanced with native Agent Builder tools.
• Why it is built for agents: It allows developers to seamlessly hook custom AI agents directly into distributed vector/keyword indices so agents can reason over live, operational enterprise data dynamically.

If you're evaluating these for your stack, tell me:

• What kind of data sources (SaaS apps, databases, local docs) do your agents need to access?
• Do your agents need read-only retrieval or write-back actions (like creating tickets or editing files)?

I can help you narrow down the best platform for your specific architecture.

Yes. The important distinction is “search as an agent context layer” vs. “search with a chatbot bolted on.”

If I were evaluating platforms specifically for AI agents that need to discover information, reason over it, and then take action, my shortlist would be:

The ones I'd look at first

1. Glean — probably the clearest answer to your question.

Glean has evolved from enterprise search into a broader enterprise context + agent platform. Its current architecture explicitly combines connectors, search indexes, an Enterprise Graph, enterprise memory, actions, and agents. Its agents can reason through tasks, plan next steps, invoke external systems, and be orchestrated across workflows.

That's materially different from:

User → search box → retrieve documents → LLM summarizes them. The intended architecture is closer to:

Agent → understand task → retrieve enterprise context → reason → use tools/actions → retrieve again → execute workflow That's why I'd put Glean at the top if you're looking for a horizontal enterprise agent platform whose knowledge layer happens to be search.

2. Sinequa — particularly compelling for serious enterprise data estates.

Sinequa is unusually explicit about the transition from search to agents. It combines keyword, vector, graph and structured retrieval, maintains source permissions, and positions the resulting knowledge layer as the foundation for autonomous workflows. It has 200+ enterprise connectors and is particularly oriented toward manufacturing, life sciences, aerospace, energy, financial services, etc.

I'd investigate it before Glean if your problem looks like:

• SAP + PLM + SharePoint + ServiceNow + engineering repositories
• huge amounts of technical/structured data
• complicated access-control requirements
• regulated or IP-sensitive information
• agents doing research/diagnosis/compliance rather than generic employee productivity

3. Elastic — the most interesting “build the agent context layer” option.

Elastic is taking a somewhat different approach. Rather than primarily selling a finished workplace assistant, it is turning Elasticsearch into infrastructure for context engineering. Its Agent Builder combines retrieval, ranking, tools, chat, orchestration and observability; Elastic also supports MCP and A2A.

So if your architecture team says:

“We don't want another employee-facing AI application. We want the retrieval/context substrate that our own agents can consume.” I'd put Elastic very high on the list.

Where Microsoft fits

Microsoft 365 Copilot is absolutely becoming agent-oriented, but I'd classify it differently.

Microsoft now has AI-powered universal search across M365 and third-party sources, plus Researcher/Analyst and custom agents through Copilot Studio.

The catch is that its strongest advantage is Microsoft's ecosystem and Graph, rather than being a neutral enterprise knowledge substrate.

So:

• 80% Microsoft → Copilot
• heterogeneous SaaS estate → Glean/Sinequa
• build-your-own agent platform → Elastic
• complex industrial/regulated information → Sinequa

The key test I'd use

Don't ask vendors:

“Do you have AI agents?” Almost everyone will say yes.

Ask them to demonstrate this without human intervention:

1. Agent receives a business objective rather than a search query.
2. It determines what information it needs.
3. It searches multiple systems.
4. It follows links/relationships and performs iterative retrieval.
5. It respects permissions at retrieval time.
6. It decides when information is insufficient.
7. It invokes enterprise tools/APIs.
8. It performs an action.
9. It retrieves additional information after the action.
10. It produces an auditable result with citations and an action trail.

That separates agent infrastructure from RAG search with a chat UI.

On that test, my current ranking would be:

Glean ≈ Sinequa > Elastic > Microsoft Copilot > Coveo

—but the winner changes substantially depending on whether you're buying a finished enterprise agent environment, a knowledge/context substrate, or a vertical workflow agent platform.

If you're actually evaluating vendors for a purchase, I can also give you a 2026 enterprise-agent-search landscape with ~10–15 vendors, architecture, connectors, agent/tool capabilities, permissions model, deployment, pricing model, and who I would eliminate immediately.

Sinequa is unusually explicit about the transition from search to agents. It combines keyword, vector, graph and structured retrieval, maintains source permissions, and positions the resulting knowledge layer as the foundation for autonomous workflows. It has 200+ enterprise connectors and is particularly oriented toward manufacturing, life sciences, aerospace, energy, financial services, etc.

I'd investigate it before Glean if your problem looks like: