How to Manage Token Costs for Custom-built AI Agents
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Gartner states that by 2028, 70% of enterprise AI agent projects that lack dedicated context engineering will be abandoned or descoped due to cost overruns. Within this 2026 research report, they lay out where costs are compounding, how to apply proven mitigation strategies, and how to monitor architectural developments that will help you shift the economics of token costs.
Key Insights
Context Accumulation is the Main Cost Driver:
Every agent task assembles a context window from enterprise documents, tool schemas, compliance rules, and conversation history. Each step reingests this growing context, compounding costs across workflows.
Reasoning Tokens: An overlooked Multiplier:
Chain-of-thought models generate more tokens than their visible output that are billed at premium rates. This can account for the majority of the total cost per action, depending on the complexity of the task and context required.
Prompt Caching Has Selective Benefits:
Prompt caching helps selectively. Fast synchronous agent loops benefit substantially. Asynchronous workflows with human review or external delays exceed the lifetime of the cache.
Evaluation Overhead is Often Omitted:
Eval costs compound on top of inference. LLM-as-a-judge pipelines add a further inference call for every assessed action.
When you download this report, you’ll learn the three disciplines for cost management for AI agents:
Context Engineering:
What goes into the context window and how to design context budgets
Agent Design:
How to scope agent behavior with explicit boundaries and clear objectives
Operational Visibility:
How to track tokens per workflow as well as how to fine-tune your reasoning levels
