The market for AI agents is booming — and it’s just getting started. According to a 2024 McKinsey report, the market for AI agent applications across customer service, sales, operations, and IT support is expected to exceed $100 billion by 2030. Business and technology leaders are rapidly embracing AI agents to automate tasks, enhance decision-making, and create seamless interactions across digital and human channels.
Amidst this explosion, two distinct categories of AI agents are emerging:
- Embedded agents, seamlessly integrated into existing enterprise platforms.
- Universal agents, developed to operate across diverse systems and workflows.
This article explores this critical divide by comparing two landmark examples: Agentforce from Salesforce, and Agentspace from Google Cloud.
Embedded Agents: Agentforce by Salesforce
Agentforce is Salesforce’s embedded AI agent framework, designed to supercharge user productivity across its Sales, Service, and Marketing Clouds. Powered by Einstein GPT and native CRM data, Agentforce deploys autonomous agents that can automate tasks, recommend next-best actions, or even interact with customers directly. These agents operate safely within Salesforce’s governance, security, and compliance standards — making them ideal for organizations seeking low-risk, high-impact AI augmentation without leaving their CRM ecosystem.
The promise of Agentforce is simple: smarter workflows, better outcomes, minimal disruption.
Universal Agents: Agentspace by Google Cloud
Agentspace is Google Cloud’s flexible development environment for creating powerful, domain-specific AI agents. It allows organizations to orchestrate multiple AI models (including Gemini and open-source options), integrate enterprise data sources, and define complex multi-step task flows. Agents built with Agentspace can operate across cloud, on-premise, and SaaS environments, offering vast flexibility.
Unlike embedded solutions, Agentspace demands a stronger technical investment — but in return, it enables businesses to build sophisticated, enterprise-wide AI capabilities tailored to their unique environments and strategies.
Comparison Table: Agentforce vs. Agentspace
| Feature | Agentforce (Salesforce) | Agentspace (Google Cloud) |
| Primary Focus | Enhancing customer service, sales, and marketing productivity within Salesforce. | Building custom, autonomous AI agents for a wide range of enterprise applications. |
| Target User | Business users (e.g., customer service agents, sales managers) and Salesforce administrators. | Developers, AI engineers, and cloud architects. |
| Integration | Deep integration within the Salesforce CRM and Service Cloud environment. | Flexible integration across cloud services, APIs, and enterprise systems. |
| Core Functionality | Real-time assistance, task automation, knowledge suggestions, next-best actions, customer interaction support. | Multi-step task execution, information retrieval, workflow orchestration, natural language understanding, integration with external systems. |
| Customization Level | Configuration and workflow adjustments within Salesforce boundaries. | High customization — full control over logic, models, tools, and integrations. |
| AI Foundation | Salesforce Einstein GPT and proprietary AI models tied to CRM data. | Google’s Gemini models, open-source models, and Google’s AI/ML toolsets. |
| Use Cases | Customer support, case management, sales assistance, marketing automation. | Custom automation, digital assistants, chatbots, research agents, operational task agents. |
| Development Effort | Lower — focuses on using built-in features and low-code tools. | Higher — requires development, model orchestration, and cloud engineering expertise. |
| Autonomy Level | Primarily agent-assisted — supports and augments human workflows. | High autonomy — agents capable of independent, multi-system operations. |
| Ecosystem | Salesforce platform ecosystem. | Google Cloud Platform and broader multi-cloud and SaaS ecosystem. |
| Deployment Speed | Fast — minimal technical lift for Salesforce customers. | Moderate to slow — requires planning, development, and integration efforts. |
| Scalability | Scales within Salesforce CRM and Service Cloud contexts. | Cloud-native scaling across diverse workloads and environments. |
| Governance & Risk Management | Strong governance built into Salesforce’s compliance and security frameworks. | Flexible — enterprises must implement their own governance, security, and monitoring. |
| Cost Considerations | Predictable — tied to Salesforce licensing models. | Variable — dependent on cloud consumption, storage, and AI compute costs. |
Conclusion: Choosing the Right Path for AI Agents
Selecting between Agentforce and Agentspace ultimately depends on your organization’s strategic priorities, technology landscape, and appetite for AI innovation.
Choose Agentforce when:
- You are heavily invested in Salesforce and want seamless AI augmentation within existing sales, service, and marketing operations.
- Your primary goal is to boost employee productivity, reduce response times, and enhance customer satisfaction through AI-powered guidance and automation.
- You need quick deployment with minimal technical lift, leveraging Salesforce’s built-in governance, security, and compliance frameworks.
- Your focus is on agent enablement — empowering human workers with intelligent tools, not fully automating complex workflows.
Key takeaway: Agentforce is ideal for organizations seeking trusted, low-risk AI augmentation embedded into the Salesforce environment, driving faster operational improvements without disrupting existing workflows.
Choose Agentspace when:
- You require highly customized, autonomous AI agents capable of performing complex, multi-system tasks.
- Your ambition is to build AI solutions that extend far beyond CRM — into operations, product development, knowledge work, and customer experience innovation.
- You have technical resources (developers, AI engineers, cloud architects) to design, deploy, and govern sophisticated agent architectures.
- You want to leverage Google’s powerful AI models and enjoy the flexibility to orchestrate bespoke workflows across your technology stack.
Key takeaway: Agentspace is suited for enterprises seeking strategic AI innovation — building differentiated, high-autonomy agents that drive transformation across the business, provided the necessary expertise and investment are in place.
Annex: Extending the Comparison Beyond Salesforce and Google
While this article focuses on the comparison between Salesforce’s Agentforce and Google Cloud’s Agentspace, it is important to note that similar considerations apply when evaluating other leading AI agent solutions.
Other Embedded AI Agent Platforms:
- ServiceNow: ServiceNow’s Generative AI Controller and Virtual Agent capabilities offer embedded AI assistance across IT Service Management, HR, and customer workflows.
- SAP: SAP’s Joule AI integrates conversational AI into ERP, CRM, and supply chain systems to drive workflow automation and insights.
- Oracle: Oracle’s Digital Assistant delivers pre-built AI agents across CX, HCM, and ERP applications with deep process integration.
- Pega: Pega’s AI and decisioning capabilities embed adaptive intelligence into customer service, case management, and workflow automation platforms.
These embedded solutions share common traits:
✅ Deep integration within their respective ecosystems.
✅ Faster deployment with lower technical lift.
✅ Strong governance, but less flexibility outside the platform boundaries.
Other Customizable AI Agent Platforms:
- Microsoft Azure AI: Azure OpenAI Service, Azure AI Studio, and Cognitive Services provide a robust environment to build, orchestrate, and deploy enterprise-grade AI agents across the Microsoft ecosystem and beyond.
- AWS AI Services: AWS offers powerful capabilities through Bedrock, SageMaker, and Lambda-based orchestration to create highly customized and scalable AI agents.
- IBM Watson: IBM Watsonx platform enables businesses to build domain-specific AI agents with strong support for enterprise governance, regulatory compliance, and hybrid cloud environments.
These customizable platforms share characteristics:
✅ High degree of design flexibility and autonomy.
✅ Potential for cross-system orchestration at enterprise scale.
✅ Require greater investment in technical resources, governance, and lifecycle management.
Key Takeaway:
Whether you evaluate embedded solutions or custom platforms, the core decision principles remain the same:
- Speed and simplicity versus flexibility and autonomy.
- Platform integration versus ecosystem-wide orchestration.
- Operational augmentation versus strategic innovation.
As the agent economy matures, technology leaders must align their agent strategy with their overall digital transformation roadmap, balancing immediate needs with future ambitions.
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