When I finally instrumented my agents' tool calls, the cost breakdown surprised me. A few lessons.

Reddit r/AI_Agents Tools
agent-cost tool-calls cost-monitoring latency attribution mcp

Summary

The author shares lessons from instrumenting AI agent tool calls, revealing that tools like web_search can account for ~50% of spend, and highlighting the importance of tracking p95 latency and attributing costs per workflow or customer to avoid surprises.

TL;DR of what I learned after I started measuring every MCP/tool call my agents make: * **A couple of tools ate \~50% of spend.** `web_search` alone was the biggest line by far. I'd have guessed the LLM was the cost; a lot of it was tools. * **p95 latency, not average, is what hurts users.** One provider had a fine average but a brutal p95 that was tanking UX. * **No attribution = no accountability.** I couldn't answer "which workflow/customer cost the most last week" until I tagged calls. Most teams find this out a month late, via the invoice. Tagging calls per workflow/customer + watching p95 + a budget alert fixed most of my blind spots. I ended up building a tool for this (MCPSpend — disclosure: I'm the founder), but the lessons stand regardless of what you use. **How are you attributing agent costs to specific customers or workflows today — anything that works well, or is it still a black box for you too?**
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