Autonomous Agent for Cloud Error Resolution
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Solution Overview
Problem
Current chaos engineering methods primarily test known failure classes in software applications, leaving unknown issues undetected and potentially causing application degradation or downtime, and require human intervention for error resolution due to the complexity of tools and systems.
Innovation Solution
Implementing agent-based error resolution using reinforcement learning and chaos experiments, where an agent is trained to detect and respond to failures in a simulated environment, selecting actions based on historical data, and deploying autonomously in production environments to handle faults without explicit human instructions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If chaos engineering tests are performed to detect failures, then application resilience is improved, but the system complexity and difficulty of operation increase due to the complexity of tools and systems required
Solution Approach 1:
The patent implements an autonomous agent that performs error detection and resolution without human intervention. The agent autonomously navigates the application environment, detects failures through chaos experiments, and executes remediation actions based on learned policies from reinforcement learning training, making the system self-servicing for operational tasks.
Solution Approach 2:
The patent introduces an autonomous agent as an intermediary between the complex chaos engineering tools and the application being tested. This agent simplifies operation by automatically interpreting tool outputs, making decisions about error resolution, and executing actions, thereby shielding operators from system complexity while maintaining reliability testing capabilities.
2Measurement precision
If human engineers manually troubleshoot failures, then error resolution accuracy is improved, but response time increases causing loss of service availability
Solution Approach 1:
The patent employs reinforcement learning to train the autonomous agent in advance on simulated environments with various failure modes. This preliminary training enables the agent to rapidly recognize and respond to actual failures in production without requiring human analysis time, achieving both high accuracy through extensive pre-learning and fast response through automated execution.
Solution Approach 2:
The autonomous agent independently performs the complete error resolution workflow including failure detection, analysis, and remediation without human intervention. This self-service capability eliminates the time lag associated with human response while maintaining accurate error detection through the agent's trained recognition capabilities.
3Manufacturing precision
If known failure classes are tested through chaos engineering, then test coverage for known issues is improved, but unknown failure modes remain undetected reducing overall reliability
Solution Approach 1:
The patent implements a reinforcement learning framework that enables the autonomous agent to dynamically adapt to new failure modes. Instead of being limited to pre-programmed known failures, the agent learns from interactions with the environment and can generalize to previously unseen failure types, thereby improving overall system reliability while maintaining precise test coverage for known issues.
Solution Approach 2:
The patent uses reinforcement learning to change the agent's behavior parameters based on learned experiences from simulated environments. This allows the agent to adjust its error detection and resolution strategies dynamically, enabling it to handle both known failure classes with high precision and unknown failure modes through generalization, thus resolving the contradiction between targeted test coverage and overall reliability.
Data Source
AI summary
Systems and methods for agent-based error resolution in private cloud application using reinforcement learning and chaos experiments are disclosed. In one embodiment, a method for training an agent computer program using reinforcement learning and chaos experiments may include: (1) causing, by an agent management computer program, a failure in a simulated environment that the agent computer program is deployed, wherein the agent computer program is configured to implement an action in response to the failure; (2) detecting, by the agent management computer program, an impact of the action in the simulated environment; and (3) rewarding, by the agent management computer program, the agent computer program in response to the impact of the action being a positive impact.