Theory: Observability + Tracing

Overview

Observability answers the question: what happened, where, and why. In agent systems, this requires more than logs. You need request-level traces, step-level metadata, and correlated cost and latency metrics.

SLO-driven dashboards help teams prioritize what matters for users. Instead of watching many noisy charts, you track a small set of reliability and performance objectives with clear alert policy.

Learning Ladder

Level	Focus	Outcome
Beginner	Correlation IDs and step traces	Reconstruct one run end-to-end
Intermediate	Latency and cost metrics	Identify expensive or slow workflow steps
Advanced	SLO and anomaly policy	Trigger actionable alerts automatically

Core Concepts

Beginner Foundation

• Assign one stable correlation id to each request lifecycle.
• Emit trace events for each planner, tool, and model step.
• Log step status, duration, and key metadata consistently.
• Ensure logs are structured for machine parsing.

Intermediate Mechanics

• Add per-step token and infrastructure cost estimates.
• Aggregate end-to-end latency and cost per request.
• Track median and P95 latency by workflow path.
• Build drill-down views from high-level dashboards to raw traces.

Advanced Production Patterns

• Define SLO targets with explicit error budgets.
• Detect anomalies with rolling baselines and seasonality awareness.
• Route severe alerts into documented incident workflows.
• Review alert quality regularly to reduce noise and fatigue.

Key Takeaways

• High-quality tracing is required for debugging multi-step failures.
• SLOs align technical monitoring with user impact.
• Useful alerts are actionable, prioritized, and low-noise.