Glean vs Agentspace
Today, we’ll compare two well-known AI products: Glean and Google Agentspace. We’ll also offer an alternative to either tool.
In a Snapshot
Glean excels at AI-enhanced search but has limited automation features. Agentspace promises sophisticated agent workflows but remains largely vaporware. Glean is ideal for organizations that strictly need search, while Agentspace is best for companies that strictly use Google products—but most enterprises should consider other alternatives instead.
Glean is Search-First. Agentspace is Agent-First.
Glean and Agentspace are solving different problems
Glean and Agentspace emerged from two fundamentally different enterprise problems.
Glean helps consolidate enterprise data that’s sprawled out between tools in one collective search. For example, a sales team might need to source candidates, check references, and schedule interviews. With Glean, they can search across all of their tools. Glean also has some limited automations—for example, a sales rep can trigger an automation that’ll theoretically surface related candidate information.
Google Agentspace, meanwhile, is focused on enabling specialized AI agents to collaborate. To theoretically solve the same problem, a sourcing agent would identify a candidate, a scheduling agent would coordinate interviews, and a reference-checking agent would validate backgrounds. Unlike Glean where the human drives most of the process, Agentspace delegates work to discrete agents.
However, a keyword is "theoretically." While Glean and Google Agentspace have aspirational AI-infused product visions, they scarcely work in practice. Let’s discuss these shortcomings in-depth.
Glean: Search That Works, Agents That Don’t
Glean was born as an enterprise search product and only later added AI capabilities. The company was built to solve information sprawl across SaaS applications.
Glean’s unified search interface understands context, language, behavior, and employee relationships through a sophisticated knowledge graph that maps who knows what, who works with whom, and how content relates across the organization. Real-time permission inheritance ensures users only see authorized data, mirroring access controls from underlying sources like Google Workspace, Microsoft 365, Slack, Salesforce, Jira, and Confluence. The value proposition is clear: reduce context-switching and search time by creating a single, intelligent interface across the entire enterprise information ecosystem.
However, Glean has rebranded as a robust AI agent product. This is a bit misleading. Glean only supports single-agent workflows with basic automation. This means agents can’t maintain memory, communicate with each other, or handle multi-step processes. In a nutshell, Glean agents only provide basic “if, then” automation. As such, agent features feel retrofitted onto the core search functionality rather than designed as first-class capabilities.
Agentspace: Compelling Vision, Limited Functionality
The vision of Google Agentspace is for an “agent-driven enterprise” where specialized AI agents collaborate across systems. Agentspace aims to automate task completion through multi-agent orchestration.
The platform’s core components reflect this agent-first thinking. The Agent Galley provides employees a unified view of available agents across the enterprise, including Google’s offerings, internal team creation, and partner solutions. Agent Designer offers a no-code interface for creating custom agents connected to enterprise data sources. Most importantly, the architecture is built to support Google’s Agent2Agent (A2A) protocol for cross-vendor agent collaboration.
Agentspace constructs its own enterprise knowledge graph, but unlike Glean’s search-oriented mapping, this graph connects employees with teams, documents, software, and data to enable autonomous task execution. The platform integrates with popular work applications like Box, Confluence, Google Drive, Jira, Microsoft SharePoint, and ServiceNow, but positions these connections as action endpoints rather than search sources. However, it’s primarily compatible with Google’s own stack, and a lot of other integrations involve hoop-jumping as they’re built and maintained by third parties outside of Google.
There is a bit of marketing hype at play here. Agentspace remains available only via allowlist after months of “general availability” announcements. The platform promises sophisticated agent workflows but delivers primarily third-party agent wrappers with limited customization. While Agentspace supports the A2A protocol for agent communication, the actual agents available through Google Cloud Marketplace provide very basic functionality. Most “agent collaboration” scenarios require extensive custom development rather than the promised no-code experience.
That said, despite its limited features, Agentspace has an amazing UI, with the feel of a consumer product despite being designed for businesses.
Neither Glean or Agentspace Deliver Complete Solutions
Organizations can use both Glean (for search) and Agentspace (for agent orchestration) simultaneously. They are complimentary. Glean excels at information discovery but leaves enterprises wanting for meaningful automation. Agentspace promises sophisticated agent workflows but lacks the robust data integration and search capabilities.
Meanwhile, platforms like Credal suggest a potential path forward. Credal has an agent-first architecture with multi-agent workflows and persistent memory. This set-up can deliver the sophisticated automation capabilities that both Glean and Agentspace advertise but currently lack. We believe that a platform like Credal is where the enterprise AI market will ultimately land: an unified place to combine robust data integration with native agent orchestration from the ground up.
