Overview
Building reliable agentic systems in production requires balancing three critical dimensions: cost, latency, and accuracy. The key insight is understanding that most accuracy improvements come at the expense of higher costs and latency, making strategic trade-offs essential for production deployment.
The Breakdown
- The three-dimensional trade-off framework - Every agentic system decision impacts cost (API calls, compute, infrastructure), latency (inference time, tool execution, network delays), and accuracy (task completion, output quality, error rates)
- Cost measurement strategy - Track per-request costs including LLM API calls, compute resources, and infrastructure, with costs varying significantly based on model choice and context length
- Latency optimization components - End-to-end timing includes LLM inference, tool execution, network latency, and sequential processing delays, with acceptable thresholds varying by use case
- Accuracy assessment methodology - Measure task completion rates, output quality, error rates, and consistency using task-specific metrics, human evaluation, and automated testing
- Decision framework for technique selection - Start by defining cost budgets, latency requirements, and accuracy targets, then choose optimization techniques based on these constraints rather than applying all available improvements