AI Agent Run Cost Tracker

Agent run costs accumulate from multiple LLM calls: initial planning, tool calls, retries, and the final response. Each call has input tokens (context + history + tool results) and output tokens. Total cost = sum of all individual call costs. Agents typically make 3-10+ LLM calls per task, so cost per run can be 5-20x a single completion.

Input tokens grow with each step because agents carry conversation history, tool results, and context. A 5-step agent where each step adds 1000 tokens to context will have quadratic token growth. The final steps are the most expensive. This is why context compression and summarization are important cost controls.

Key strategies: (1) Use cheaper models for simple steps (GPT-4o mini for classification, GPT-4o for reasoning), (2) Compress/summarize conversation history, (3) Limit tool output size — truncate long results, (4) Cache repeated tool calls, (5) Use streaming to stop generation early if goal met, (6) Design tasks to minimize steps.

It depends on use case and value delivered. Simple Q&A agents: $0.001-$0.01. Research/analysis agents: $0.05-$0.50. Complex multi-step agents: $0.50-$5.00. Compare against the value delivered (employee time saved, revenue generated) to assess ROI. Track median and p95 costs to catch outliers.

Use LLM providers' usage APIs or middleware like LiteLLM, Langfuse, or Helicone. Log input/output tokens per call with trace IDs. Aggregate by agent_id, task_type, and user. Set budget alerts at 80% of monthly limits. Most providers offer per-minute rate limits that can cap runaway costs.