Check out what I made: An agent that monitors error logs and creates Jira tickets.

Jane D. · 2026-07-30T02:41:16Z

Hi everyone. I've been deep in the weeds with AutoGen for a few months now, primarily exploring how to bridge monitoring systems with developer workflows. I wanted to share a project that’s been saving my team a significant amount of time: an agentic system that automatically monitors application error logs and creates detailed, actionable Jira tickets. The core problem was our alert fatigue. We’d get a flood of error notifications, but triaging and translating them into properly scoped development tickets was still a manual, context-switching heavy process. This setup aims to emulate that initial triage step. Here’s a high-level view of the agent group I configured: * **Monitor Agent:** This is the "listener." It's configured to consume filtered error streams from our application monitoring tool (in our case, Sentry). It doesn't act on every single error, but uses logic to identify new, escalating, or critical error patterns. * **Analyst Agent:** This is the "brain." When the Monitor Agent signals a ticket-worthy event, it passes all the context (error message, stack trace, frequency, user impact) to the Analyst Agent. This agent's job is to summarize the issue, identify the likely service/component, and suggest a priority based on predefined rules. * **Jira Creator Agent:** This agent takes the structured output from the Analyst Agent. It formats a Jira ticket with a clear summary, description, labels, priority, and assigns it to the correct team's backlog based on the component mapping. It interacts directly with the Jira Cloud API. The real magic is in the conversation workflow and the custom prompts that guide each agent. For instance, the Analyst Agent is prompted to always ask itself: * Is this a new error signature or a recurrence? * What part of the codebase is implicated in the stack trace? * Based on keywords and our severity matrix, what's the suggested priority (P0-P3)? Some pitfalls and learnings from getting this to run smoothly: * **Information Overload:** The first version dumped the entire error payload into the conversation. We had to refine the Monitor Agent to extract only the most salient 10-12 lines of the stack trace and the key error message to keep the context window manageable. * **Idempotency is Key:** You must build in checks to avoid creating duplicate tickets for the same recurring error. We implemented a simple cache of recent error signatures that the Monitor Agent checks against before initiating a new conversation thread. * **Human-in-the-Loop Gate:** We initially had it create tickets automatically, but we’ve since added a final step where the proposed ticket is posted to a dedicated Slack channel for a team lead to approve with a simple emoji reaction before the Jira Creator Agent executes. This safety net has been invaluable. The result isn't fully autonomous, and it shouldn't be. It's a force multiplier that handles the tedious parts of synthesis and data entry, freeing up the engineering team to focus on actual problem-solving. It’s been running for about six weeks and has created over 60 well-structured tickets we might have otherwise missed or delayed. I'm happy to dive deeper into the specific agent configurations or prompt structures if anyone is interested. Has anyone else built similar bridges between monitoring/observability tools and task management systems? I'm particularly curious about alternative approaches to the deduplication problem. ~Jane

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cloud_watcher_99

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30/07/2026 11:16 pm

This is such a clever application of AutoGen! We built something conceptually similar for our container crash loops using Grafana logs, but you're using the Analyst Agent exactly right - making it the summarizer and scoping engine.

That first filtering logic on your Monitor Agent is the key. Getting it to only pass the *interesting* errors is the entire battle. If it's too sensitive, your Analyst gets overwhelmed with noise. Too lax, and you miss something brewing.

How are you handling the feedback loop? Like, when a dev closes the Jira ticket, does that signal get fed back to the Monitor Agent to adjust its thresholds for that error type? We're still trying to nail that part.

cost first, then scale

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