Agent2Agent Protocol is bigger than just Agentspace
Beyond Google Agentspace is the protocol that powers it: Agent2Agent (A2A) Protocol. We believe that this protocol, which has applications outside of Agentspace, will eclipse the impact of Agentspace long-term. A2A is backed by 50+ technology partners, including Atlassian, Box, Cohere, Intuit, Salesforce, SAP, ServiceNow.
The goal of A2A is to embrace an agent’s natural capabilities. Built on existing standards such as HTTP, SSE, and JSON-RPC, A2A enables agents to collaborate while retaining autonomy and privacy over each’s memory, tools, and context. A2A treats agents in the same capacity that managers would treat employees—diverse individuals with the capacity to work together.
Notably, Glean isn’t designed for an A2A ecosystem. It was built around a single agent mindset, and its search-first design doesn’t support agent-to-agent communication. However, while Credal and Glean have some overlap in the way they treat permissions, governance, and integrations, Credal is a native AI product and was designed to support multi-agent workflows from day 1. At the same time, Credal is also more robust than products like Agentspace, which promise lofty features, but it is mostly designed to wrap third-party agents instead of providing tooling for building truly custom, first-party agents.
Decision Framework: When Each Platform Makes Sense
The decision between Glean and Agentspace ultimately depends on your organization’s current pain points, technical sophistication, and strategic vision for AI transformation.
When to Choose Glean: Immediate Search & Synthesis Needs
Best for: Organizations suffering from information sprawl and context-switching inefficiencies. If your employees constantly search across five or more sources of information, losing productivity to data scattered messily across locations, Glean delivers immediate value through its universal search interface. For instance, Engineering teams hunting for documentation across Confluence, GitHub, and Slack. Or sales teams piecing together account history from Salesforce, email threads, and Slack channels.
Technical requirements: Glean is designed for established SaaS stacks with inheritable permission structures.
When to Choose Google Agentspace: Agent-First Transformation
Best for: Organizations ready to rebuild workflows around AI agent collaboration rather than simply enhancing existing processes: agent-first vision, complex process automation needs, technical sophistication, and future-proofing strategies.
For instance: multi-agent research projects combining deep research agents with idea generation agents, or workflow orchestration across enterprise systems, where agents coordinate actions and handoffs automatically.
Potential upside: Even though Google Agentspace presently has very limited functionality, it’ll eventaually allow agents to collaborate between Google’s ecosystem and external platforms like Salesforce Agentforce or Microsoft Copilot. Long-running tasks requiring hours or days with human-in-the-loop approvals align well with A2A’s design for persistent, collaborative workflows.
Technical considerations: Integration heavily favors organizations already committed to the Google Cloud Platform, where Agentspace leverages VPC-SC and IAM controls natively. However, enterprises not on Google Cloud may want to consider platforms like Credal that provide multi-agent infrastructure without vendor lock-in to Google’s ecosystem.
Reality check: Teams must be capable of designing and managing agent workflows using Agent Designer, understanding the A2A protocol, and investing in organizational change management. This is a plug-and-play intended solution without a plug-and-play experience.
Most Enterprises Need Neither
The truth is that most enterprises evaluating these platforms may not need either solution. Organizations with straightforward search problems might achieve similar results with enhanced Microsoft 365 or Google Workspace search capabilities at a fraction of Glean’s cost. Companies interested in agent automation might find better value in purpose-built platforms like Credal that deliver working multi-agent capabilities without Agentspace’s execution limitations and procurement complexity.
Before committing to either platform, enterprises should honestly assess their technical capabilities, risk tolerance, and vision for AI transformation. The choice between Glean and Agentspace often reflects a deeper strategic question: do you optimize current workflows with AI enhancement, or do you rebuild workflows around AI collaboration?
Implementation Reality: What You’re Actually Buying
Understanding the true cost of these platforms requires looking beyond published pricing. Instead, you need to consider implementation complexity, hidden expenses, and long-term value propositions.
Glean: High Cost, Questionable ROI
Pricing structure: Glean’s pricing reflects its enterprise positioning, with no publicly available pricing that requires sales engagement for every evaluation. Enterprise contracts often reach seven figures, with per-user costs up to $600 annually for comprehensive deployments. Notably, support fees total 10% of annual recurring revenue and cannot be removed from contracts.
Technical implementation: Despite Glean’s claims of quick deployment, complex enterprise environments require substantial configuration across multiple systems and permission structures.
