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68 lines
3.0 KiB
68 lines
3.0 KiB
5 months ago
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# Swarms Multi-Agent Framework Documentation
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## Table of Contents
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- Agent Failure Protocol
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- Swarm Failure Protocol
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---
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## Agent Failure Protocol
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### 1. Overview
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Agent failures may arise from bugs, unexpected inputs, or external system changes. This protocol aims to diagnose, address, and prevent such failures.
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### 2. Root Cause Analysis
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- **Data Collection**: Record the task, inputs, and environmental variables present during the failure.
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- **Diagnostic Tests**: Run the agent in a controlled environment replicating the failure scenario.
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- **Error Logging**: Analyze error logs to identify patterns or anomalies.
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### 3. Solution Brainstorming
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- **Code Review**: Examine the code sections linked to the failure for bugs or inefficiencies.
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- **External Dependencies**: Check if external systems or data sources have changed.
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- **Algorithmic Analysis**: Evaluate if the agent's algorithms were overwhelmed or faced an unhandled scenario.
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### 4. Risk Analysis & Solution Ranking
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- Assess the potential risks associated with each solution.
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- Rank solutions based on:
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- Implementation complexity
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- Potential negative side effects
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- Resource requirements
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- Assign a success probability score (0.0 to 1.0) based on the above factors.
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### 5. Solution Implementation
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- Implement the top 3 solutions sequentially, starting with the highest success probability.
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- If all three solutions fail, trigger the "Human-in-the-Loop" protocol.
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---
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## Swarm Failure Protocol
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### 1. Overview
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Swarm failures are more complex, often resulting from inter-agent conflicts, systemic bugs, or large-scale environmental changes. This protocol delves deep into such failures to ensure the swarm operates optimally.
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### 2. Root Cause Analysis
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- **Inter-Agent Analysis**: Examine if agents were in conflict or if there was a breakdown in collaboration.
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- **System Health Checks**: Ensure all system components supporting the swarm are operational.
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- **Environment Analysis**: Investigate if external factors or systems impacted the swarm's operation.
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### 3. Solution Brainstorming
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- **Collaboration Protocols**: Review and refine how agents collaborate.
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- **Resource Allocation**: Check if the swarm had adequate computational and memory resources.
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- **Feedback Loops**: Ensure agents are effectively learning from each other.
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### 4. Risk Analysis & Solution Ranking
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- Assess the potential systemic risks posed by each solution.
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- Rank solutions considering:
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- Scalability implications
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- Impact on individual agents
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- Overall swarm performance potential
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- Assign a success probability score (0.0 to 1.0) based on the above considerations.
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### 5. Solution Implementation
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- Implement the top 3 solutions sequentially, prioritizing the one with the highest success probability.
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- If all three solutions are unsuccessful, invoke the "Human-in-the-Loop" protocol for expert intervention.
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---
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By following these protocols, the Swarms Multi-Agent Framework can systematically address and prevent failures, ensuring a high degree of reliability and efficiency.
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