Prompt Saturation - AI Agent Kryp
Oct 16, 2023
Encountering the challenge of “Context Bleeding” or “Prompt Saturation” was the impetus for the development of the 🧬🌍GenWorlds framework. Let’s dive into how this issue surfaced and how we’ve tackled it.
Context Bleeding or Prompt Saturation
“Context Bleeding” or ”Prompt Saturation” occurs when an AI Agent, gets overwhelmed by having to choose from too many actions, impairing its decision-making accuracy and efficacy.
We witnessed this phenomenon firsthand in our AI Agent journey. 🤖 First, we developed yAgents (https://github.com/yeagerai/yeagerai-agent) — an AI agent that creates other agents. We observed erratic decisions and suboptimal reactions, signaling a clear case of Context Bleeding. In other words, we were asking the agent to do too much. This flaw needed addressing to optimize agent performance!
Our first approach to mitigate Context Bleeding was to compartmentalize the decision-making process by segregation of:
Navigation phase — selecting the next action
Communication phase - sending event via WebSocket
By dividing the phases, we reduced the prompt size at each step, enabling the agent to focus on fewer aspects simultaneously. This minimized Prompt Saturation and amplified precision in the agent’s actions and decisions!
When an agent still faces saturation — either due to numerous required actions or a highly complex problem — the multi-agent system in 🧬🌍GenWorlds comes into play. Here’s how it serves as our second mechanism to alleviate Context Bleeding.
By enabling agents to communicate and collaborate, the multi-agent system allows the decomposition of a larger problem into smaller, manageable segments. Instead of a single agent handling multiple tasks, several agents each tackle a section of the problem.
Our innovative dual-pronged approach within GenWorlds not only addresses the intrinsic issues of Context Bleeding and Prompt Saturation but also paves the way for developing more robust, accurate, and efficient AI agents in diverse applications!
Try it and give Feedback
We greatly 🙏appreciate🙏 any feedback on the framework and we are here to support anyone building on it: