Real-Time Token Monitoring: The Pulse of AI

You cannot manage what you do not measure. In a production AI system, a "Token Spike" is just as critical as a "CPU Spike." If one agent version starts consuming 5x more tokens, you need to know In Seconds, not at the end of the month when you get the invoice.

Real-Time Token Monitoring is the practice of streaming usage metrics to a centralized dashboard (Prometheus, Grafana, or Datadog).

In this lesson, we learn how to implement Telemetry Hooks, build Alerst, and visualize the "Token Efficiency Curve" for your entire stack.

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1. Metric 1: TPM (Tokens Per Minute) per Agent

This is your stability metric. If the Researcher_Agent usually uses 10k TPM but suddenly jumps to 100k TPM, you have a Recursive Loop (Module 9.1).

2. Metric 2: Input-to-Output Ratio

A healthy system usually has a consistent ratio.

• RAG heavy: High Input / Low Output.
• Creative heavy: Low Input / High Output.
• Wait, what's this?: If your Input and Output both climb together, you are likely repeating massive contexts in every turn.

graph LR
    A[Input Tokens] --> B[Prometheus]
    C[Output Tokens] --> B
    D[Latency ms] --> B
    
    B --> E[Grafana Dashboard]
    E --> F[Alerting: budget_over_threshold]
    
    style F fill:#f66

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3. Implementation: The Telemetry Hook (Python)

You should push your usage data to a time-series database.

Python Code: StatsD/Prometheus Integration

from prometheus_client import Counter, Histogram

# Metric Definitions
TOKEN_USAGE_COUNTER = Counter(
    "llm_token_usage_total", 
    "Total tokens consumed", 
    ["model", "agent_id", "type"] # type="input" or "output"
)

LATENCY_HISTOGRAM = Histogram(
    "llm_latency_seconds", 
    "Time to first token", 
    ["model"]
)

def log_completion_metrics(response, start_time, agent_id):
    # 1. Capture Time
    duration = time.time() - start_time
    LATENCY_HISTOGRAM.labels(response.model).observe(duration)
    
    # 2. Capture Usage
    usage = response.usage
    TOKEN_USAGE_COUNTER.labels(response.model, agent_id, "input").inc(usage.prompt_tokens)
    TOKEN_USAGE_COUNTER.labels(response.model, agent_id, "output").inc(usage.completion_tokens)

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4. Setting "Anomly Alerts"

Efficiency is about maintaining a Baseline.

• The Alert: "Alert: TPM for user 'John' has exceeded the 7-day average by 400%."

This allows your ops team to "Kill" a specific session or user account without shutting down the entire platform.

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5. Visualizing the "Efficiency Gradient"

In Grafana, plot: Total Cost / Total Successes. If this line is flat or down, your efficiency engineering (Modules 1-15) is working. If this line is climbing, your agents are becoming "Legacy Code" that needs a prompt refactor.

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6. Summary and Key Takeaways

1. Tag Everything: Use labels for Model, UserID, and FeatureID.
2. Observe the Ratio: High input + high output usually indicates history bloat.
3. Alert on Delta: Don't just alert on "High usage"; alert on "Unexpected growth."
4. Ops Visibility: Token metrics should be on the same screen as CPU/RAM metrics.

In the next lesson, Handling Burst Traffic without Token Burn, we look at چگونه to survive a "Viral Moment" on social media.

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Exercise: The Telemetry Dashboard

1. Imagine you have a dashboard showing 1M tokens/hour.
2. Identify a "Rogue Agent": One graph shows a sharp spike in Output tokens only.
   • What happened? (Usually: The model got stuck in a repetitive loop).
3. Identify a "Content Leak": One graph shows a steady climb in Input tokens over 20 turns.
   • What happened? (Usually: You aren't pruning the conversation window correctly).
4. Calculations: How much would you have lost if you didn't have these graphs for 24 hours?

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Congratulations on completing Module 16 Lesson 3! You are now an observability expert.