ROI reality: While Glean claims companies recover their investments in under six months because of productivity gains, the actual ROI is unclear. This is especially true for agent capabilities, where the value proposition becomes much weaker. Organizations paying premium prices for “agent orchestration” often discover they’re getting basic workflow automation that doesn’t justify the cost.
Agentspace: Theoretical Architecture, Practical Limitations
Pricing chaos: Beyond allowlist-only availability creating months-long procurement delays, Agentspace pricing remains opaque and wildly inconsistent. The platform offers two tiers, Agentspace Enterprise and Agentspace Enterprise Plus, but actual pricing varies dramatically based on Google’s strategic priorities and customer relationship status.
Some enterprises receive effective free access through discounted GCP bundles or strategic partnership agreements while others face steep trial fees, with reports of $40,000 charges for 200-person pilots.
Technical requirements: While Agent Designer provides no-code interfaces for basic tasks, meaningful implementations require substantial investment in A2A protocol. Teams need specialized expertise in agent communication patterns, state management, and Google Cloud infrastructure.
Integration complexity: Despite the platform promising cross-vendor agent collaboration, connecting to non-GCP systems requires understanding multiple agent APIs, managing complex authentication protocols, and architecting distributed error handling, precisely the complexity enterprises hoped to avoid.
Potential upside: Agentspace is a long-term bet and, as such, the long-term value proposition remains theoretical. For organizations willing to make early investments, Agentspace offers potential advantages in scalability and future-proofing, particularly for enterprises already committed to Google Cloud infrastructure and willing to bet on the A2A protocol becoming an industry standard.
The Hidden Costs Both Platforms Share
Beyond platform-specific costs, both Glean and Agentspace share significant hidden implementation expenses that enterprises often underestimate. Integration complexity requires dedicated technical resources regardless of platform choice. Change management costs multiply when introducing new AI workflows across large organizations. Training requirements extend beyond initial deployment, requiring ongoing education as platforms evolve rapidly.
Most critically, both platforms create lock-in effects that increase long-term costs. Glean’s comprehensive data indexing makes migration difficult and expensive. Agentspace’s agent workflows create dependencies on Google’s ecosystem that complicate future platform decisions. Organizations choosing either platform should budget not just for initial implementation but for the long-term strategic implications of their architectural decisions.
What Multi-Agent Workflows Actually Require
Enterprise implementations of multi-agent systems demonstrate the technical sophistication required for meaningful automation. Capital One’s four-agent system illustrates the complexity of real-world agent orchestration, with four specialized agents working in coordination: a communication agent handling customer interactions, a planning agent structuring workflow sequences, an accuracy evaluation agent validating outputs, and a validation agent ensuring compliance with financial regulations.
This four-agent architecture delivers measurable results. Auto dealers using Capital One’s chat concierge system report improved customer engagement metrics up to 55%, demonstrating how properly orchestrated agents can enhance business outcomes. However, the technical implementation required extensive custom development, specialized expertise in agent communication protocols, and months of integration work across multiple enterprise systems.
Technical architecture patterns:
- Shared scratchpad: Agents collaborate on persistent message streams where all work is visible. Requires a careful message schema design and state synchronization.
- Hierarchical supervision: Orchestrator agents coordinate specialized sub-agents with sophisticated error handling. Demands precise API design and rollback capabilities when downstream agents reject upstream outputs.
- Bidirectional data flows: Unlike Glean’s read-only model, working systems require agents to read from source systems, write back results, and maintain consistency across platforms.
Why Glean and Agentspace Both Exhibit Lock-in Effect
Glean’s data dependency: Once enterprise data is indexed into Glean's knowledge graph, migration becomes expensive. Teams develop search workflows that are difficult to replicate elsewhere.
Agentspace’s ecosystem bet: Agent workflows create dependencies on Google’s infrastructure and A2A protocol adoption. If either assumption fails, migration costs multiply exponentially.
Why patience is your ally: Enhanced Microsoft 365 or Google Workspace search handles discovery needs at a fraction of the cost. Purpose-built platforms like Credal deliver working multi-agent capabilities without architectural compromises or vendor lock-in.
Glean’s Enterprise Search Implementation Patterns
Glean’s real-world deployments reveal different technical challenges and success patterns. An enterprise implementation may involve a company with 10,000+ employees spread across engineering, sales, marketing, and operations teams with information sprawled across hundreds of applications: multiple Slack workspaces, Confluence spaces, Google Drive folders, Salesforce customizations, Jira tickets, etc.
This technical implementation would require months of dedicated engineering work to configure permissions mirroring across all systems, establish proper data indexing pipelines, and tune the knowledge graph for organizational relationships. Despite Glean’s pre-built connectors, each integration demands custom configuration to handle complex permission hierarchies, especially around sensitive financial data, customer information, and proprietary technical documentation.
