Miro has transformed its bug triaging process by integrating Amazon Bedrock’s foundation models and AI capabilities, reducing the time to resolve software issues from days to hours and minimizing team reassignments by a factor of six.
- Cut bug reassignment rates by 6x through AI-enhanced classification
- Accelerate time-to-resolution by 5x using multimodal data processing
- Streamline developer workflow with Slack-based bug routing integration
Infrastructure signal
Miro’s implementation relies heavily on Amazon Bedrock, a managed service providing access to multiple proprietary foundation models suitable for complex classification problems. This multi-model flexibility allows the infrastructure to handle diverse data types including text, screenshots, and video. The system enriches incoming bug reports with contextual information sourced from internal documentation, GitHub repositories, and issue tracking systems using retrieval-augmented generation (RAG). Leveraging Anthropic’s Claude Sonnet 4 model enhances the classification's precision by interpreting enriched, multimodal data inputs.
The solution architecture embraces dynamic organizational changes such as team restructuring and product updates by continuously updating knowledge bases and team responsibilities, ensuring classification remains relevant and timely. This adaptive infrastructure reduces cloud expenditure indirectly by minimizing inefficient bug-handling labor costs, while directly improving operational reliability through accurate routing that prevents cascading delays and resource waste.
Developer impact
Developers benefit from significantly reduced context-switching and frustration thanks to the high accuracy of bug classification. By correctly routing bugs on the first attempt, the system eliminates redundant investigations and handoffs, which traditionally prolonged resolution times. Root cause analyses generated optionally by the system equip engineers with immediate insights, further expediting troubleshooting.
The Slack-based integration of BugManager into everyday workflows simplifies adoption and minimizes disruption. Developers receive prioritized suggestions for routing accompanied by clear rationales, allowing manual overrides when necessary. This user-friendly interface reduces cognitive load and streamlines collaboration, directly enhancing team productivity and overall developer experience.
What teams should watch
Product and engineering leadership should monitor the evolving knowledge bases and team mappings to keep classifications accurate as organizational structures and product scopes change. Maintaining freshness in these data sources is critical to preventing misrouting and preserving the operational gains achieved so far. Observability around classification confidence scores and reassignment rates will provide leading indicators for intervention.
Infrastructure teams should ensure cloud resource allocation aligns with the demands of continuous multimodal data processing and prompt ingestion from multiple knowledge repositories. Optimization efforts might focus on scaling Bedrock model usage efficiently to balance cost with performance gains. Developer enablement groups can prepare to scale Slack bot functionality and training to smoothly onboard new users and refine workflows over time.