Controlling Cost and Latency in Agentic AI Systems

Asked in an AI Engineer Interview

Interviewer: A simple customer request causes an agent to make 12 LLM calls and 8 tool calls, increasing cost and latency significantly.

Question: How would you redesign the agent orchestration and AI Gateway to control token usage, tool calls, model selection, and execution budgets?

Explanation:

1. Set execution budgets: Define maximum LLM calls, tool calls, tokens, and execution time per request.

2. Use model routing: Send simple tasks to smaller, cheaper models and complex reasoning to larger models.

3. Reduce LLM calls: Combine related reasoning steps instead of calling the LLM repeatedly.

4. Control tool usage: Allow tools only when necessary and prevent repeated calls with the same inputs.

5. Cache results: Cache repeated LLM responses, embeddings, and tool results.

6. AI Gateway: Centralize model routing, rate limits, token limits, cost tracking, retries, and policies.

7. Early stopping: Stop the agent once the required answer is obtained instead of continuing unnecessary reasoning.

8. Monitor: Track tokens/request, LLM calls, tool calls, latency, cost, and success rate.

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When an AI agent handling a simple customer request ends up making an excessive number of LLM and tool calls, driving up cost and latency, the fix involves redesigning both agent orchestration and the AI Gateway layer. This includes setting execution budgets that cap LLM calls, tool calls, tokens, and execution time per request, and using model routing to send simple tasks to smaller, cheaper models while reserving larger models for complex reasoning. Reducing LLM calls by combining related reasoning steps, controlling tool usage to prevent unnecessary or repeated calls, and caching repeated LLM responses, embeddings, and tool results all help limit redundant work. A centralized AI Gateway can manage model routing, rate limits, token limits, cost tracking, retries, and policies in one place, while early stopping ends the agent's process once a sufficient answer is reached rather than continuing unnecessary reasoning. Finally, monitoring metrics such as tokens per request, LLM and tool call counts, latency, cost, and success rate supports ongoing optimization of the system.