Claude Agent Observability: Logging, Tracing, Debugging Agents

Originally published at claudeguide.io/claude-agent-observability

Claude Agent Observability: Logging, Tracing, and Debugging Production Agents

Production Claude agents need three observability layers: structured logging of every LLM call with token counts and latency, trace IDs that connect multi-turn conversations to individual requests, and a cost dashboard that shows per-user API spending before your bill arrives in 2026. Without these, debugging agent failures is guesswork and cost surprises are inevitable. This guide covers the full observability stack for production Claude agents, from structured logging to cost alerts.

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Why Agent Observability Is Different

Standard web application observability tracks HTTP requests: status codes, latency, errors. This covers the surface of agent behavior but misses the most important signals:

• What did the agent actually do? (tool calls, reasoning steps)
• Why did it give a bad answer? (context, instructions, model version)
• How much did each user cost? (token usage by conversation)
• Is the agent looping? (turn count anomalies)
• Did prompt caching work? (cache hit rate by conversation type)

You need purpose-built agent observability on top of standard infrastructure monitoring.

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Layer 1: Structured Logging

Every API call should emit a structured log event — not a print statement, a JSON record.

Python logging setup

python
import logging
import json
import time
import uuid
from dataclasses import dataclass, asdict
from typing import Any
import anthropic

# Configure structured logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger("claude_agent")


@dataclass
class LLMCallEvent:
    event_type: str = "llm_call"
    trace_id: str = ""
    session_id: str = ""
    user_id: str = ""
    model: str = ""
    input_tokens: int = 0
    output_tokens: int = 0
    cache_read_tokens: int = 0
    cache_write_tokens: int = 0
    latency_ms: float = 0.0
    stop_reason: str = ""
    tool_calls: list = None
    cost_usd: float = 0.0
    error: str = ""

    def __post_init__(self):
        if self.tool_calls is None:
            self.tool_calls = []


def calculate_cost(model: str, input_tokens: int, output_tokens: int,
                   cache_read_tokens: int = 0) -

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