Nexus vs Glean: Enterprise Knowledge Layer vs Enterprise Agents

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Glean is an enterprise AI platform built around the knowledge layer — it indexes your company's tools, surfaces information using natural language, and is now adding agent capabilities on top of that search foundation. At $200M+ ARR and a $7.2B valuation (Series F, June 2025), Glean has validated that enterprises need better ways to find and access information. Nexus is a different category: autonomous agents that go beyond the knowledge layer entirely, completing entire business workflows end-to-end — sales research, customer onboarding, support triage — backed by Forward Deployed Engineers embedded in your team.

The core question: is your bottleneck finding information, or completing the work that information points to?

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Quick honest summary

Glean connects to your company's tools, indexes the data, and helps employees find answers faster using natural language. It does this well, and for companies where the biggest problem is scattered information, Glean is a strong choice. Glean is now adding agent capabilities on top of that knowledge layer, but the architecture is fundamentally designed around search, knowledge, and content — not around completing deep business processes. The depth of automation it can handle is bounded by that foundation.

Nexus is a different category. It is an enterprise AI solution (platform plus service) where autonomous agents go beyond the knowledge layer entirely. They complete entire business workflows end-to-end: sales research, customer onboarding, support triage, compliance monitoring. Nexus agents do not just find information. They collect it, validate it across systems, make decisions within guardrails, escalate when uncertain, and execute actions. They combine information retrieval with deep process execution, autonomous decision-making, and multi-system orchestration. And Nexus comes with Forward Deployed Engineers embedded in your team to ensure agents deliver measurable outcomes, not just technology.

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Side-by-side comparison

Dimension	Glean	Nexus
Core function	Knowledge-layer platform: finds, summarizes, and surfaces information across company systems — now adding agent actions on top of search foundation	Autonomous AI agents that go beyond the knowledge layer — complete entire workflows end-to-end across enterprise systems, backed by embedded Forward Deployed Engineers
Category	Knowledge-layer platform, adding agents — automation depth bounded by search-first architecture	Agent-first enterprise AI solution — platform + service, built for deep process execution from day one
Who builds and owns it	IT deploys the platform; employees use it to search and ask questions	Business teams build and deploy agents, supported by Forward Deployed Engineers — no engineering dependency
How it handles complexity	Surfaces relevant information for the employee to act on — 100+ native actions across connected apps, with depth of process automation bounded by search-first design	Agents adapt intelligently to exceptions, escalate with full context when uncertain, built for multi-step multi-system process execution including decision logic
Completes work autonomously?	Primarily finds and presents information — agent capabilities are emerging but built on knowledge-layer architecture	Agents execute, validate, route, decide, and escalate independently — core architecture, not bolted onto a search foundation
Deployment model	SaaS platform — IT connects data sources for indexing, weeks to months for full indexing	3-month POC tied to measurable outcomes — Forward Deployed Engineers embedded with your team, production agents in days to weeks
Integrations	100+ enterprise connectors for indexing and search (Glean integrations) — Google Workspace, Slack, Confluence, Salesforce, and others; 100+ native agent actions	4,000+ integrations across CRMs, ERPs, communication tools, and custom APIs — deploy across Slack, Teams, WhatsApp, email, phone, and web
Pricing model	Per-seat licensing — $50/user/month starting price, minimum enterprise contracts ($50K–60K/year), generative AI add-on ~$15/user/month extra, mandatory 10% support fee on ARR	Per-agent pricing tied to value delivered — not tied to headcount, 3-month POC with measurable outcomes first, annual commitment after POC
Security and compliance	SOC 2 Type II, data governance controls, granular permissions	SOC 2 Type II, ISO 27001, ISO 42001, GDPR — full audit trails, decision traceability, role-based access
Support model	Standard enterprise SaaS support — requires 1–2 FTEs internally to manage connectors, permissions, and index sync	Forward Deployed Engineers embedded in your organization — change management guidance, ongoing optimization, 100% POC-to-contract conversion rate
Best for	Companies where the bottleneck is finding information — information scattered across tools, need a knowledge layer	Enterprises where the bottleneck is completing work, not finding information — high-volume workflows crossing multiple systems with measurable financial outcomes

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Choose Glean if / Choose Nexus if

Choose Glean if:

• Your primary problem is employees unable to find information across tools
• You need a company-wide knowledge layer connecting all systems
• Every employee needs a capable AI assistant for daily search and Q&A tasks
• You want to improve how your organization surfaces institutional knowledge
• The work gets done once people have the right answer

Choose Nexus if:

• Your team already knows where the information is — the bottleneck is acting on it at scale
• You need AI that completes multi-step business processes, not just finds information about them
• Workflows cross multiple systems and require decision logic, exception handling, and autonomous execution
• You want a partner (Forward Deployed Engineers) not just a platform
• Per-seat pricing does not match your actual use case — you need value-based, per-agent pricing

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When Glean is the better choice

Being honest about this matters. Glean is the right fit in several scenarios, all of which share a common thread: the bottleneck is at the knowledge layer, not the execution layer.

