Agent Observability

Monitor AI agent behavior with logs, traces, metrics, and cost telemetry. This skill covers the full observability stack for LLM-powered applications: from raw Prometheus counters to Grafana dashboards, OpenTelemetry tracing, structured logging, cost tracking, SLO definition, and PII redaction.

---

When to Use

Apply this skill whenever you operate:

• Autonomous AI agents that make multi-step tool calls (e.g., coding agents, support agents, data-pipeline agents).
• LLM-backed APIs serving chat completions, summarisation, or classification behind a REST or gRPC gateway.
• RAG pipelines where a retriever fetches context from a vector store before prompting a model.
• Multi-agent orchestrations (crew-style or graph-based) where several agents collaborate on a single task.
• Batch inference jobs that process thousands of prompts against a model endpoint.

Key signals that you need this skill:

1. You cannot answer "what is p95 latency for agent responses this week?"
2. You have no per-request cost attribution.
3. Debugging a bad agent response requires grepping raw application logs.
4. You have no alerting on token-usage spikes or elevated error rates.

---

Core Metrics

Define these metrics at the application layer. All examples use the Prometheus client library naming conventions.

Latency

from prometheus_client import Histogram

# Total end-to-end latency for a full agent turn (user prompt -> final response)
AGENT_LATENCY = Histogram(
    "agent_request_duration_seconds",
    "End-to-end latency of an agent request",
    labelnames=["agent_name", "model", "status"],
    buckets=(0.25, 0.5, 1, 2, 5, 10, 30, 60, 120),
)

# Latency of a single LLM API call (one completion request)
LLM_CALL_LATENCY = Histogram(
    "llm_call_duration_seconds",
    "Latency of an individual LLM API call",
    labelnames=["model", "provider", "stream"],
    buckets=(0.1, 0.25, 0.5, 1, 2, 5, 10, 30),
)

# Latency of tool/function calls executed by the agent
TOOL_CALL_LATENCY = Histogram(
    "agent_tool_call_duration_seconds",
    "Latency of a tool call executed by the agent",
    labelnames=["tool_name", "agent_name", "status"],
    buckets=(0.05, 0.1, 0.25, 0.5, 1, 2, 5, 10),
)

Token Usage

from prometheus_client import Counter, Histogram

PROMPT_TOKENS = Counter(
    "llm_prompt_tokens_total",
    "Total prompt tokens sent to the model",
    labelnames=["model", "agent_name"],
)

COMPLETION_TOKENS = Counter(
    "llm_completion_tokens_total",
    "Total completion tokens received from the model",
    labelnames=["model", "agent_name"],
)

CACHED_TOKENS = Counter(
    "llm_cached_tokens_total",
    "Prompt tokens served from KV-cache (provider-reported)",
    labelnames=["model", "agent_name"],
)

TOKENS_PER_REQUEST = Histogram(
    "llm_tokens_per_request",
    "Total tokens (prompt + completion) per request",
    labelnames=["model", "agent_name"],
    buckets=(100, 500, 1000, 2000, 4000, 8000, 16000, 32000, 64000, 128000),
)

Cost

from prometheus_client import Counter

LLM_COST = Counter(
    "llm_cost_dollars_total",
    "Estimated cost in USD for LLM usage",
    labelnames=["model", "agent_name", "cost_type"],  # cost_type: prompt | completion
)

Tool Calls

from prometheus_client import Counter

TOOL_CALLS_TOTAL = Counter(
    "agent_tool_calls_total",
    "Total tool calls made by agents",
    labelnames=["tool_name", "agent_name", "status"],  # status: success | error | timeout
)

Errors and Retries

from prometheus_client import Counter, Gauge

LLM_ERRORS = Counter(
    "llm_errors_total",
    "Errors returned by the LLM provider",
    labelnames=["model", "provider", "error_type"],  # error_type: rate_limit | timeout | 5xx | auth
)

LLM_RETRIES = Counter(
    "llm_retries_total",
    "Retried LLM API calls",
    labelnames=["model", "provider", "retry_reason"],
)

AGENT_ACTIVE_REQUESTS = Gauge(
    "agent_active_requests",
    "Number of agent requests currently in flight",
    labelnames=["agent_name"],
)

---

OpenTelemetry Integration

Use the OpenTelemetry Python SDK to create traces that capture every step of an agent turn: the top-level request, each LLM call, each tool execution, and retrieval operations.

