/observability-design

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