Agentic AI costs can grow unexpectedly when workflows repeatedly send large prompts, retrieve excessive context, invoke expensive models, transfer verbose tool results, or allow agents to continue reasoning after a useful answer is available. These inefficiencies are difficult to detect when teams measure only total API spending rather than the economics of each workflow step.
This presentation develops a transparent, hypothetical enterprise scenario involving 10 million agent workflows per year. At illustrative blended rates of $2 per million input tokens and $8 per million output tokens, a baseline workload averaging 20,000 input and 2,000 output tokens per workflow would cost approximately $560,000 annually. Reducing average input usage to 7,000 tokens and output usage to 1,500 tokens would lower the modeled cost to approximately $260,000, representing a potential annual saving of $300,000 (~54%).
The session explains how an organization could achieve these reductions through semantic context selection, retrieval pruning and reranking, prompt compression, conversation summarization, tool-result compaction, model routing, caching, bounded agent loops, early termination, and token budgets. It also shows how quality evaluations and business-outcome metrics can prevent cost optimization from degrading accuracy, safety, or user experience.
Attendees will leave with a model-independent framework for measuring cost per successful outcome, locating token waste, prioritizing optimizations, and building financial guardrails for enterprise agent platforms.