Discovery and Execution: How AI Agents Use the UIM Protocol to Perform Actions

One of the biggest challenges AI agents face today is figuring out how to interact with web services reliably and securely. Traditionally, they’ve had to rely on methods like web scraping or manually crafted API integrations. But these approaches are messy, inconsistent, and often insecure. The Unified Intent Mediator (UIM) protocol changes this by introducing a standardized way for AI agents to discover and execute actions on web services. At the heart of this system are discovery and execution endpoints, which guide agents from finding what they need to performing the desired action, all within a secure and efficient framework.

Discovery Endpoints: Finding What’s Available

Discovery endpoints are like search engines for AI agents, but instead of searching the web, they search for available intents. When an AI agent wants to know what actions it can perform on a web service, it queries the discovery endpoint. The discovery endpoint responds with a list of intents—structured actions that the service can execute, like “get weather data,” “book an appointment,” or “process a payment.”

The discovery process starts when an AI agent sends a query to the discovery endpoint of a web service. This query can include filters or keywords to help narrow down the search. For example, if an AI agent is looking for intents related to financial transactions, it can specify that in its query. The discovery endpoint responds with a detailed list of matching intents, each accompanied by metadata that describes what the intent does, what parameters it requires, and how it works.

Each intent in the list comes with critical information:

• Intent Name and Description: Tells the AI agent what the action is and what it does.

• Parameters: Lists the required and optional inputs needed to perform the action.

• Execution Endpoint URL: Provides the specific address where the action can be performed.

This information allows the AI agent to quickly identify the actions that are available and decide which one fits its needs. Discovery endpoints eliminate the guesswork, giving AI agents a clear, reliable way to find the functionalities they’re looking for.

Execution Endpoints: Performing the Action

Once the AI agent has identified the right intent through the discovery process, the next step is execution. This is where the execution endpoint comes into play. The execution endpoint is the point of action—the place where the AI agent sends the necessary parameters and gets back the results.

Think of the execution endpoint as the checkout counter in a store. The AI agent shows up with the required inputs, and the web service processes the request, performing the action specified by the intent. The execution endpoint handles all the backend work, from validating the inputs to executing the action and formatting the response.

Here’s how it works in practice:

1. Validation: The execution endpoint first checks the parameters provided by the AI agent. Are they complete? Do they match the expected types? This validation step helps catch errors early and ensures that the request is properly formatted.

2. Processing: After validation, the execution endpoint carries out the action. This could involve querying a database, making a payment, or fetching real-time data. The endpoint handles all the internal logic required to complete the action.

3. Response: Finally, the execution endpoint sends back a response to the AI agent. The response is formatted according to the specifications outlined in the intent, making it easy for the AI agent to process the results.

The execution endpoint streamlines the whole process. Instead of cobbling together scripts to interact with APIs or web pages, AI agents simply send a well-formed request and get back a structured response. It’s efficient, predictable, and secure.

Workflow of an AI Agent: From Discovery to Execution

Let’s walk through an example of how an AI agent might use discovery and execution endpoints in a real-world scenario.

Imagine an AI agent designed to help manage appointments for a busy professional. Here’s how it would use the UIM protocol:

1. Discovery: The AI agent begins by querying the discovery endpoint of a scheduling service to find available intents. It specifies that it’s looking for actions related to appointments. The discovery endpoint returns a list that includes intents like “view available slots,” “schedule appointment,” and “cancel appointment.”

2. Selecting an Intent: The AI agent reviews the intents and selects “schedule appointment.” It reads the metadata to understand what parameters are needed, such as the date, time, and purpose of the appointment.

3. Requesting a PAT: Before executing the intent, the AI agent requests a Policy Adherence Token (PAT) from the web service. The PAT confirms the agent’s permissions, billing terms, and compliance requirements.

4. Execution: With the PAT and the required parameters, the AI agent sends a request to the execution endpoint for the “schedule appointment” intent. The endpoint validates the inputs, books the slot, and confirms the appointment.

5. Receiving the Response: The execution endpoint sends back a confirmation, including details like the appointment time, location, and any additional instructions. The AI agent processes this information and relays it to the user.

This whole workflow—from discovery to execution—is fast, secure, and streamlined. The AI agent doesn’t need to scrape web pages or navigate complex APIs. It simply follows a clear path from intent discovery to action execution.

Security and Efficiency

The discovery and execution model significantly enhances both security and efficiency compared to traditional methods. In web scraping, AI agents bypass security controls, which creates vulnerabilities. Even custom API integrations often involve hard-coded credentials and ad-hoc security measures that can be easily compromised. The UIM protocol’s endpoints, however, incorporate robust security at every step.

• Security: Discovery and execution endpoints validate every interaction. Only authorized AI agents with valid PATs can access execution endpoints, ensuring that actions are performed securely. This prevents unauthorized access and reduces the risk of data breaches.

• Efficiency: The structured approach of discovery and execution endpoints eliminates the inefficiencies of web scraping, where errors are frequent and updates are constant. AI agents no longer need to adapt to changes in web pages or deal with inconsistent data formats. Everything is standardized, making interactions faster and less prone to failure.

By replacing traditional methods with a structured discovery and execution model, the UIM protocol turns what used to be a chaotic and risky process into something predictable and reliable. It’s a better way forward for AI agents and web services alike, offering a secure, efficient, and scalable framework that can keep up with the growing demands of digital automation.