How to test agent cost-efficiency with Currai

An agent that completes the task can still be too expensive to run.

Cost-efficiency is not the same as low cost. The question is whether the agent achieves the desired outcome with reasonable model calls, token usage, tool calls, and latency. Currai traces make that measurable.

Capture the full agent run

Agent cost hides across steps. One final answer might involve planning, retrieval, multiple tool calls, validation, retries, and a final model response. If those steps are not traced, the team can only see the invoice after the fact.

In Currai, the agent run should be one trace. Model calls are generations. Tool calls, retrieval, routing, validation, and MCP activity are spans. Usage and latency stay attached to the steps that produced them.

That structure lets teams answer where the cost came from.

Evaluate outcome and efficiency together

Do not optimize cost in isolation. A cheaper agent that fails more often is not better. A more expensive agent might be worth it if it solves high-value tasks more reliably.

Useful evals combine quality and efficiency:

• Did the agent complete the task?
• Did it call only necessary tools?
• Did it avoid repeated or circular steps?
• Did it stay under the expected latency budget?
• Did token usage fit the task complexity?
• Did the final answer satisfy the user's request?

Currai keeps these signals next to the trace, so the team can compare quality, latency, and cost together.

Look for repeated waste

The highest-value cost fixes usually come from repeated patterns:

• the agent calls a search tool before reading available context
• a planner produces long hidden reasoning for simple tasks
• retries happen after recoverable tool errors
• the same document is retrieved multiple times
• a high-cost model handles low-risk classification work

These are not billing problems. They are workflow design problems.

Compare versions on real traffic

When you change an agent, compare versions on production traces. A new routing strategy might reduce model calls. A cheaper model might increase retries. A tool schema change might improve success rate and reduce tokens.

Currai lets teams inspect those tradeoffs by prompt version, model, trace tag, workflow, or time window.

Define a cost-efficiency bar

Every agent needs a product-specific bar. For one workflow, a 10-second response may be acceptable. For another, it is unusable. For one task, a premium model is justified. For another, it is waste.

Write the bar down:

• maximum tool calls for common tasks
• target latency or time-to-first-token
• expected cost per successful task
• required success or eval score
• escalation behavior when the agent is uncertain

Then use Currai to measure against it.

Related: Cost and tokens, Observability for AI agents, and Budgets and alerts for LLM cost.