A Methodology for Coordinated Agent-Based AI Systems Using Dynamic Templates and Streaming Connections

Abstract

This paper presents a novel approach for creating efficient, modular AI systems using agents defined by dynamic templates. The agents in this system are designed to handle streaming inputs and outputs in a flexible and coordinated manner. By leveraging placeholders within these templates, the agents can dynamically switch between different inputs and process them through a series of interconnected components.

Introduction

In advanced AI systems, modularity and efficiency are critical for handling complex tasks. This paper introduces a methodology where AI agents are defined as templates with embedded placeholders. These agents dynamically manage input and output flows through streaming connections, enabling efficient and scalable processing of data.

System Overview

1. Agent Definition Each agent in the system is fundamentally a template. It consists of a predefined block of text with embedded placeholders for inputs. The agent operates by initially sending the text tokens of the template. Based on the placeholders, it dynamically switches between different inputs, processes these inputs through an LLM, and then returns to sending the original template.

2. Dynamic Input Switching The agents are designed to handle input streams by identifying placeholders within their templates. When processing, the agent switches to the corresponding input based on the placeholder, continues processing this input until completion, and then resumes sending the template text. This allows for flexible and efficient handling of different types of inputs.

3. Streaming Connections The system utilizes streaming connections between agents to enable real-time data processing. As data is streamed through the system, agents process the input according to their templates and dynamically handle outputs. This method ensures that the system can scale and adapt to various processing needs.

4. Coordination and Filtering To manage and correct outputs, especially in scenarios where data might be malformed or incomplete (such as JSON), the system employs additional filtering mechanisms. These filters are designed to process streaming output from agents and correct any issues in real time. The filters themselves operate as generators, ensuring that the processing remains efficient and does not compromise the benefits of streaming connections.

5. Modular Processing By breaking down tasks into smaller, manageable components and using agents to handle specific aspects of the problem, the system achieves modularity. Each agent can focus on a particular task, such as data transformation or filtering, while interacting with other agents to complete the overall processing workflow.

Benefits

1. Efficiency The use of dynamic templates and streaming connections enhances processing efficiency. Agents can handle large volumes of data in real time, reducing latency and improving throughput.

2. Scalability The system's modular design allows for easy scaling. Agents can be added or modified without disrupting the entire workflow, enabling adaptation to various processing requirements.

3. Flexibility The dynamic switching of inputs based on placeholders within templates provides flexibility in handling diverse data types and processing scenarios. This adaptability is crucial for complex tasks that require nuanced processing.

Conclusion

This methodology for building AI systems using dynamic templates and streaming connections represents a significant advancement in modular and efficient data processing. By leveraging placeholders and real-time data handling, the system achieves both flexibility and scalability, making it well-suited for complex and large-scale tasks.