Setup

# otel_setup.py
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.resources import Resource

def init_tracing(service_name: str, otlp_endpoint: str = "http://localhost:4317"):
    resource = Resource.create({
        "service.name": service_name,
        "service.version": "1.0.0",
        "deployment.environment": "production",
    })
    provider = TracerProvider(resource=resource)
    exporter = OTLPSpanExporter(endpoint=otlp_endpoint, insecure=True)
    provider.add_span_processor(BatchSpanProcessor(exporter))
    trace.set_tracer_provider(provider)
    return trace.get_tracer(service_name)

Tracing LLM Calls

# llm_tracing.py
import time
from opentelemetry import trace
from opentelemetry.trace import StatusCode

tracer = trace.get_tracer("agent.llm")

def traced_llm_call(client, messages, model="gpt-4o", **kwargs):
    """Wrap an LLM completion call with a full OpenTelemetry span."""
    with tracer.start_as_current_span("llm.chat_completion") as span:
        span.set_attribute("llm.model", model)
        span.set_attribute("llm.provider", "openai")
        span.set_attribute("llm.message_count", len(messages))
        span.set_attribute("llm.temperature", kwargs.get("temperature", 1.0))
        span.set_attribute("llm.max_tokens", kwargs.get("max_tokens", 0))

        start = time.perf_counter()
        try:
            response = client.chat.completions.create(
                model=model, messages=messages, **kwargs
            )
            elapsed = time.perf_counter() - start

            usage = response.usage
            span.set_attribute("llm.prompt_tokens", usage.prompt_tokens)
            span.set_attribute("llm.completion_tokens", usage.completion_tokens)
            span.set_attribute("llm.total_tokens", usage.total_tokens)
            span.set_attribute("llm.duration_seconds", elapsed)
            span.set_attribute("llm.finish_reason", response.choices[0].finish_reason)
            span.set_status(StatusCode.OK)

            # Update Prometheus counters
            PROMPT_TOKENS.labels(model=model, agent_name="default").inc(usage.prompt_tokens)
            COMPLETION_TOKENS.labels(model=model, agent_name="default").inc(usage.completion_tokens)
            LLM_CALL_LATENCY.labels(model=model, provider="openai", stream="false").observe(elapsed)

            return response

        except Exception as exc:
            elapsed = time.perf_counter() - start
            span.set_status(StatusCode.ERROR, str(exc))
            span.record_exception(exc)
            LLM_ERRORS.labels(model=model, provider="openai", error_type=type(exc).__name__).inc()
            raise

Tracing Tool Execution

# tool_tracing.py
import functools
from opentelemetry import trace
from opentelemetry.trace import StatusCode

tracer = trace.get_tracer("agent.tools")

def traced_tool(tool_name: str):
    """Decorator that wraps a tool function with an OTel span and Prometheus metrics."""
    def decorator(func):
        @functools.wraps(func)
        def wrapper(*args, **kwargs):
            with tracer.start_as_current_span(f"tool.{tool_name}") as span:
                span.set_attribute("tool.name", tool_name)
                span.set_attribute("tool.args_count", len(args) + len(kwargs))

