Agentic Cost Optimization: Choosing Tools Wisely
Is your agent burning through credits? Learn how to make your agent choose tools based on cost and efficiency, not just functionality.
💸 Agents often pick tools purely on functional capability, overlooking real-world costs like API fees or execution time.
🎯 System Scenario & Requirements
System Goal
💸 Agents often pick tools purely on functional capability, overlooking real-world costs like API fees or execution time.
Actors & Components
- Request Gateway
- Policy & Router
- Target Executors
- Request Entrypoint
- Policy Router
- Target Handler
Functional Requirements
- 💸 Agents often pick tools purely on functional capability, overlooking real-world costs like API fees or execution time.
- Implement cost-aware tool selection to optimize your agent's spending and performance.
- You've built a powerful agent that can leverage various tools, but suddenly your cloud bill skyrockets, or your workflows are hitting rate limits.
Constraints & Assumptions
- The solution involves making your agent cost-aware and performance-aware in its tool selection process.
- This means augmenting your tool definitions with metadata about their real-world implications (e.g., cost per call , avg latency ms , rate limit per minute ).
⚡ Execution Stages & Decision Trace
Stage 1 — Tool Metadata & Decision Logic
Here's how you might augment your tool definitions and a simplified decision-making process:
The components in “Tool Metadata & Decision Logic” follow the mechanism explained in this stage.
Deep Dive
Deep Dive: Stage 1 — Tool Metadata & Decision Logic
Here's how you might augment your tool definitions and a simplified decision-making process:
# Enhanced Tool Definitions with Cost/Performance Metadata
tools_metadata = {
"get_weather_basic": {
"description": "Get current weather from a free, public API.",
"cost_per_call": 0.0,
"avg_latency_ms": 1500, # Slower response
"rate_limit_per_min": 10,
"accuracy_level": "standard"
},
"get_weather_premium": {
"description": "Get current weather from a paid, high-performance API.",
"cost_per_call": 0.01, # $0.01 per call
"avg_latency_ms": 200, # Faster response
"rate_limit_per_min": 1000,
"accuracy_level": "high"
}
}
# Agent's pseudo-code decision strategy (could be LLM prompt or programmatic)
def decide_weather_tool(task_details, user_preference="cost_optimized"):
if user_preference == "cost_optimized":
# Prioritize free/low-cost tools
return "get_weather_basic"
elif user_preference == "performance_critical":
# Prioritize fast/accurate tools, even if paid
return "get_weather_premium"
else:
# Default or fallback
return "get_weather_basic"
# Example usage:
# agent_task = "What's the weather in London?"
# selected_tool = decide_weather_tool(agent_task, user_preference="cost_optimized")
# print(f"Agent chose: {selected_tool}") # Output: Agent chose: get_weather_basic
Observable outcome: Here's how you might augment your tool definitions and a simplified decision-making process:
📊 System Outcome & Trade-offs
Observed User & Operator Behavior
User: Clients observe deterministic state without lost updates or race conditions.
Operator: Operators can verify stage outcomes against the scenario requirements.
Key Architectural Trade-offs
- The solution involves making your agent cost-aware and performance-aware in its tool selection process.
- This means augmenting your tool definitions with metadata about their real-world implications (e.g., cost per call , avg latency ms , rate limit per minute ).
⚠️ Failure Modes & Degraded Operation
The Solution & Trade-offs
The solution involves making your agent cost-aware and performance-aware in its tool selection process. This means augmenting your tool definitions with metadata about their real-world implications (e.g., cost per call , avg latency ms , rate limit per minute ). You can then instruct your Language Model (LLM) to consider these factors in its reasoning, or implement a programmatic "tool router" that makes these decisions before the LLM even sees the tool options. Metadata Enrichment: Add cost, latency, and rate limit data directly to your tool definitions.
Key learnings
- Here's how you might augment your tool definitions and a simplified decision-making process:
Apply Concepts from this Scenario
Test your understanding of the architecture decisions and trade-offs presented in this case study.