5 Patterns to Cut Your Agent's Token Bill
Blog post from Harper
Efficiently building agents involves optimizing the frequency and content of LLM calls, as early mastery can lead to successful production deployment while late realization may result in high costs. The text explores five architectural patterns to enhance these efficiencies, including prompt caching, parallel tool calls, planning execution, deterministic code paths, and semantic caching, with a focus on minimizing LLM interactions and their associated costs. Anthropic's prompt caching can significantly reduce costs by marking static prompt parts as cacheable, while parallel tool calls allow simultaneous data retrieval, reducing sequential interactions. Planning execution involves creating a structured plan for dependent tasks, minimizing unnecessary LLM calls, whereas deterministic code paths involve running known workflows directly in code, reserving LLM calls for language understanding and reasoning. Semantic caching can reuse past responses for similar queries, and pattern caching aims to store execution plans for repeated workflows, although it requires robust invalidation logic due to potential changes in inputs. Implementing these patterns can be complex, especially across distributed systems, but a unified runtime like Harper can simplify the process, reducing operational overhead and making advanced agent infrastructure more accessible.
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