Section 11: Production: Scale, Latency and Cost

SLIDE 74: Section Intro, Production Deployment

Section 11: Production: Scale, Latency and Cost

A demo agent handles one user at a time on a developer’s laptop. A production agent handles 10,000 concurrent users across distributed infrastructure, under latency constraints, within a budget. The gap between “it works” and “it works at scale, reliably, affordably” is where most agent projects fail. This section covers the engineering required to cross that gap: how to model and reduce cost, how to minimize latency, how to cache intelligently, and how to scale infrastructure horizontally.

Every architectural decision from previous sections has a cost and latency implication. The model selection from Section 2, the execution patterns from Section 3, the tool calls from Section 4, the context management from Section 5, the RAG pipeline from Section 6, the multi-agent coordination from Section 7, and the reflection loops from Section 8 all contribute to the per-interaction cost and latency of your production agent. This section ties those decisions together through the lens of production economics.

What We’ll Cover:

1. Token economics and cost modeling
2. Caching strategies: prompt cache, semantic cache, tool result cache
3. Latency optimization techniques
4. Scaling agent infrastructure horizontally
5. Worked example: scaling the support agent to 10,000 concurrent conversations

Where This Fits in the Customer Support Agent:

Our support agent works in a demo. Now we need to deploy it to handle 300,000 conversations per month at under $0.15 per conversation with a 3-second response latency target. That requires caching, model routing, infrastructure scaling, and careful cost engineering. This section builds the production deployment plan.

SLIDE 75: Token Economics and Cost Modeling

How LLM Pricing Works

LLM APIs charge per token, with separate rates for input tokens (what you send: system prompt, context, conversation history, retrieved chunks) and output tokens (what the model generates: the response). Output tokens are typically 3-5x more expensive than input tokens because generation requires more computation than processing input.

Token Pricing Reference (approximate, varies by provider)