• Your biggest problem is finding information, not acting on it. If employees waste hours every week searching through Slack, Confluence, Google Drive, and SharePoint for answers that already exist somewhere, Glean solves that problem well. It indexes your company's knowledge and makes it searchable with natural language. That is genuinely valuable. If the work gets done once people have the right information, a knowledge-layer tool is the right investment.

• You need a company-wide knowledge layer. Glean connects to your existing tools and starts indexing. If the priority is giving every employee a single place to search across all company systems, Glean provides that without requiring workflow design or agent configuration. At $200M+ ARR and customers spanning over 50 industries, they have validated this use case at scale (Glean surpasses $200M ARR, December 2025). Glean does the knowledge layer well.

• Your team needs better answers to internal questions. "What is our return policy for enterprise customers?" "Where is the Q3 competitive analysis?" "What did the engineering team decide about the API migration?" If these are the questions your team asks daily, Glean's AI-powered search and assistant handles them well.

• You want to improve how your company surfaces institutional knowledge. Companies with significant tribal knowledge — where critical information lives in people's heads or buried in old Slack threads — benefit from Glean's ability to index, rank, and surface that knowledge to anyone who needs it.

• You are primarily looking for a self-serve AI assistant. Glean's assistant now reasons through multi-step challenges, orchestrates sub-agents, and supports real-time voice interaction. If you want every employee to have a capable AI assistant for daily tasks, Glean's per-seat model is designed for that. This is the knowledge-layer paradigm at its strongest: making every individual more effective at finding and using information.

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When Nexus is the better choice

Enterprises that partner with Nexus tend to share a specific pattern: they already have access to information. The problem is that finding it does not complete the work. Knowing the answer and executing on it are fundamentally different challenges.

This is where the knowledge-layer architecture hits its ceiling. Enterprise AI platforms built around search, knowledge, and content excel at surfacing information and answering questions. Some are adding agent capabilities on top, but the depth of automation they can handle is bounded by that foundation. When work requires orchestrating across multiple systems, handling exceptions intelligently, and executing multi-step workflows autonomously, you need something that goes beyond the knowledge layer: agents that combine information retrieval with deep process execution, autonomous decision-making, and multi-system orchestration. Finding the right information is valuable, but it is only the first step of most enterprise workflows.

• You need AI that completes business processes, not just finds information about them. Sales research across thousands of accounts, customer onboarding across multiple countries, support triage with compliance requirements. These are multi-step workflows that require collecting data, validating it across systems, making decisions, handling exceptions, and taking action. A knowledge-layer tool surfaces the inputs. Nexus agents complete the entire workflow. That is the difference between the knowledge layer and the execution layer.

• Your team already knows where the information is. They need help acting on it at scale. Some sales teams do not struggle to find account information. They struggle to analyze thousands of enterprise accounts at the depth required to identify buying signals and competitive movements. The bottleneck is not search — it is the hours of research and synthesis required per account, multiplied across thousands. Nexus agents handle the entire research workflow autonomously.

• You want AI that works across systems, not just reads from them. Glean indexes your tools for search and is adding write actions across 100+ connected apps. But there is a structural difference between a knowledge-layer platform that adds actions and an agent platform built for deep process execution. Nexus agents operate bidirectionally across 4,000+ enterprise systems: pulling data from CRMs, validating against ERPs, communicating via WhatsApp or email, updating ticketing systems. When an enterprise onboards a customer, the agent collects information, validates it against multiple backend systems, checks compatibility, routes unusual cases, and escalates complex issues. That is not a search problem with actions bolted on. It is autonomous workflow execution that goes well beyond what knowledge-layer architecture supports.

• You need more than software. You need a partner. Nexus is not a platform you deploy and figure out on your own. Forward Deployed Engineers (real engineers, not support reps) embed with your team to identify the highest-impact use cases, design agents for your specific workflows, handle integration complexity, and run pilots without consuming your internal resources. Deploying enterprise AI is 10% technology and 90% organizational change.

