// CLAUDE AGENTS · COST GOVERNANCE

Claude Budget Limits, Cost Governance & Prompt Caching

Enterprise token economics for autonomous agents: mastering Anthropic Prompt Caching mechanics, configuring tiered model cascades, enforcing hard spend circuit breakers, and reducing monthly TCO by up to 80%.

1. Prompt Caching: Slashing 90% of Input Token Costs

In multi-turn agent sessions, passing repository files or long documentation repeatedly quickly inflates operational expenses. Anthropic's Prompt Caching solves this: prefix tokens flagged with cache breakpoints receive an automatic 90% discount upon cache hits:

flowchart LR
    Turn1[Turn 1: Ingest 100k Repo Context] --> WriteCache[Write to Cache: Standard Cost + 25%]
    WriteCache --> CacheStore[(5-Minute TTL Cache Store)]
    Turn2[Turn 2: Follow-up Question] --> CheckCache{Cache Hit?}
    CheckCache -- Yes --> ReadCache[Read 100k Tokens: 90% DISCOUNT]
    ReadCache --> Model[Generate Tool Response]
    CheckCache -- No --> WriteCache

2. Tiered Model Cascading Matrix

Deploying Claude Opus 5.5 for every single ping is an anti-pattern. Smart routing routes routine triage to Haiku, standard workflows to Sonnet, and reserves Opus for deep verified reasoning:

Step 1 · Inbound Triage

Claude Haiku 5.5 ($0.25 / M tok)

Classifies intent, extracts metadata, verifies permissions, and executes simple read operations.

Step 2 · Core Engineering

Claude Sonnet 5.5 ($3.00 / M tok)

Handles 85% of software engineering, tool calling, and multi-file code editing.

Step 3 · Escalation

Claude Opus 5.5 ($15.00 / M tok)

Escalated only when unit tests fail repeatedly or critical architectural validation is needed.

3. Tutorial: Automated Prompt Caching in Python

prompt_cache_agent.py

import os
from anthropic import Anthropic

client = Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"])

def cached_agent_session():
    # Long codebase documentation to cache across multi-turn sessions
    heavy_documentation = "Enterprise API Documentation..." * 2000

    response = client.messages.create(
        model="claude-3-7-sonnet-20250219",
        max_tokens=1024,
        system=[
            {
                "type": "text",
                "text": "You are a specialized enterprise assistant for the internal API.",
            },
            {
                "type": "text",
                "text": heavy_documentation,
                "cache_control": {"type": "ephemeral"} # Cache this block!
            }
        ],
        messages=[
            {"role": "user", "content": "How do I authenticate with the billing endpoint?"}
        ]
    )

    print(f"Input Tokens: {response.usage.input_tokens}")
    print(f"Cache Creation Tokens: {getattr(response.usage, 'cache_creation_input_tokens', 0)}")
    print(f"Cache Read Tokens: {getattr(response.usage, 'cache_read_input_tokens', 0)}")

if __name__ == "__main__":
    cached_agent_session()

1. Prompt Caching: Slashing 90% of Input Token Costs

2. Tiered Model Cascading Matrix

3. Tutorial: Automated Prompt Caching in Python

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