Successful deployments show measurable productivity gains. Employee search time commonly decreases significantly with particularly strong adoption among customer-facing teams who previously may have spent significant time hunting for account information across multiple systems. However, attempts to extend beyond search into workflow automation consistently reveal Glean’s technical limitations; the platform’s single-agent architecture cannot handle complex multi-step processes that require coordination between different business systems.
Financial services requirements expose the technical challenges of enterprise agent deployment. Stringent regulatory compliance demands holistic visibility across the entire customer journey workflows, requiring agents to maintain detailed audit trails, handle sensitive data appropriately, and integrate seamlessly with existing compliance monitoring systems. These requirements push beyond simple workflow automation into complex technical territory that few platforms handle effectively.
Integration Complexity: Technical Reality Check
Gleans Integration Approach
Glean’s technical integration promises simplicity but delivers complexity in practice. While the platform’s 100+ pre-built connectors suggest straightforward integration, enterprise deployments require substantial engineering resources to handle complex permission structures, custom data models, and enterprise-specific security requirements. Permission mirroring, though automated through OAuth and similar authentication protocols, demands careful configuration across multiple systems.
However, this straightforward setup comes with technical limitations. Customization options remain constrained to Glean's predefined workflows and data models. Organizations with complex permission hierarchies, custom data structures, or unique workflow requirements often discover that Glean's simplified approach cannot accommodate their technical needs without significant workarounds or data transformation layers.
The technical architecture reflects Glean's search-first design philosophy. Data flows remain unidirectional from enterprise systems into Glean's knowledge graph, with limited capabilities for writing data back to source systems or triggering complex multi-system workflows. This architectural constraint makes Glean unsuitable for sophisticated automation scenarios that require bidirectional data synchronization or complex workflow orchestration.
Agentspace’s Technical Architecture
Agentspace presents a more complex but flexible technical integration model. Agent Designer provides no-code interfaces for basic agent creation, but sophisticated implementations require an understanding of the A2A protocol, agent communication patterns, and Google Cloud infrastructure. The Vertex AI Agent Development Kit offers advanced capabilities for technically sophisticated teams willing to invest in custom agent development.
The platform's cloud-native architecture leverages Google Cloud Platform's VPC-SC and IAM systems natively, providing enterprise-grade security and compliance capabilities for organizations already committed to Google's ecosystem. However, enterprises using multi-cloud or hybrid architectures face additional integration complexity when connecting Agentspace agents to non-GCP systems.
Ecosystem integration capabilities represent Agentspace's most compelling technical advantage. The platform can integrate agents from Google Cloud Marketplace and external platforms through the A2A protocol, enabling sophisticated cross-vendor agent collaboration. However, this flexibility requires technical teams to understand multiple agent APIs, manage complex authentication protocols across vendors, and architect robust error handling for distributed agent systems.
The Credal Alternative
For most organizations, the fundamental question shouldn’t be choosing between Glean and Agentspace: neither delivers working agent capabilities for enterprise workflows! Both platforms suffer from critical architectural limitations that force enterprises into expensive compromises.
However, Credal offers a solution that unites the best of both Glean and Google Agentspace.
Credal’s Agent & MCP First Approach
Credal eliminates the architectural compromises that plague both Glean and Agentspace. The platform enables companies to build agentic workflows with multiple agents working together, where each agent specializes in specific roles with focused context windows.
The practical advantages compound quickly. While Glean and Agentspace both try to lock you into their proprietary chat interface, Credal offers Agents and MCP servers that can be interacted with in virtually any third-party app, from ChatGPT to Slack. Credal offers more abundant pre-built actions for agents compared to Glean’s limited automation capabilities. As an AI-first company, Credal prioritized agentic aspects from initial architecture rather than retrofitting agent features onto existing search infrastructure.
Credal is also an easier product to buy. Evaluation processes are streamlined with 30-day windows and no upfront billing commitments, eliminating the procurement complexity that hampers Agentspace adoption. Transparent pricing based on seat usage, data volume, and AI consumption eliminates the budget uncertainty that complicates Glean and Agentspace procurement.
Enterprise-ready features distinguish Credal from theoretical platforms. The system supports both high-touch agents requiring human oversight and orchestrator-driven automations for routine workflows. Robust APIs enable real-time and batch processing patterns that enterprise architectures demand. Custom assistants integrate with specific knowledge bases and tasks without forcing organizations into rigid platform constraints.
In a nutshell, Credal provides a mature approach to the agent-based future, and substitutes fluffy promises for well-designed, robust features.
