Behind the Scenes: Understanding .do Data Models for Agents and Services

Building reliable and powerful applications on any platform requires a solid understanding of its foundational architecture. On the .do platform, this foundation is built upon clearly defined data models that govern how Agents, Workflows, and Services-as-a-Software interact and exchange information.

While the high-level concepts of Agentic Workflows might seem abstract, diving into the underlying structure is crucial for developers and users who want to leverage the platform to its full potential. This is where the canonical reference documentation, accessible through reference.do, plays a vital role.

What Are Data Models and Why Do They Matter?

In the context of the .do platform, data models are the blueprints that define the structure and relationships of the core components and the data they process. Think of them as the API contracts and object schemas that dictate what information is available, how it's organized, and how different parts of the system communicate.

Understanding these models is essential because they provide:

• Predictability: Knowing the expected input and output formats (like those referenced in the .do documentation) allows you to build integrations and orchestrate workflows with confidence.
• Robustness: Adhering to the defined data models helps prevent errors and ensures your Agents and Services function correctly within the platform's ecosystem.
• Clarity: They offer a precise technical language for discussing and designing complex agentic workflows.
• Integration: Data models define the clear interfaces necessary for building Services-as-Software that can be seamlessly incorporated into workflows.

Key Data Models on the .do Platform

The .do platform organizes its core functionalities around several key concepts, each represented by specific data models. While the full details are in the agentic workflow reference, let's touch upon the primary ones:

1. Agents: The fundamental units of action. Their data model likely defines their identity, configuration parameters, capabilities, and potentially their state. Understanding the Agent model is key to configuring and deploying individual task executors.
2. Workflows: The orchestrators of Agents and Services. The Workflow data model describes the sequence of steps, the agents or services involved in each step, control flow logic (like branching or looping), and the overall input and output structure of the entire flow. This model is critical for designing and executing complex multi-step processes.
3. Services-as-Software: Represents external or reusable logic wrapped with a clear interface. The Service data model focuses on defining the input parameters, expected output structure, versioning, and potential authentication/authorization details for interacting with the service via its services-as-software API.

Data Models in Action: The Flow of Information

When an agentic API call triggers a Workflow, the platform manages the execution based on the definitions in these data models. Data flows between steps, agents, and services according to the specified schemas.

Consider the output of a single step executed by an agent within a workflow. The platform needs a standard way to report the result. This is where a data model like AgentStepOutput comes into play:

interface AgentStepOutput {
  status: 'success' | 'failure'; // Did the step succeed or fail?
  output: any; // The actual data produced by the step
  agentId: string; // Which agent executed this step?
}

And similarly, the overall outcome of the entire workflow execution is captured by a AgentWorkflowOutput model:

interface AgentWorkflowOutput {
  status: 'completed' | 'failed'; // Did the workflow finish successfully?
  steps: AgentStepOutput[]; // A list of the results for each step
  finalOutput: any; // The combined or final result of the workflow run
}

These TypeScript interfaces, often used as data model definitions, provide the blueprint for how execution results are structured and can be consumed by other parts of the workflow or external systems. They are part of the detailed business-as-code reference available through the platform's documentation.

Leveraging the Canonical Reference

The most comprehensive source for understanding the .do platform's data models, API documentation, and interaction patterns is the official reference documentation. It provides detailed specifications, schemas, and examples that go far beyond this brief overview. Whether you are implementing a new Agent, designing a complex Workflow, or integrating an external Service-as-Software, referring to this documentation is your most reliable path to success.

By exploring the reference, you gain insights into:

• Specific API endpoints and their parameters.
• Detailed schemas for request and response bodies.
• Exact definitions of data structures like those illustrated above.
• Best practices for interacting with the platform programmatically.

Conclusion

Thinking "behind the scenes" about the data models that power the .do platform's Agentic Workflows and Services-as-Software is a critical step towards building robust, scalable, and predictable applications. These models define the language and structure of interaction within the ecosystem.

Don't leave your understanding to chance. Dive into the comprehensive reference documentation at https://reference.do to explore the definitive source for the platform's data models, API specifications, and more. Master the blueprints, and unlock the full potential of Agentic Workflows on the .do platform.