• Business teams need to own workflows without engineering dependency. The most effective Nexus deployments are built by business teams, not engineering. Agents are deployed in weeks, not months. This is not about "no-code" as a feature. It is about business teams owning the outcome, with engineers embedded to support them.

• Per-seat pricing does not match your use case. Glean's per-seat model means every employee who might search needs a license, with costs adding up quickly across large organizations ($50+/user/month before add-ons). Nexus charges per agent. An agent that handles research across thousands of accounts or onboards thousands of customers costs the same regardless of how many employees are in your organization.

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What enterprises have built

Going beyond the knowledge layer in sales intelligence

An AI infrastructure company with world-class engineers — the kind of organization that could build sales automation internally if anyone could — faced a specific challenge: monitoring 12,000+ enterprise accounts for buying signals, competitive movements, and market intelligence. The problem was not finding information. It was synthesizing it at scale and turning it into actionable intelligence.

They tried open-ended AI tools first. Too inconsistent. They looked at traditional automation. Too rigid. With Nexus, their Head of Sales Intelligence (no engineering background) built an autonomous research agent that performs deep analysis per account across dozens of data sources, delivering structured intelligence to account executives.

The results:

• 24,000+ hours of research capacity added annually (equivalent to 12 full-time analysts)
• Agent adapts as they add data sources or change segmentation, without requiring a rebuild
• Expanding to a full agent fleet across their entire go-to-market organization

Autonomous workflow completion at enterprise scale

A multi-billion euro telecom operator with 120,000+ employees and significant internal engineering resources built autonomous customer onboarding agents using Nexus. The agent collects customer information in real-time, validates data against backend systems, checks compatibility, routes unusual cases, and escalates complex issues with full context — across multiple countries and languages, with full compliance and audit trails.

The results:

• Deployed in 4 weeks (business team built it, not engineering)
• 50% conversion improvement
• $4M+ incremental yearly revenue
• 100% team adoption because agents live inside channels teams already use
• Full governance: when the agent is confident, it proceeds; when uncertain, it escalates with context

The key: this is not a knowledge-layer problem. They did not need help finding customer data. They needed AI that could complete the onboarding workflow end-to-end, autonomously, at scale, with compliance built into every step.

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Key differences explained

The knowledge layer vs. the execution layer: different categories

This is the fundamental distinction, and it matters more than any feature comparison.

Enterprise AI platforms like Glean are essentially knowledge-layer tools and assistant builders. They connect to your company's systems, index the data, and help employees find answers faster. They excel at finding information, answering questions, and surfacing knowledge. The employee is still doing the work. The AI finds the information; the human decides what to do with it and executes. Glean is adding agent capabilities (100+ native actions, sub-agent orchestration, agent builder), but the architecture is fundamentally designed around search, knowledge, and content — not around completing deep business processes.

Nexus agents go beyond the knowledge layer. They combine information retrieval with deep process execution, autonomous decision-making, and multi-system orchestration. They complete entire workflows independently: collecting information, validating against systems, making decisions within guardrails, handling exceptions, escalating when uncertain, and executing actions across multiple systems. The agent does not just find the answer. It completes the work the answer points to.

This is not a criticism of Glean. Enterprise search is a real problem, and Glean has built a strong business solving it ($200M+ ARR, $7.2B valuation as of June 2025). But there is a meaningful difference between making information accessible and making work get done. The depth of automation that knowledge-layer platforms can handle is bounded by their search-first foundation. They can add agent actions, but the architecture was not designed for deep process execution, exception handling, or autonomous multi-system orchestration. If your bottleneck is "we cannot find things," Glean addresses that. If your bottleneck is "we know what needs to happen but cannot do it at scale," that is a different problem requiring agents that operate beyond the knowledge layer.

According to Deloitte's 2026 Tech Trends report, agentic AI is now the leading enterprise AI investment priority — specifically because enterprises have solved the knowledge retrieval problem and are now focused on execution (Deloitte Insights, 2026).

Knowledge-layer architecture vs. agent-first architecture: why it matters

Glean started as enterprise search and is adding agent capabilities on top. Nexus was built agent-first from day one. This distinction shapes how each platform handles the hard parts of enterprise AI, and it is not a gap that closes easily.

