---
name: observability-design
description: Making multi-agent workflows visible and debuggable for designers and developers.
---
# Observability Design
You can't improve what you can't see. Observability design makes the internal workings of multi-agent systems visible — so designers can understand user experience problems, developers can debug failures, and teams can improve the system over time.
## What to Make Observable
- **Workflow execution**: Which agents were involved, in what order, with what results
- **Decision points**: What decisions were made, what alternatives were considered, why one was chosen
- **Handoff details**: What context transferred between agents, was anything lost
- **Timing**: How long each agent took, where bottlenecks occur
- **Failures**: What failed, how it was recovered, what the user experienced
- **Quality signals**: Output quality scores, user satisfaction signals, task success markers
## Observability for Different Audiences
**For designers:**
- User journey view: What did the user experience across the whole workflow?
- Pain point identification: Where did users struggle, abandon, or express frustration?
- Quality patterns: Which outputs are high and low quality, and why?
**For developers:**
- Execution traces: Step-by-step log of agent actions
- Error logs: What failed and where
- Performance metrics: Latency, throughput, resource usage
**For product managers:**
- Usage patterns: Which workflows are used most, which are abandoned
- Success metrics: Task completion rates, user satisfaction trends
- Cost analysis: Resource consumption per workflow
**For users (optional):**
- Progress indicators: Where is the system in the workflow?
- Agent transparency: Which agent is handling their request?
- Audit trails: What the system did on their behalf
## Designing Observability Interfaces
- **Dashboards**: Real-time and historical views of system health and performance
- **Trace viewers**: Detailed step-by-step views of individual workflow executions
- **Alert systems**: Notifications when metrics exceed thresholds
- **Search and filter**: Ability to find specific executions by criteria
- **Comparison tools**: Compare performance across time periods, versions, or cohorts
## Observability Without Overload
Too much data is as bad as too little:
- **Layered detail**: Start with high-level summary, drill down on demand
- **Smart defaults**: Show the most important information first
- **Anomaly highlighting**: Surface unusual patterns automatically
- **Contextual views**: Different views for different questions
## Design Artefacts
- Observability architecture diagrams
- Dashboard specifications per audience
- Trace schema definitions
- Alert threshold configurations
- Observability tool requirements