                import time
                start = time.perf_counter()
                try:
                    result = func(*args, **kwargs)
                    elapsed = time.perf_counter() - start
                    span.set_attribute("tool.duration_seconds", elapsed)
                    span.set_status(StatusCode.OK)
                    TOOL_CALLS_TOTAL.labels(
                        tool_name=tool_name, agent_name="default", status="success"
                    ).inc()
                    TOOL_CALL_LATENCY.labels(
                        tool_name=tool_name, agent_name="default", status="success"
                    ).observe(elapsed)
                    return result
                except Exception as exc:
                    elapsed = time.perf_counter() - start
                    span.set_status(StatusCode.ERROR, str(exc))
                    span.record_exception(exc)
                    TOOL_CALLS_TOTAL.labels(
                        tool_name=tool_name, agent_name="default", status="error"
                    ).inc()
                    TOOL_CALL_LATENCY.labels(
                        tool_name=tool_name, agent_name="default", status="error"
                    ).observe(elapsed)
                    raise
        return wrapper
    return decorator

# Usage
@traced_tool("web_search")
def web_search(query: str) -> str:
    # ... tool implementation ...
    pass

@traced_tool("sql_query")
def sql_query(statement: str) -> list:
    # ... tool implementation ...
    pass

Propagating Trace Context Across Services

# context_propagation.py
from opentelemetry import context
from opentelemetry.propagate import inject, extract
import httpx

def call_downstream_service(url: str, payload: dict) -> dict:
    """Propagate the current trace context to a downstream HTTP service."""
    headers = {}
    inject(headers)  # injects traceparent + tracestate headers
    response = httpx.post(url, json=payload, headers=headers)
    response.raise_for_status()
    return response.json()

def extract_context_from_request(request_headers: dict):
    """Extract trace context from incoming request headers (for the receiving service)."""
    ctx = extract(request_headers)
    token = context.attach(ctx)
    return token  # call context.detach(token) when done

---

Structured Logging

Emit JSON logs for every agent action so they can be ingested by Loki, Elasticsearch, or Datadog.

Python Logging Configuration

# logging_config.py
import logging
import json
import sys
from datetime import datetime, timezone

class AgentJSONFormatter(logging.Formatter):
    """Structured JSON formatter for agent logs."""

    def format(self, record: logging.LogRecord) -> str:
        log_entry = {
            "timestamp": datetime.now(timezone.utc).isoformat(),
            "level": record.levelname,
            "logger": record.name,
            "message": record.getMessage(),
            "module": record.module,
            "function": record.funcName,
            "line": record.lineno,
        }
        # Merge any extra fields attached to the record
        for key in ("trace_id", "span_id", "agent_name", "model",
                     "tool_name", "request_id", "user_id",
                     "prompt_tokens", "completion_tokens", "cost_usd",
                     "duration_seconds", "status", "error_type"):
            value = getattr(record, key, None)
            if value is not None:
                log_entry[key] = value

        if record.exc_info and record.exc_info[0] is not None:
            log_entry["exception"] = self.formatException(record.exc_info)

        return json.dumps(log_entry, default=str)


def configure_logging(level: str = "INFO"):
    handler = logging.StreamHandler(sys.stdout)
    handler.setFormatter(AgentJSONFormatter())

    root = logging.getLogger()
    root.setLevel(getattr(logging, level))
    root.handlers = [handler]

    # Suppress noisy libraries
    logging.getLogger("httpx").setLevel(logging.WARNING)
    logging.getLogger("opentelemetry").setLevel(logging.WARNING)

Logging Agent Actions

# agent_logging.py
import logging
from opentelemetry import trace

logger = logging.getLogger("agent")

def log_llm_call(model: str, prompt_tokens: int, completion_tokens: int,
                 duration: float, cost: float, status: str = "ok"):
    span = trace.get_current_span()
    ctx = span.get_span_context() if span else None
    logger.info(
        "LLM call completed",
        extra={
            "trace_id": format(ctx.trace_id, "032x") if ctx else None,
            "span_id": format(ctx.span_id, "016x") if ctx else None,
            "model": model,
            "prompt_tokens": prompt_tokens,
            "completion_tokens": completion_tokens,
            "duration_seconds": round(duration, 3),
            "cost_usd": round(cost, 6),
            "status": status,
            "agent_name": "default",
        },
    )

