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Glossary

Agent Credit Budget

A cost control mechanism that limits how many API credits (search queries, tool calls, LLM tokens) an AI agent can consume per task, per day, or per month, preventing runaway spending from agent loops, retry storms, or unexpectedly complex queries.

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Definition

A cost control mechanism that limits how many API credits (search queries, tool calls, LLM tokens) an AI agent can consume per task, per day, or per month, preventing runaway spending from agent loops, retry storms, or unexpectedly complex queries.

In Depth

AI agents make autonomous decisions about tool calls, which means they can generate unexpected API costs. An agent stuck in a retry loop, an overly thorough research task, or a misunderstood query can burn through hundreds of API calls in minutes. Credit budgets prevent this. Budget layers: (1) Per-task budget -- limit each user query to a maximum number of tool calls. Example: max 10 search queries per user question at $0.005/query = $0.05 max per question. (2) Per-session budget -- limit total tool calls within a conversation. Example: max 50 calls per session = $0.25 max. (3) Daily budget -- hard limit on total API spend per day. Example: $5/day = 1,000 Scavio queries. (4) Monthly budget -- overall spending cap. Example: $50/mo = 10,000 queries. Implementation pattern: wrap all tool call functions with a budget tracker that increments a counter and checks against limits before executing. Return a 'budget exceeded' message to the agent when limits are hit, forcing it to work with the data it already has. Budget sizing guidelines: (1) Simple Q&A agent: 2-3 calls/question, $0.01-0.015/question. 1,000 questions/day = $10-15/day. (2) Research agent: 5-15 calls/question, $0.025-0.075/question. 100 research tasks/day = $2.50-7.50/day. (3) Monitoring agent: fixed query count per run. 200 keywords tracked daily = $1/day. Alert thresholds: set alerts at 50% and 80% of monthly budget. If an agent hits 50% in the first week, investigate whether query volume is higher than expected or if the agent is making redundant calls.

Example Usage

Real-World Example

class BudgetTracker: def __init__(self, daily_limit: float = 5.0, per_query_cost: float = 0.005): self.daily_limit = daily_limit self.per_query_cost = per_query_cost self.daily_spend = 0.0 def can_query(self) -> bool: return self.daily_spend + self.per_query_cost <= self.daily_limit def record_query(self): self.daily_spend += self.per_query_cost budget = BudgetTracker(daily_limit=5.0) if budget.can_query(): result = search(query) budget.record_query()

Platforms

Agent Credit Budget is relevant across the following platforms, all accessible through Scavio's unified API:

  • Google
  • Amazon
  • YouTube
  • TikTok
  • Reddit

Related Terms

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Critic Loop Pattern

An AI agent design pattern where the model evaluates its own generated output against quality criteria, identifies factu...

Budget Search API

A web search API that provides structured search results at a total monthly cost under $50 for moderate usage (5,000-10,...

Frequently Asked Questions

A cost control mechanism that limits how many API credits (search queries, tool calls, LLM tokens) an AI agent can consume per task, per day, or per month, preventing runaway spending from agent loops, retry storms, or unexpectedly complex queries.

class BudgetTracker: def __init__(self, daily_limit: float = 5.0, per_query_cost: float = 0.005): self.daily_limit = daily_limit self.per_query_cost = per_query_cost self.daily_spend = 0.0 def can_query(self) -> bool: return self.daily_spend + self.per_query_cost <= self.daily_limit def record_query(self): self.daily_spend += self.per_query_cost budget = BudgetTracker(daily_limit=5.0) if budget.can_query(): result = search(query) budget.record_query()

Agent Credit Budget is relevant to Google, Amazon, YouTube, TikTok, Reddit. Scavio provides a unified API to access data from all of these platforms.

AI agents make autonomous decisions about tool calls, which means they can generate unexpected API costs. An agent stuck in a retry loop, an overly thorough research task, or a misunderstood query can burn through hundreds of API calls in minutes. Credit budgets prevent this. Budget layers: (1) Per-task budget -- limit each user query to a maximum number of tool calls. Example: max 10 search queries per user question at $0.005/query = $0.05 max per question. (2) Per-session budget -- limit total tool calls within a conversation. Example: max 50 calls per session = $0.25 max. (3) Daily budget -- hard limit on total API spend per day. Example: $5/day = 1,000 Scavio queries. (4) Monthly budget -- overall spending cap. Example: $50/mo = 10,000 queries. Implementation pattern: wrap all tool call functions with a budget tracker that increments a counter and checks against limits before executing. Return a 'budget exceeded' message to the agent when limits are hit, forcing it to work with the data it already has. Budget sizing guidelines: (1) Simple Q&A agent: 2-3 calls/question, $0.01-0.015/question. 1,000 questions/day = $10-15/day. (2) Research agent: 5-15 calls/question, $0.025-0.075/question. 100 research tasks/day = $2.50-7.50/day. (3) Monitoring agent: fixed query count per run. 200 keywords tracked daily = $1/day. Alert thresholds: set alerts at 50% and 80% of monthly budget. If an agent hits 50% in the first week, investigate whether query volume is higher than expected or if the agent is making redundant calls.

Agent Credit Budget

Start using Scavio to work with agent credit budget across Google, Amazon, YouTube, Walmart, and Reddit.

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