Unlocking Token Costs in Multi-Tool AI Agents
In the ever-evolving landscape of AI, understanding token costs is paramount for developers. I built an agent framework from scratch to analyze token usage across tools without the noise of libraries or abstractions.
Key Insights Include:
- Setup Structure:
- 6 tools implemented (metrics, alerts, etc.)
- Utilization of gpt-4o-mini
- Four phases of token instrumentation
- Phases of Analysis:
- Phase 1: Baseline with a single tool (590 tokens).
- Phase 2: Introducing six tools (1,250 tokens).
- Phase 3: Chained calls elevate usage (4,500 tokens).
- Phase 4: Multi-turn conversations spike costs (7,166 tokens).
Notable Findings:
- Adding tools doubles costs.
- Increasing conversation turns triples them.
Implications:
Token expenses compound in real-world applications. Poor architecture can lead to exponential costs!
Stay tuned for more insights on mitigating token expansion. How are you navigating this challenge? Share your experiences below!
