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Traces, Metrics, and Costs

Beginner explanation​

Traces tell the story of one run. Metrics summarize many runs. Cost tracking shows whether the product economics make sense.

Production explanation​

Agentic systems need visibility into model calls, tool calls, retrieval quality, approval pauses, error paths, token usage, and per-task cost. Otherwise debugging and budgeting become guesswork.

Real-world enterprise example​

A claims assistant traces every workflow step, including retrieval hit count, tool latency, approval duration, and final model cost. Operations uses this to find slow, expensive, or failure-prone paths.

Mermaid diagram​

TypeScript example​

export interface TraceRecord {
  runId: string;
  step: string;
  latencyMs: number;
  costUsd: number;
  error?: string;
}

Python example​

def total_run_cost(costs: list[float]) -> float:
    return round(sum(costs), 4)

Common mistakes​

  • storing only the final answer and not the steps that created it
  • tracking average latency without percentiles
  • ignoring tool cost and retrieval cost in total spend
  • no run identifier shared across services

Mini exercise​

Design the trace schema for one full workflow run, including IDs, timestamps, step names, and error fields.

Project assignment​

Add a trace record format and dashboard metric list to your main project.

Interview questions​

  • What is the difference between a trace and a metric?
  • Which cost signals matter most during early rollout?
  • How would you investigate a run that was correct but too slow?

Monetization angle​

Observability is part of making AI sustainable in production. It supports platform work, managed services, and enterprise support retainers.