Knowledge-layer platforms that add agents inherit the assumptions of the knowledge paradigm: the human is ultimately making decisions and the AI is providing inputs. The architecture was designed to find and surface information, not to execute deep business processes. Agent actions are an extension of search, not a rethinking of it. Agent-first architecture starts from the opposite premise: the agent completes the work, and humans step in for judgment calls. This leads to fundamentally different approaches to exception handling, multi-system orchestration, escalation logic, audit trails, and governance.

Glean's agent builder now supports 100+ native actions and MCP host support, which expands what agents can do within connected apps. But there is a structural difference between taking actions within apps and orchestrating complete workflows across 4,000+ systems with decision logic, exception handling, and intelligent escalation. The depth of automation a knowledge-layer platform can support is bounded by a foundation that was built to retrieve and present, not to execute and decide.

Software vs. solution: the service layer difference

Knowledge-layer platforms are typically self-serve software. Glean is enterprise software. You purchase licenses, connect your data sources, and your team uses the platform. Glean recommends 1–2 dedicated FTEs internally to manage connectors, permissions, and index synchronization. That model works for a knowledge tool where the primary interaction is employees asking questions and getting answers.

Going beyond the knowledge layer requires more than software. Nexus is a solution: platform plus service. Forward Deployed Engineers embed with your team from day one. They help identify the highest-impact use cases, design agents for your specific workflows, handle integration complexity, provide change management guidance, and continuously optimize performance. This is not premium support. It is an embedded engineering partnership.

This matters because deploying autonomous agents that execute deep business processes is 10% technology and 90% organizational change. The technology works. The hard part is identifying the right workflows, designing for edge cases, getting teams to trust the system, and building momentum from quick wins to enterprise-wide impact. Nexus includes that service layer by design because agents that complete work require a fundamentally different deployment approach than tools that surface information. Forward Deployed Engineers are not optional. They are what makes the difference between deploying a platform and delivering outcomes.

Knowledge-layer integrations vs. execution-layer integrations

Glean connects to 100+ enterprise apps primarily for indexing and search. Its agent capabilities now include 100+ native actions in connected applications, allowing agents to take action within those apps. These integrations serve the knowledge layer: they exist to pull information in for indexing and to take limited actions within connected apps.

Nexus integrations serve the execution layer. Agents operate across 4,000+ enterprise systems with deep bidirectional integration. The same agent can pull data from your CRM, validate it against your ERP, send a message via WhatsApp, update a ticket in your support system, and escalate to a human in Slack — all within a single workflow. Agents deploy across Slack, Teams, WhatsApp, email, phone, and web widgets. One agent, multiple channels, zero code changes.

This is the practical consequence of the knowledge-layer vs. agent-first distinction. A research agent does not just find information about enterprise accounts. It synthesizes data from dozens of sources, identifies patterns, scores buying signals, and delivers structured intelligence. That requires deep integration across many systems simultaneously, orchestrated by an agent that executes a complete process — not a knowledge tool that surfaces information with actions in a few connected apps.

Can you use both Glean and Nexus together?

Yes, and some enterprises do. Glean serves as the knowledge layer — giving every employee a searchable, AI-powered interface to company information. Nexus serves as the execution layer — handling high-volume workflows that require autonomous action, multi-system orchestration, and measurable business outcomes. The two are not inherently in conflict. The question is: once you have Nexus agents running live workflows with direct system access, does maintaining a separate knowledge-layer tool still add enough value to justify the per-seat cost? That depends on how central information search is to your employees' daily work versus how central workflow execution is to your business outcomes.

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Frequently asked questions

Does Nexus replace Glean?

For workflow-heavy use cases, yes. Nexus agents access your enterprise systems directly during workflow execution — pulling live data from CRMs, ERPs, knowledge bases, and databases as part of completing work. That covers the information retrieval Glean provides, while also completing the business process that information points to. That said, Glean is genuinely better for companies where the primary problem is employees searching across fragmented tools for answers. The question worth asking: is your bottleneck finding information, or completing the work that information points to?

We already invested in Glean. Is that wasted?

The organizational learning is not wasted, but the tool can become redundant for teams that move to execution-layer agents. Nexus agents access your enterprise systems directly during workflow execution, pulling live data as part of completing work. That covers information retrieval while also completing the business process. The Glean investment can be redirected toward agents that deliver measurable outcomes — if the bottleneck has shifted from finding information to completing work at scale.

Glean is adding agents. How is that different from Nexus?

Glean has made significant progress with agents. Their agent builder now supports 100+ native actions, MCP host support, Fast and Thinking modes, and sub-agent orchestration. These capabilities make Glean's agents useful for tasks that extend naturally from search and knowledge work.

