Bot Performance Video Streams for Faster Failure Diagnosis
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Solution Overview
Problem
Traditional bot monitoring systems are reactive, lack predictive capabilities, and face integration challenges, leading to inefficiencies in diagnosing and preventing software bot failures, which can result in operational and security risks.
Innovation Solution
A bot monitoring system that captures time-ordered data, including screenshots and logs, to generate video streams depicting bot behavior, allowing for proactive identification of anomalies and failures, and provides user-friendly renderings on a separate device without requiring access to the computing environment where the bot is hosted.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional bot monitoring systems are used, then bot performance can be monitored, but the systems are reactive and lack predictive capabilities, leading to delays in diagnosing and preventing bot failures
Solution Approach 1:
The system performs preliminary actions by capturing and storing screenshots and log data during normal bot operations before failures occur. This preliminary data collection enables proactive analysis and prediction of potential bot failures, allowing the system to alert users before actual failures happen, thus improving reliability while reducing diagnosis time.
Solution Approach 2:
The system creates a buffer of visual and log data that cushions against the impact of bot failures. By maintaining this data buffer in advance, the system ensures that when failures do occur, the diagnostic information is already available, eliminating the time loss associated with post-failure investigation and allowing for quicker response and prevention of recurring issues.
2Reliability
If detailed bot behavior data is captured and analyzed, then predictive capabilities improve, but computing resources are consumed
Solution Approach 1:
The system extracts only the essential visual and log data needed for bot behavior analysis, separating this critical information from unnecessary data. By taking out only the relevant screenshots and log entries that indicate bot state and performance, the system maintains strong predictive capabilities while minimizing computing resource consumption during both data capture and analysis phases.
Solution Approach 2:
The system applies local quality by capturing and analyzing data with varying levels of detail based on bot operational context. During normal operations, minimal data is captured to conserve resources. When anomalies or critical events are detected, the system increases data capture intensity locally at those specific moments, maintaining predictive accuracy while optimizing overall resource usage.
3Productivity
If video streams with metadata are generated for user review, then diagnostic efficiency improves, but device complexity increases
Solution Approach 1:
The system creates simplified copies of bot behavior in the form of video streams with embedded metadata. Instead of requiring users to access the complex computing environment where the bot operates, the system generates visual copies that replicate bot actions and state changes. This copying approach dramatically improves diagnostic efficiency by presenting information in an easily consumable format while avoiding the complexity of direct environment access.
Solution Approach 2:
The video stream with metadata acts as an intermediary between the complex bot execution environment and the user. This intermediary translates complex bot behaviors, system states, and error conditions into visual representations that are easy to interpret. The intermediary layer handles the complexity internally while presenting a simplified view to users, thereby improving diagnostic efficiency without requiring users to deal with system complexity.
Data Source
AI summary
In some implementations, a monitoring system may capture a set of screenshots that depict behavior associated with a bot during an automation run. The monitoring system may obtain one or more logs that include information describing the behavior of the bot during the automation run. The monitoring system may create, based on a portion of the set of screenshots, video data that includes a sequence of frames related to an anomaly or a failure event that occurred during the automation run. The monitoring system may annotate, based on the information included in the one or more logs, the sequence of frames with metadata describing the behavior of the bot. The monitoring system may provide, to a user device, user interface data that includes the video data and the metadata used to annotate the sequence of frames.