observability-design

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