Building the Claude API for the Frontier
Tune Claude API settings with your own evals, and optimize prompt caching first to control costs.
- Aim for high prompt-cache reuse by appending to stable prompts instead of changing cached content.
- Before changing models or settings, run task-specific evals for quality, token use, and outcomes.
- Reassess your agent harness as models improve; remove constraints or steps that are no longer needed.
The interview gives practical guidance, but includes no detailed setup walkthrough or demo. · Best for: Developers building Claude-powered products, especially agent workflows.
An interview about the Messages API and Claude Platform, covering customization, evaluation, migrations, cost controls, safety, and agent development.
Messages API and customization0:14
- The Messages API uses assistant and user turns, with tool calls in between.
- Settings such as effort and system prompts let developers shape behavior beyond ordinary prompting.
- The API's defaults reflect the team's preferred out-of-the-box experience.
Evals, model changes, and migrations3:10
- Evaluate changes during development, and run your own evals for your specific use case.
- Track task outcomes and token usage when changing models or settings.
- Use compatibility mechanisms such as beta headers to ease migrations; recheck the harness as models improve.
Prompt caching and cost8:27
- Prompt caching reuses previously processed blocks when later requests largely append to earlier ones.
- The speakers recommend prioritizing prompt-cache optimization as a major cost lever.
- Compare cost per task and the outcome achieved, not only cost per token.
Safety and safeguards11:28
- The API uses classifiers to reduce harmful use; access and safeguards can differ by model offering.
- The team evaluates safeguard variations and describes thinking protections as measures against distillation.
New capabilities and agents14:30
- Re-run evals on features that previously fell short when a more capable model becomes available.
- The Messages API supports agent workflows with thinking and effort controls, plus client-side tools.
- Claude managed agents is built on top of the Messages API.
Step by step
- 1. Identify a target workflow and define evals that measure task outcomes and token use.
- 2. Establish a baseline, then test model, effort, thinking, or prompt changes against it.
- 3. Optimize prompt caching by keeping repeated prompt content stable and appending new content where possible.
- 4. Compare cost per task alongside outcome quality; adjust model and settings based on eval results.
- 5. Reassess the harness as model capabilities change, and use migration mechanisms when needed.
Tools & resources mentioned
- Claude Messages API — build conversational and agent workflows.
- Prompt caching — reuse processed prompt blocks across similar or appended requests.
- Evals — test quality, cost, and workflow behavior for your own use case.
- Beta headers — provide a way to roll back some changes while updating a harness.
- Claude managed agents — managed agent offering built on the Messages API.
- Claude Code and Claude AI — first-party apps mentioned as examples of Messages API capabilities.
Notes are AI-generated from the video's transcript and may miss nuance.