def log_tool_call(tool_name: str, duration: float, status: str, error: str = None):
    span = trace.get_current_span()
    ctx = span.get_span_context() if span else None
    extra = {
        "trace_id": format(ctx.trace_id, "032x") if ctx else None,
        "span_id": format(ctx.span_id, "016x") if ctx else None,
        "tool_name": tool_name,
        "duration_seconds": round(duration, 3),
        "status": status,
        "agent_name": "default",
    }
    if error:
        extra["error_type"] = error
    logger.info("Tool call completed", extra=extra)

Example log output:

{
  "timestamp": "2026-03-24T14:22:01.337Z",
  "level": "INFO",
  "logger": "agent",
  "message": "LLM call completed",
  "module": "agent_logging",
  "function": "log_llm_call",
  "line": 12,
  "trace_id": "0af7651916cd43dd8448eb211c80319c",
  "span_id": "b7ad6b7169203331",
  "model": "gpt-4o",
  "prompt_tokens": 1842,
  "completion_tokens": 356,
  "duration_seconds": 2.417,
  "cost_usd": 0.013770,
  "status": "ok",
  "agent_name": "support-agent"
}

---

Grafana Dashboards

Agent Overview Dashboard

Save this JSON as agent-overview.json and import it into Grafana.

{
  "dashboard": {
    "title": "AI Agent Overview",
    "uid": "agent-overview-v1",
    "tags": ["ai", "agent", "llm"],
    "timezone": "browser",
    "refresh": "30s",
    "panels": [
      {
        "title": "Request Latency (p50 / p95 / p99)",
        "type": "timeseries",
        "gridPos": { "h": 8, "w": 12, "x": 0, "y": 0 },
        "targets": [
          {
            "expr": "histogram_quantile(0.50, sum(rate(agent_request_duration_seconds_bucket[5m])) by (le))",
            "legendFormat": "p50"
          },
          {
            "expr": "histogram_quantile(0.95, sum(rate(agent_request_duration_seconds_bucket[5m])) by (le))",
            "legendFormat": "p95"
          },
          {
            "expr": "histogram_quantile(0.99, sum(rate(agent_request_duration_seconds_bucket[5m])) by (le))",
            "legendFormat": "p99"
          }
        ],
        "fieldConfig": {
          "defaults": {
            "unit": "s",
            "thresholds": {
              "steps": [
                { "color": "green", "value": null },
                { "color": "yellow", "value": 5 },
                { "color": "red", "value": 15 }
              ]
            }
          }
        }
      },
      {
        "title": "Token Usage (prompt vs completion)",
        "type": "timeseries",
        "gridPos": { "h": 8, "w": 12, "x": 12, "y": 0 },
        "targets": [
          {
            "expr": "sum(rate(llm_prompt_tokens_total[5m])) by (model)",
            "legendFormat": "prompt - {{ model }}"
          },
          {
            "expr": "sum(rate(llm_completion_tokens_total[5m])) by (model)",
            "legendFormat": "completion - {{ model }}"
          }
        ],
        "fieldConfig": {
          "defaults": { "unit": "short" }
        }
      },
      {
        "title": "Cost per Hour (USD)",
        "type": "stat",
        "gridPos": { "h": 4, "w": 6, "x": 0, "y": 8 },
        "targets": [
          {
            "expr": "sum(rate(llm_cost_dollars_total[1h])) * 3600",
            "legendFormat": "$/hr"
          }
        ],
        "fieldConfig": {
          "defaults": {
            "unit": "currencyUSD",
            "thresholds": {
              "steps": [
                { "color": "green", "value": null },
                { "color": "yellow", "value": 10 },
                { "color": "red", "value": 50 }
              ]
            }
          }
        }
      },
      {
        "title": "Error Rate (%)",
        "type": "gauge",
        "gridPos": { "h": 4, "w": 6, "x": 6, "y": 8 },
        "targets": [
          {
            "expr": "sum(rate(llm_errors_total[5m])) / (sum(rate(llm_call_duration_seconds_count[5m])) + 1e-10) * 100",
            "legendFormat": "error %"
          }
        ],
        "fieldConfig": {
          "defaults": {
            "unit": "percent",
            "min": 0,
            "max": 100,
            "thresholds": {
              "steps": [
                { "color": "green", "value": null },
                { "color": "yellow", "value": 1 },
                { "color": "red", "value": 5 }
              ]
            }
          }
        }
      },
      {
        "title": "Tool Call Success vs Failure",
        "type": "timeseries",
        "gridPos": { "h": 8, "w": 12, "x": 0, "y": 12 },
        "targets": [
          {
            "expr": "sum(rate(agent_tool_calls_total{status='success'}[5m])) by (tool_name)",
            "legendFormat": "ok - {{ tool_name }}"
          },
          {
            "expr": "sum(rate(agent_tool_calls_total{status='error'}[5m])) by (tool_name)",
            "legendFormat": "err - {{ tool_name }}"
          }
        ]
      },
      {
        "title": "Active Requests",
        "type": "timeseries",
        "gridPos": { "h": 8, "w": 12, "x": 12, "y": 12 },
        "targets": [
          {
            "expr": "sum(agent_active_requests) by (agent_name)",
            "legendFormat": "{{ agent_name }}"
          }
        ]
      }
    ]
  }
}

