feat: WORKSPACE_ID env propagation for namespaced paths (agent-relay-505, 488, 489)

[khaliqgant]

Summary

• Implements agent-relay-505: WORKSPACE_ID env propagation for workspace-namespaced paths
• Also covers agent-relay-488 (socket path namespacing) and agent-relay-489 (outbox path namespacing)

Changes

When WORKSPACE_ID is set (via process.env or config.env), RelayPtyOrchestrator now uses workspace-namespaced paths:

• Socket: /tmp/relay/{workspaceId}/sockets/{name}.sock
• Outbox: /tmp/relay/{workspaceId}/outbox/{name}/

Falls back to legacy paths for local development:

• Socket: /tmp/relay-pty-{name}.sock
• Outbox: /tmp/relay-outbox/{name}

This enables multi-tenant isolation in cloud deployments where multiple workspaces run on the same host.

Files Modified

• src/wrapper/relay-pty-orchestrator.ts - Path namespacing logic in constructor
• src/wrapper/relay-pty-orchestrator.test.ts - Tests for namespacing scenarios

Test plan

• Existing tests pass (25/25)
• New tests verify workspace-namespaced paths when WORKSPACE_ID set
• New tests verify legacy paths when WORKSPACE_ID not set
• Manual test in cloud environment

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📊 38 files reviewed • 1 high risk • 2 need attention

🚨 High Risk:

• src/bridge/spawner.ts — Introduction of new spawning logic that may cause agent conflicts if implemented incorrectly.

⚠️ Needs Attention:

• docs/INTEGRATION-GUIDE.md — Discusses integration of multiple systems which could introduce potential vulnerabilities if misconfigured or not properly secured.

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[khaliqgant]

AI Conversation Transcript

👤 khaliqgant

This error panel array seems brittle and not comprehensive. How is it used and talk about the drawbacks of this type of approach

🤖 AI Assistant

In the provided code, the error patterns are defined in an array called errorPatterns (located at line 40). This array contains regular expressions that are used to identify specific error messages in the outputs fed to the StuckDetector. Here are the key points about how it is used and the potential drawbacks of this approach:

Usage

1. Error Detection: The errorPatterns array is utilized within the detectErrorLoop method (lines 191-228). This method checks the recent outputs for any matches against the patterns defined in this array.
2. Loop Detection: If an output matches any of the patterns, it is considered to be an error. The method counts how many times the same error message appears in succession. If the count exceeds the configured threshold (loopThreshold), the error is deemed to be in a loop, and a stuck event is emitted.

Drawbacks of This Approach

1. Brittleness:

   • The reliance on specific string patterns means that any slight change in the error message format (e.g., different casing, additional context) could lead to missed detections. For example, an error message that is slightly modified or contains additional information may not match any of the predefined patterns.

2. Limited Coverage:

   • The predefined patterns may not cover all possible error messages that could occur in the system. New errors or variations of existing errors would require updates to the array, which could lead to undetected issues if not maintained diligently.

3. False Positives:

   • The approach could result in false positives, where benign outputs that match the patterns (but are not actual errors) are incorrectly identified as errors. This can lead to unnecessary stuck events being emitted, leading to confusion and making it harder to differentiate between genuine and false alarms.

4. Scalability:

   • As the number of error messages increases, managing and updating the patterns becomes more cumbersome. A more scalable solution might involve a more sophisticated logging and error handling framework that can dynamically categorize and handle errors.

5. Performance Concerns:

   • The regex checks in loops can become a performance bottleneck if the number of recent outputs grows large, especially if they are checked against multiple patterns. The current implementation keeps only the last 5 times the threshold is exceeded, but if many patterns are added, it may still slow down the detection process.

Conclusion

While the current implementation of the error patterns provides a straightforward mechanism for detecting error loops, it is important to be aware of its limitations. An ideal implementation would require a more flexible and comprehensive approach to error detection, potentially utilizing more advanced error tracking mechanisms, such as structured logging or integrating with a monitoring system that can dynamically adapt to new error messages.

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From My Senior Dev

[khaliqgant]

AI Conversation Update

Continuation of previous conversation...

👤 khaliqgant

Propose alternative approaches

🤖 AI Assistant

Here are several alternative approaches to improve error detection and handling in the StuckDetector, addressing the limitations of the current regex-based pattern matching:

1. Structured Logging with Error Codes

• Description: Implement a structured logging system where error messages are logged with associated error codes and metadata (e.g., severity, source, context).
• Benefits:
  • Easier to categorize and filter errors based on severity or type.
  • Allows for more sophisticated querying and analysis of logs.
  • Reduces reliance on specific string patterns.
• Implementation: Use a logging library that supports structured logs (e.g., Winston, Pino) and define a set of standard error codes.

2. Machine Learning for Anomaly Detection

• Description: Train a machine learning model to recognize normal versus abnormal output patterns, identifying potential errors based on learned behaviors rather than predefined patterns.
• Benefits:
  • Can adapt to new and varied error messages without manual updates.
  • Reduces false positives by learning from historical data.
• Implementation: Use historical output data to train a model (e.g., supervised learning with labeled data) and integrate it into the stuck detection process.

3. Dynamic Rule Engine

• Description: Implement a rule-based engine that allows users to define error detection rules dynamically.
• Benefits:
  • Users can add, modify, or remove rules as needed without changing the codebase.
  • Provides flexibility to adapt to new error types and formats.
• Implementation: Use a rules engine library (e.g., Nools, json-rules-engine) to define and evaluate rules on the outputs.

4. Error Reporting Service Integration

• Description: Integrate with an external error monitoring and reporting service (e.g., Sentry, Rollbar) that can capture and analyze errors in real-time.
• Benefits:
  • Gains access to a more comprehensive analysis of error occurrences, trends, and patterns.
  • Allows for better prioritization of issues based on impact and frequency.
• Implementation: Send error messages to the service with context, and use its capabilities to monitor for stuck conditions.

5. Threshold-Based Alerts with Contextual Awareness

• Description: Instead of relying solely on repetitive patterns, use contextual awareness to determine the significance of an error based on its occurrence rate, timing, and related outputs.
• Benefits:
  • Reduces false positives by considering the context in which errors occur.
  • Allows for more nuanced detection of stuck conditions based on various metrics, not just repetition.
• Implementation: Implement a scoring system that evaluates outputs based on multiple factors (e.g., frequency, type, duration) to determine if a stuck condition is reached.

6. Custom Error Classes

• Description: Define custom error classes with specific attributes to capture relevant error information and characteristics.
• Benefits:
  • Provides a clear structure for handling different error types and their associated behaviors.
  • Simplifies the process of identifying and reacting to specific errors.
• Implementation: Create a hierarchy of error classes and use them in the output handling logic to classify and manage errors.

Summary

These alternative approaches can enhance the robustness and flexibility of the error detection mechanism in the StuckDetector. Depending on the complexity of the system and the variety of expected error messages, a combination of these methods may provide the best results, leading to improved performance and reliability in detecting and handling stuck conditions.

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From My Senior Dev