But adding agent capabilities to a knowledge-layer platform is not the same as building an agent-first platform. The depth of automation a knowledge-layer architecture can handle is bounded by a foundation designed around search, knowledge, and content. Nexus was built agent-first, meaning autonomous execution, exception handling, deep process execution across 4,000+ integrations, and enterprise governance are core architecture — not extensions of a search product.

The other difference is the service layer. Nexus includes Forward Deployed Engineers embedded with your team to ensure agents deliver measurable outcomes. Glean is self-serve software with standard enterprise support. Going beyond the knowledge layer requires that embedded partnership.

How does pricing compare?

Glean uses per-seat pricing starting around $50/user/month, with generative AI features as an additional ~$15/user/month add-on, and a mandatory 10% support fee. Minimum enterprise contracts typically start around $50K–60K/year, with Fortune 500 deals exceeding $5M annually. Cost scales with headcount.

Nexus uses per-agent pricing tied to value delivered. Every Nexus engagement starts with a 3-month proof of concept tied to specific measurable outcomes — you see the ROI before committing to an annual contract. If the POC does not deliver, you walk away. The per-agent model also means cost does not scale with employee headcount, which matters when a single agent handles workflows touching thousands of accounts or customers.

Glean has $200M+ ARR and a $7.2B valuation. Why consider Nexus?

Glean has built an impressive business as a knowledge-layer platform. Their growth validates that enterprises need better ways to access information (Glean $200M ARR announcement, December 2025; $7.2B valuation, Series F, June 2025). But that validation also reveals the boundary: the knowledge layer is necessary but not sufficient for many enterprise workflows. Many enterprises that adopted Glean for search still have a separate, unsolved problem — high-volume workflows that require AI to go beyond finding answers and actually complete work across systems. Both could access information. Neither could execute on it at the scale they needed until they deployed Nexus agents with Forward Deployed Engineers guiding the implementation.

Our team mostly needs better internal search. Should we still consider Nexus?

If the primary problem is employees struggling to find information across company tools, Glean is likely the better fit. It is a strong knowledge-layer platform, and that is a real problem worth solving. Nexus is built for a different problem. The question worth asking: does finding information faster actually resolve your bottleneck, or does the real cost come from what happens after the information is found? If teams spend hours on research, synthesis, validation, and multi-step processes that cross systems, you need AI that goes beyond the knowledge layer. That is where Nexus agents and Forward Deployed Engineers deliver.

What about Glean's Enterprise Graph and enterprise context?

Glean's Enterprise Graph — combining memory, connectors, indexes, personal and enterprise graphs, and governance — is a strong foundation for understanding company context. It helps their assistant and agents reason about work with real understanding of organizational structure and knowledge. This is the knowledge layer at its most sophisticated.

Nexus approaches context differently because agents that execute deep business processes need different things than tools that surface knowledge. Rather than building a separate knowledge graph, Nexus agents connect directly to 4,000+ systems and access live data during execution (real-time RAG), combined with vectorized knowledge bases for stable company knowledge. This means agents work with current data, not data that was last indexed. For workflows where data freshness matters — live CRM data, real-time system checks, current inventory — direct system access avoids the index sync delays that can introduce uncertainty. When an agent is making decisions and executing actions autonomously, it needs real-time data, not last-indexed data.

Is Glean worth the price for large enterprises?

Glean's minimum contracts start around $50K–60K/year, and Fortune 500 deals can exceed $5M annually once generative AI add-ons, the mandatory support fee, and user count are factored in. Whether that is worth it depends entirely on the problem: if scattered information is genuinely costing the organization in lost productivity and decisions made on incomplete data, Glean has strong ROI. If the real cost comes from workflows that cannot be executed at scale — not from information that cannot be found — the same budget delivers more measurable return deployed toward execution-layer agents.

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Worth exploring?

If your team already has access to information but the bottleneck is completing the work that information points to, it might be worth seeing how enterprises are going beyond the knowledge layer. Teams have built agents that autonomously analyze thousands of enterprise accounts. Others have achieved 50% conversion improvement and $4M+ yearly revenue with agents that complete customer onboarding end-to-end. Neither needed better search. Both needed agents that execute.

Every engagement starts with a 3-month proof of concept tied to specific outcomes. Forward Deployed Engineers embed with your team from day one. You see results before committing, and you can exit anytime.

[See how enterprises built their agent fleets →] (case studies)

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