If data is the raw material of AI, metadata is the blueprint that makes it usable.

Think of it like building a city‌. Data is the concrete, steel, and wood, whereas metadata is like the architectural diagram that determines how those materials become roads, buildings, or houses. Without that framework, cities (and AI systems) would be chaotic and inefficient, and even lead to risks.

Metadata is the linchpin of a successful AI ecosystem, enabling AI systems and agents to interpret, adapt, and scale across vast and complex datasets.

Srinivas Tallapragada, President and Chief Engineering and Customer Officer 

Imagine AI agents operating without a way to interpret the data they process, or unable to collaborate and share context with other agents to execute complex workflows as the AI ecosystem grows. Metadata is the linchpin of a successful AI ecosystem, enabling AI systems and agents to interpret, adapt, and scale across vast and complex datasets. In turn, it shapes how businesses, developers, and consumers interact with these intelligent systems:

• For businesses, a well-structured metadata framework enhances automation, personalization, and decision-making by ensuring AI can efficiently understand relationships within data.
• For developers, a metadata framework allows them to focus on innovating AI-powered applications rather than being bogged down in the complexities of the underlying infrastructure and data management.
• For consumers, metadata will make AI more personalized, relevant, and helpful.

Metadata is not just a technical detail. It’s the foundation upon which the future of AI innovation and scalability will be built.

Building a metadata framework

As AI becomes more embedded into enterprise applications and organizations build their digital labor of AI agents, a scalable metadata framework is crucial.

Here are some key principles for building one that guides dynamic decision-making, real-time data processing, and multi-agent collaboration: 

• ​​Abstraction drives agility: A metadata-driven approach abstracts complexity, enabling AI applications to evolve without requiring constant manual updates to code, schemas, or logic. For example, Salesforce’s metadata framework is an abstraction layer over its internal infrastructure and technology, which is why applications built a decade ago can still work today even as we’ve continued to transform and embrace new technologies. As AI applications grow in complexity, the metadata framework should be able to evolve dynamically to support new data types, entities, and relationships without disrupting existing systems.
• Common standards are critical for agent collaboration: Effective AI agents don’t operate in isolation‌ — they need to collaborate, exchange knowledge, and make decisions together. To facilitate this, a standardized metadata layer is vital for ensuring that different agents can communicate and share relevant information with common language. Establishing common standards within the metadata framework — like shared data models or communication protocols — can ensure smoother cross-agent collaboration and more coherent decision-making across systems. 
• Structured metadata provides organization: Metadata can be structured to make it more intuitive and understandable for humans, as well as machine-readable for low- to pro-code tools to build apps that work with data, actions, business logic, access controls, and more. For example, our Salesforce Object metadata for defining data models has structured metadata that defines a human-friendly localizable label, a developer-consistent API name, and well-defined data types that AI agents can understand. AI agents can iterate and evolve with greater accuracy and relevance to market demands using structured metadata rather than custom code. 
• Strongly-typed metadata ensures consistency: Strongly-typed metadata means it organizes data into predefined structures, such as entities (representations of real-world objects within a system, such as an account or product), fields, and records. For example, instead of interacting with raw data directly through our data store technology, customers interact through our data abstraction via Salesforce Object APIs (sObjects) and Salesforce Object Query Language (SOQL). This makes it easier for users to access and manipulate data in a way that ensures consistency and security.

Powering a growing agent ecosystem

At Salesforce, we deliver a deeply unified platform that combines the power of Agentforce, Data Cloud, and Customer 360 applications, so CIOs, developers, and partners can build autonomous AI agents that will collaborate with humans to achieve customer success. And metadata has always been the foundation of our multi-tenant architecture‌ — ‌powering our deeply unified platform with hundreds of thousands of businesses and millions of users to tailor software to their needs without complex code. With the rise of AI-driven applications and the goal of reaching 1 billion agents with Agentforce by the end of 2025, the role of metadata has grown exponentially.

Already, 93% of enterprise IT leaders have implemented or plan to implement AI agents in the next two years. Customers like SharkNinja and Vivint are rapidly adopting Agentforce, deploying autonomous AI agents to boost efficiency, enhance productivity, and scale personalized customer service.

To enable at least 1 billion agents, Salesforce’s metadata framework supports layered extension on its deeply unified platform, which allows different levels of users and developers to customize and extend Salesforce functionality using metadata. It’s like building with Lego blocks — Salesforce provides the foundation, and others can build apps and agents on top of it without rewriting software from scratch and compromising security.

The layered extension approach is a key reason why a growing ecosystem of partners, ISVs, and developers are building on top of the Salesforce Platform.

Shaping the future of AI agents

 As we look to the future of AI, metadata will remain the essential for building and deploying increasingly complex and capable AI systems.

This foundation allows industries to continue pushing the boundaries of what’s possible in AI, creating an ecosystem of businesses and developers that can unlock new opportunities with agents while maintaining the highest standards of performance, security, and customization.

More information:

• Read about the new AgentExchange 
• Dive deeper into Salesforce’s approach to metadata
• Learn how Salesforce’s platform manages massive data and AI workloads at scale
• Learn about the evolution of Salesforce’s platform

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