Watsonx Orchestrate Debug Skill for IBM Bob

2026-05-18 · Source: Niklas Heidloff · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, quick

Summary

IBM Bob is an AI SDLC partner designed to augment existing workflows and facilitate confident work with real codebases. It can be extended through skills, MCP tools, and custom modes, such as for developing agents in watsonx Orchestrate. This post details a custom debug skill created to identify and fix issues with watsonx Orchestrate agents running locally. This skill connects to the local Orchestrate Developer Edition via the 'orchestrate' CLI and a virtual environment, reading logs from specific containers and combining this data with traces. The skill then summarizes recent issues and suggests next steps, automating a previously manual and time-consuming debugging workflow that involved searching StackOverflow and manually applying solutions. The skill currently operates in 'Advanced' mode and can be customized to include logs from additional Orchestrate containers.

Key takeaway

For AI Engineers debugging watsonx Orchestrate agents locally, integrating custom skills like the described debug skill into IBM Bob can significantly streamline issue identification and resolution. This approach automates the manual process of log analysis and solution searching, allowing you to quickly pinpoint errors and receive actionable next steps, thereby accelerating your development cycle and reducing cognitive load.

Key insights

IBM Bob's custom debug skill automates local watsonx Orchestrate agent issue identification and resolution.

Principles

• Automate repetitive debugging tasks.
• Integrate AI tools into existing SDLC.
• Combine logs and traces for comprehensive diagnostics.

Method

The debug skill activates a Python virtual environment, reads traces using "orchestrate observability traces search --last 1h", exports full trace data, and reads specific container logs to identify errors and suggest fixes.

In practice

• Use "orchestrate observability traces search" for recent traces.
• Export full trace data with "orchestrate observability traces export --trace-id".
• Customize log sources for specific Orchestrate containers.

Topics

• IBM Bob
• watsonx Orchestrate
• AI SDLC
• Agent Debugging
• Observability Traces

Best for: AI Engineer, Machine Learning Engineer, MLOps Engineer

Related on AIssential

• Debugging Tools in IBM watsonx Orchestrate — IBM watsonx Orchestrate now offers dedicated debugging tools for agents and agentic workflows.
• Accessing Orchestrate Environments via Agentic Skill — Agentic tools gain significant power by directly accessing and interacting with live execution environments.
• The Future of Debugging: Let AI Read Your Logs, Metrics & Traces — MCP integrates AI assistants with monitoring tools for conversational, context-aware debugging.
• IBM and ElevenLabs: Enterprise AI Finds its Voice — The IBM-ElevenLabs partnership enables secure, scalable, voice-first agentic AI for enterprises, transforming human-computer interaction.
• The DataRobot platform as skills in Claude Code — Packaging platform operational judgment as agent skills enables reliable, governed AI agent production deployments.
• Fighting AI with AI — Lawrence Jones, Incident — AI systems managing other AI systems require specialized tools for debugging, evaluation, and scalable analysis.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Niklas Heidloff.