Streamlining Agent Execution In OpenSearch ML Commons A Feature Request

Hey guys, let's dive into a cool feature request that aims to streamline the agent execution process in OpenSearch ML Commons! Currently, executing an agent involves a two-step process: first, you need to set up the agent, and then you grab its ID to execute it. This can be a bit cumbersome, so let's explore how we can make things smoother.

Is your feature request related to a problem?

Right now, if you want to get an agent going, you've gotta jump through a couple of hoops. You first create the agent, which gives you an agent ID. Then, you use that ID to actually execute the agent. For example, check out this flow:

# create an agent
POST /_plugins/_ml/agents/_register
{
  "name": "ToolName",
  "type": "flow",
  "tools": [
    {
      "name": "A demo tool",
      "type": "ToolType",
      "parameters": {}
    }
  ]
}

{
  "agent_id": "jxLPKpgBrJw2kn1hVBOx"
}

# execute agent

POST /_plugins/_ml/agents/jxLPKpgBrJw2kn1hVBOx/_execute
{
  "parameters": {
    ....
  }
}

See how you first create the agent using /_plugins/_ml/agents/_register, and then you execute it using /_plugins/_ml/agents/<agent_id>/_execute? It works, but it could be more efficient, right? This two-step agent execution process, while functional, introduces unnecessary complexity. Imagine a scenario where you frequently need to create and execute agents on the fly. The current method requires you to keep track of the agent ID, adding an extra step that can slow down your workflow. Furthermore, this separation of creation and execution can make automated processes more intricate, as you need to handle the ID retrieval and subsequent execution calls. The need for a more streamlined approach becomes particularly apparent in environments where rapid prototyping and experimentation are crucial. By reducing the steps involved in agent execution, developers and researchers can iterate faster and focus more on the core logic of their applications. The goal is to create a more intuitive and user-friendly experience that empowers users to leverage the power of agents without getting bogged down in unnecessary procedural details. This enhancement would not only simplify the development process but also make the system more accessible to users with varying levels of expertise. The complexity of managing agent IDs can be a barrier to entry for new users, while a streamlined process can encourage wider adoption and experimentation with agents. This improvement is essential for fostering a vibrant and active community around OpenSearch ML Commons.

What solution would you like?

So, here’s the idea: let's combine the agent creation and execution into a single step! How much simpler would that be? Check out this proposal:

POST /_plugins/_ml/agents/_execute
{
  "agent": {
    "name": "ToolName",
    "type": "flow",
    "tools": [
      {
        "name": "A demo tool",
        "type": "ToolType",
        "parameters": {}
    }
  ]
  },
  "parameters": {}
}

This streamlined approach proposes a unified endpoint for both creating and executing agents. Instead of separate calls for registration and execution, you can define the agent configuration directly within the execution request. This significantly reduces the number of steps involved and simplifies the overall workflow. The key advantage here is the ability to define the agent and its parameters in a single, cohesive request. This not only makes the process more intuitive but also reduces the potential for errors. For instance, you no longer need to store and manage agent IDs, eliminating a common source of confusion and operational overhead. This approach aligns with the principles of simplicity and efficiency, making it easier for users to interact with the agent execution framework. Furthermore, this method opens up new possibilities for dynamic agent creation and execution. Imagine a scenario where you need to create agents on the fly based on real-time data or user input. With the combined creation and execution endpoint, you can easily integrate agent management into your application logic. This level of flexibility is crucial for building adaptive and intelligent systems that can respond to changing conditions. The proposed solution not only simplifies the existing process but also lays the groundwork for future enhancements and more sophisticated use cases. This enhancement is a significant step towards making the OpenSearch ML Commons platform more user-friendly and versatile.

By sending the agent definition along with the execution parameters in a single request, we eliminate the need for a separate registration step. This makes the process much more intuitive and efficient. Imagine being able to define and run an agent with a single command – it's a game-changer for quick prototyping and automated workflows. This approach not only simplifies the user experience but also reduces the overhead associated with managing agent IDs. No more juggling IDs – just define and execute! This streamlined process is particularly beneficial for users who frequently work with temporary or short-lived agents. They can create and run these agents without having to worry about the lifecycle management of the agent ID. Moreover, this change promotes a more declarative style of agent management, where you specify the desired state (the agent configuration and execution parameters) rather than the steps to achieve it. This declarative approach can make your code more readable and maintainable. The single-step execution also simplifies the integration of agents into automated systems. Whether you're building a complex workflow or a simple script, the combined creation and execution endpoint makes it easier to incorporate agents into your automation pipelines. This ease of integration is crucial for the widespread adoption of agents in various applications and environments.

What alternatives have you considered?

What else could we do? Well, we could stick with the current two-step process, but let's be honest, that’s not the most elegant solution. We're always looking for ways to make things more efficient and user-friendly, and this inline execution proposal seems like a solid step in that direction. Another alternative might be to introduce a caching mechanism for agent IDs, so you wouldn't have to create the agent every time you want to execute it. However, this adds complexity to the system and doesn't address the fundamental issue of having separate creation and execution steps. We could also consider a more sophisticated agent management interface, but that would be a larger undertaking and might not be necessary if we can simplify the core execution process. The focus here is on streamlining the most common use case – creating and executing agents – and the inline execution proposal achieves this goal effectively. This approach provides a balance between simplicity and functionality, making it a practical solution for improving the agent execution workflow. Other ideas might involve different ways of structuring the API calls, but the core principle remains the same: we want to reduce the number of steps required to run an agent. By focusing on this goal, we can create a more intuitive and efficient system that empowers users to leverage the full potential of agents in OpenSearch ML Commons.

Do you have any additional context?

No extra context for now, but feel free to chime in with your thoughts and ideas! This feature request is all about making agent execution smoother and more intuitive. Your input is super valuable in shaping the future of OpenSearch ML Commons, so let's discuss how we can make this happen! We want to ensure that the final solution meets the needs of the community and provides a seamless experience for all users. This includes considering various use cases and potential edge cases to ensure that the new feature is robust and reliable. Your feedback will help us identify any potential issues and refine the design to create the best possible solution. Together, we can build a more powerful and user-friendly platform for machine learning and artificial intelligence in OpenSearch. So, don't hesitate to share your thoughts, suggestions, and concerns – your voice matters! Let's work together to make this feature a reality and enhance the capabilities of OpenSearch ML Commons.