---

Cost Tracking

Per-Model Cost Calculation

# cost_tracker.py
from dataclasses import dataclass

@dataclass
class ModelPricing:
    prompt_cost_per_1k: float    # USD per 1,000 prompt tokens
    completion_cost_per_1k: float  # USD per 1,000 completion tokens

# Updated pricing as of early 2026 -- adjust to your negotiated rates
MODEL_PRICING: dict[str, ModelPricing] = {
    "gpt-4o":           ModelPricing(0.0025, 0.0100),
    "gpt-4o-mini":      ModelPricing(0.00015, 0.0006),
    "gpt-4.1":          ModelPricing(0.002, 0.008),
    "gpt-4.1-mini":     ModelPricing(0.0004, 0.0016),
    "gpt-4.1-nano":     ModelPricing(0.0001, 0.0004),
    "claude-sonnet-4":  ModelPricing(0.003, 0.015),
    "claude-haiku-3.5": ModelPricing(0.0008, 0.004),
    "claude-opus-4":    ModelPricing(0.015, 0.075),
}

def calculate_cost(model: str, prompt_tokens: int, completion_tokens: int) -> float:
    """Return estimated cost in USD. Falls back to zero if model is unknown."""
    pricing = MODEL_PRICING.get(model)
    if pricing is None:
        return 0.0
    prompt_cost = (prompt_tokens / 1000) * pricing.prompt_cost_per_1k
    completion_cost = (completion_tokens / 1000) * pricing.completion_cost_per_1k
    return prompt_cost + completion_cost

def record_cost(model: str, prompt_tokens: int, completion_tokens: int, agent_name: str = "default"):
    """Calculate cost and record it in the Prometheus counter."""
    pricing = MODEL_PRICING.get(model)
    if pricing is None:
        return
    prompt_cost = (prompt_tokens / 1000) * pricing.prompt_cost_per_1k
    completion_cost = (completion_tokens / 1000) * pricing.completion_cost_per_1k
    LLM_COST.labels(model=model, agent_name=agent_name, cost_type="prompt").inc(prompt_cost)
    LLM_COST.labels(model=model, agent_name=agent_name, cost_type="completion").inc(completion_cost)

Budget Alerting -- Prometheus Rules

Save as agent-cost-alerts.yaml and load it into Prometheus or Cortex ruler.

