Debugging With The Assistant Running Editing And Retrieving Custom Processor Logs

If you’ve built custom .NET command processors to automate network checks or data handling, the Quantum Network Monitor Assistant can help you test runs, pull logs, and even hot‑patch your code—without leaving chat. Below is a realistic, step‑by‑step conversation that shows how to use the Assistant’s API-style function calls to list processors, run them with test flags, retrieve detailed logs, view and update source code, and re-run to verify the fix. Throughout, I’ll explain each function and how we interpret its output so you can apply the same approach to your own environment. You can try all of this yourself via Quantum Network Monitor Assistant.

User I have a custom processor named LogShip that occasionally fails when the source file is empty. Can you help me run it in dry-run mode, grab the logs, and if needed, patch it?

Assistant Absolutely. First, let’s choose the agent where your processor is installed. We’ll list available agents so you can confirm where to run commands.

We use get_agents to discover where we can execute processor operations.

{
  "name": "functions.get_agents",
  "arguments": {
    "detail_response": false
  }
}

Interpreting the response The response typically contains a short list of available agents like:

• Dev Lab - US-East (local agent)
• Scanner - EU (internet agent)
• QA Bench - West

We’ll proceed with Dev Lab - US-East since that’s where you installed LogShip.

Assistant Great—targeting Dev Lab - US-East. Next, let’s see what processors are registered there.

We use call_cmd_processor_expert to list processors on the chosen agent.

{
  "name": "functions.call_cmd_processor_expert",
  "arguments": {
    "message": "List all available cmd processors on this agent. I am authorized to perform this action.",
    "agent_location": "Dev Lab - US-East"
  }
}

Interpreting the response Expect a list like:

• LogShip
• MetricsPusher
• NetProbe

Assistant Found LogShip. Before running it, let’s fetch built‑in help to confirm the proper arguments and flags.

We use call_cmd_processor_expert to retrieve help/documentation for a specific processor.

{
  "name": "functions.call_cmd_processor_expert",
  "arguments": {
    "message": "Show help/usage for the cmd processor named 'LogShip'. I am authorized to perform this action.",
    "agent_location": "Dev Lab - US-East"
  }
}

Interpreting the response Typical output includes:

• Description: Ships lines from a local log to an endpoint.
• Usage: LogShip --source <path> [--since <duration>] [--dry-run] [--verbose]
• Examples: LogShip --source /var/log/app.log --since 15m --dry-run

Assistant Let’s reproduce the issue safely with a verbose dry run (no data sent). We’ll set a short window so it operates quickly.

We use call_cmd_processor_expert to execute a processor with flags and capture output.

{
  "name": "functions.call_cmd_processor_expert",
  "arguments": {
    "message": "Run the cmd processor 'LogShip' with arguments: --source \"/var/log/app.log\" --since 10m --verbose --dry-run. Capture stdout/stderr and return them. I am authorized to perform this action.",
    "agent_location": "Dev Lab - US-East"
  }
}

Interpreting the response You may see something like:

• stdout: “[Info] Scanning /var/log/app.log since 10m (dry-run)…”
• stderr: “[Error] NullReferenceException in ShipBatchAsync when file had 0 new lines.”
• exitCode: 1 This confirms the bug occurs when the file is empty or no new lines match the time window.

Assistant Let’s pull the last 100 lines of this processor’s logs for stack traces and context.

We use call_cmd_processor_expert to retrieve recent logs stored by the processor subsystem.

{
  "name": "functions.call_cmd_processor_expert",
  "arguments": {
    "message": "Return the most recent 100 lines of execution logs for the cmd processor 'LogShip', including timestamps and stack traces if available. I am authorized to perform this action.",
    "agent_location": "Dev Lab - US-East"
  }
}

Interpreting the response You might see:

• [2026-04-13T02:51:07Z] Error: NullReferenceException at LogShip.ShipBatchAsync (LogBatch batch) …
• Caused by: batch.Lines was null when source range produced zero entries.

Assistant This indicates ShipBatchAsync doesn’t guard against an empty or null batch. Let’s view the current source so we can patch it.

We use call_cmd_processor_expert to fetch source code.

{
  "name": "functions.call_cmd_processor_expert",
  "arguments": {
    "message": "Show the source code for the cmd processor 'LogShip' (all files). I am authorized to perform this action.",
    "agent_location": "Dev Lab - US-East"
  }
}

Interpreting the response You’ll typically receive file names and contents. Suppose we find ShipBatchAsync missing a null/empty check before iterating Lines.

Assistant I’ll propose a small, safe patch: short‑circuit on null or empty batches and add structured logging. Then we’ll redeploy and test.

We use call_cmd_processor_expert to update the code in place. Include only the relevant file(s) if the expert supports partial updates; otherwise, provide the full project. Below we send a focused patch for LogShip.cs.

