Blueprint: Analytical Budget Shield
Predictive Pricing Gating for Research Agents
Autonomous research agents (e.g., GPT-Researcher) can generate extremely long prompts by pulling in large amounts of web context. Without gating, a single request can cost several dollars.
This blueprint demonstrates Predictive Gating where the Gateway rejects a request before it is sent to OpenAI if the predicted cost exceeds a threshold.
Architecture
1. Master Policy Definition
{
"name": "analytical-research-policy",
"priority": 8500,
"rules": [
{
"id": "allow-research-model",
"effect": "allow",
"principals": ["agent:research-bot"],
"actions": ["llm.generate"],
"resources": ["model/gpt-4o"]
}
],
"cost_policy": {
"max_cost_per_request": 0.5
}
}
2. Implementation
Python Prototype
from openai import OpenAI
client = OpenAI(
api_key="ignored",
base_url="http://cz-gateway:8001/v1",
default_headers={"X-ControlZero-Agent-ID": "research-bot"}
)
def run_deep_research(context_snippets: list):
# Aggregated context could be 100k+ tokens
full_prompt = "Summarize these documents: " + " ".join(context_snippets)
try:
# The Gateway calculates the cost based on prompt length
# and current GPT-4o pricing (e.g., $5 per 1M tokens)
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": full_prompt}]
)
return response.choices[0].message.content
except Exception as e:
# Gateway returns a 403 with the predicted cost
return f"Budget Intervention: {e}"
# Scenario: Massive context that would cost $0.75
massive_context = ["Relevant data..."] * 150000
print(run_deep_research(massive_context))
3. Validation Checklist
- Price Table Update: Ensure the Control Zero engine has the latest pricing data for the models being used.
- Token Estimation: Verify that the tokenizer used by the Gateway (tiktoken) matches the upstream provider's calculation.
- Error Messaging: Confirm the 403 message includes both the
predicted_costand themax_allowedvalue. 埋