# agent-cost-alerts.yaml
groups:
  - name: agent_cost_alerts
    interval: 1m
    rules:
      # Fire if hourly spend exceeds $25
      - alert: AgentCostHourlyHigh
        expr: sum(rate(llm_cost_dollars_total[1h])) * 3600 > 25
        for: 5m
        labels:
          severity: warning
          team: ai-platform
        annotations:
          summary: "Agent LLM spend exceeds $25/hr"
          description: >
            Current hourly spend is ${{ $value | printf "%.2f" }}.
            Check for runaway loops, prompt-stuffing, or unexpected traffic.

      # Fire if daily projected spend exceeds $500
      - alert: AgentCostDailyProjectionHigh
        expr: sum(rate(llm_cost_dollars_total[1h])) * 86400 > 500
        for: 15m
        labels:
          severity: critical
          team: ai-platform
        annotations:
          summary: "Projected daily agent spend exceeds $500"
          description: >
            Projected daily spend: ${{ $value | printf "%.2f" }}.
            Consider throttling requests or switching to a cheaper model.

      # Fire if a single agent's cost spikes 3x above its 24h average
      - alert: AgentCostSpike
        expr: >
          sum(rate(llm_cost_dollars_total[5m])) by (agent_name)
          /
          (sum(rate(llm_cost_dollars_total[24h])) by (agent_name) + 1e-10)
          > 3
        for: 10m
        labels:
          severity: warning
          team: ai-platform
        annotations:
          summary: "Agent {{ $labels.agent_name }} cost spiked 3x above 24h average"

---

Langfuse / Helicone Integration

Langfuse (Self-hosted or Cloud)

Langfuse provides trace-level visibility with prompt management and scoring. It can run alongside your existing OTel stack.

# langfuse_integration.py
from langfuse import Langfuse
from langfuse.decorators import observe, langfuse_context

# Initialize -- reads LANGFUSE_SECRET_KEY, LANGFUSE_PUBLIC_KEY, LANGFUSE_HOST from env
langfuse = Langfuse()

@observe(as_type="generation")
def call_llm(client, messages, model="gpt-4o", **kwargs):
    """Langfuse automatically captures input/output, tokens, latency, and cost."""
    response = client.chat.completions.create(
        model=model, messages=messages, **kwargs
    )
    langfuse_context.update_current_observation(
        model=model,
        usage={
            "input": response.usage.prompt_tokens,
            "output": response.usage.completion_tokens,
        },
        metadata={"temperature": kwargs.get("temperature", 1.0)},
    )
    return response

@observe()
def run_agent(user_input: str):
    """Top-level agent trace -- all nested @observe calls become child spans."""
    langfuse_context.update_current_trace(
        user_id="user-123",
        session_id="session-abc",
        tags=["production"],
    )
    # ... agent logic with nested call_llm() and tool calls ...

Environment variables for Langfuse:

export LANGFUSE_SECRET_KEY="sk-lf-..."
export LANGFUSE_PUBLIC_KEY="pk-lf-..."
export LANGFUSE_HOST="https://cloud.langfuse.com"  # or your self-hosted URL

Helicone (Proxy-based)

Helicone acts as a logging proxy. Point your OpenAI base URL at Helicone and it captures everything automatically.

# helicone_integration.py
from openai import OpenAI

client = OpenAI(
    base_url="https://oai.helicone.ai/v1",
    default_headers={
        "Helicone-Auth": "Bearer sk-helicone-...",
        "Helicone-Property-Agent": "support-agent",
        "Helicone-Property-Environment": "production",
        "Helicone-User-Id": "user-123",
        "Helicone-Session-Id": "session-abc",
        "Helicone-Cache-Enabled": "true",       # enable response caching
        "Helicone-Rate-Limit-Policy": "100;w=60", # 100 req per 60s
    },
)

# All calls through this client are automatically logged in Helicone
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Summarise this document..."}],
)

---

SLO Definition