Agentic Debugging
Overview
Agentic Debugging allows you to delegate debugging tasks to an AI agent that uses contextual data, integrated tools, and reasoning to:
- Analyze system symptoms
- Identify likely root causes
- Recommend next steps for remediation
🛠️ How It Works
Agentic Debugging can be triggered in two ways:
- Prompt-based: You provide a natural language query describing the issue or a symptom.
- Alert-based: You click Debug directly from the Alerts Inbox, where the AI automatically begins analysis based on the alert metadata.
🔄 Integration-Based Behavior
| Scenario | Behavior |
|---|---|
| No integrations connected | The agent provides general debugging guidance—questions to ask, commands to run, and logs to inspect manually. |
| Integrations connected | The agent actively runs queries and fetches data (e.g., logs, metrics, infra info), uses that to reason, and then shares the diagnosis and remediation suggestions. |
📋 Example
For example, if a Kubernetes pod is CrashLooping, and you have observability integrations configured:
- The agent may fetch logs, check deployment configurations, and diagnose the root cause (e.g., OOMKill, bad image).
- If the root cause is found, the agent may suggest a remediation such as “Restart the pod” or “Increase memory limit”.
Agentic Debugging
Overview
Agentic Debugging allows you to delegate debugging tasks to an AI agent that uses contextual data, integrated tools, and reasoning to:
- Analyze system symptoms
- Identify likely root causes
- Recommend next steps for remediation
🛠️ How It Works
Agentic Debugging can be triggered in two ways:
- Prompt-based: You provide a natural language query describing the issue or a symptom.
- Alert-based: You click Debug directly from the Alerts Inbox, where the AI automatically begins analysis based on the alert metadata.
🔄 Integration-Based Behavior
| Scenario | Behavior |
|---|---|
| No integrations connected | The agent provides general debugging guidance—questions to ask, commands to run, and logs to inspect manually. |
| Integrations connected | The agent actively runs queries and fetches data (e.g., logs, metrics, infra info), uses that to reason, and then shares the diagnosis and remediation suggestions. |
📋 Example
For example, if a Kubernetes pod is CrashLooping, and you have observability integrations configured:
- The agent may fetch logs, check deployment configurations, and diagnose the root cause (e.g., OOMKill, bad image).
- If the root cause is found, the agent may suggest a remediation such as “Restart the pod” or “Increase memory limit”.