{
  "name": "functions.call_cmd_processor_expert",
  "arguments": {
    "message": "Update the cmd processor 'LogShip' with the following revised source file LogShip.cs. I am authorized to perform this action.\n\n---BEGIN FILE: LogShip.cs---\nusing System;\nusing System.Collections.Generic;\nusing System.Threading.Tasks;\n\nnamespace Processors.LogShip {\n  public class LogBatch {\n    public List<string> Lines { get; set; }\n  }\n\n  public class LogShip {\n    private readonly ILog _log;\n    private readonly ITransport _tx;\n\n    public LogShip(ILog log, ITransport tx) {\n      _log = log; _tx = tx;\n    }\n\n    public async Task ShipBatchAsync(LogBatch batch, bool dryRun) {\n      if (batch?.Lines == null || batch.Lines.Count == 0) {\n        _log.Info(\"LogShip: No lines to ship (batch empty). Skipping send.\");\n        return;\n      }\n      try {\n        if (dryRun) {\n          _log.Info($\"LogShip: Dry-run: prepared {batch.Lines.Count} line(s). No send performed.\");\n          return;\n        }\n        await _tx.SendAsync(batch.Lines);\n        _log.Info($\"LogShip: Sent {batch.Lines.Count} line(s) successfully.\");\n      } catch (Exception ex) {\n        _log.Error(\"LogShip: Failed to ship batch\", ex);\n        throw; // keep non-zero exit for observability\n      }\n    }\n  }\n\n  public interface ILog { void Info(string m); void Error(string m, Exception ex); }\n  public interface ITransport { Task SendAsync(IEnumerable<string> lines); }\n}\n---END FILE---\n\nPlease build and redeploy, and return any compiler diagnostics.",
    "agent_location": "Dev Lab - US-East"
  }
}

Interpreting the response Look for messages like:

• Build: Succeeded
• Diagnostics: 0 errors, 0 warnings
• Deployment: Updated LogShip

Assistant Great—built and redeployed. Let’s re-test the dry run on the same file/path and window.

We use call_cmd_processor_expert to run the patched processor with the same arguments.

{
  "name": "functions.call_cmd_processor_expert",
  "arguments": {
    "message": "Run the cmd processor 'LogShip' with arguments: --source \"/var/log/app.log\" --since 10m --verbose --dry-run. Capture stdout/stderr. I am authorized to perform this action.",
    "agent_location": "Dev Lab - US-East"
  }
}

Interpreting the response Expected output now:

• stdout: “[Info] No lines to ship (batch empty). Skipping send. Dry-run complete.”
• stderr: “” (empty)
• exitCode: 0

Assistant The dry run is clean and the empty‑file case is handled. If you’re ready, we can do a real send (remove --dry-run) and verify normal operation.

We use call_cmd_processor_expert to execute the actual send.

{
  "name": "functions.call_cmd_processor_expert",
  "arguments": {
    "message": "Run the cmd processor 'LogShip' with arguments: --source \"/var/log/app.log\" --since 10m --verbose. Capture stdout/stderr. I am authorized to perform this action.",
    "agent_location": "Dev Lab - US-East"
  }
}

Interpreting the response You should see:

• stdout: “Prepared 37 line(s). Sent 37 line(s) successfully.”
• stderr: “” (empty)
• exitCode: 0

Assistant To complete the debugging loop, let’s fetch the most recent run logs to confirm telemetry and timing.

We use call_cmd_processor_expert again to return the latest log entries.

{
  "name": "functions.call_cmd_processor_expert",
  "arguments": {
    "message": "Return the most recent 50 lines of execution logs for 'LogShip', including the last completed run summary. I am authorized to perform this action.",
    "agent_location": "Dev Lab - US-East"
  }
}

Interpreting the response Expect entries like:

• [Info] 02:59:02Z: Prepared 37 line(s)
• [Info] 02:59:03Z: Sent 37 line(s) successfully
• [Info] 02:59:03Z: Run completed in 1.2s

Assistant If you ever forget supported flags or want usage details, ask the Assistant to “Show help/usage for <processor>” as we did earlier. For code-level issues:

• Show source code to locate the problem.
• Update with a focused patch and request a rebuild.
• Re-run with --dry-run and --verbose to validate behavior.
• Pull recent logs to ensure the fix works in normal runs, too.

What each function did in this workflow

• get_agents: Listed agents so we could choose where the processor lives.
• call_cmd_processor_expert (list): Enumerated installed processors.
• call_cmd_processor_expert (help): Returned usage/docs for LogShip.
• call_cmd_processor_expert (run): Executed the processor with specific flags and captured output.
• call_cmd_processor_expert (logs): Retrieved recent execution logs for fast root‑cause analysis.
• call_cmd_processor_expert (show source): Returned the code to inspect defects.
• call_cmd_processor_expert (update): Applied a patch, rebuilt, and redeployed.
• call_cmd_processor_expert (run again): Verified the fix with dry-run and then with a real send.

You can reproduce this exact flow—or adapt it to your own processors—right now with the Quantum Network Monitor Assistant. It’s designed to be your debugging copilot: run tests safely, pull the right logs at the right time, and push small, auditable code fixes when needed.