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Design for failure

As explained in Generative AI in Action

A systems-engineering pattern that anticipates component failures and provides fallback, recovery, or degradation behavior.

Design for failure anticipates the operational risks of generative-AI services and applies established cloud and mission-critical architecture practices instead of relying on optimistic execution. section 1.7 section 1.8

A resilient enterprise integration should account for:

  • Latency: commercially available generative-AI APIs are complex and slower than traditional APIs. section 1.7
  • Transient failures: apply retry mechanics, including exponential backoff policies. section 1.7
  • Repeated work: use caching where appropriate to reduce avoidable calls and improve operational behavior. section 1.7
  • Enterprise exposure: retain security practices and extend existing architecture guidance to new AI endpoints. section 1.7 section 1.8

Enterprise API integration

An enterprise adding a generative-AI endpoint to an existing line-of-business system should preserve its current architecture practices, then add retry and exponential-backoff handling, caching, and security controls for the new dependency. section 1.7 section 1.8

How Generative AI in Action covers it

How deep it goes
Explains the architectural pattern and names retries, exponential backoff, caching, and security without implementation detail.
Examples
Uses commercially available cloud APIs and GPT-3.5 Turbo/GPT-3 cost-quality comparison as architectural context.
What it assumes
Assumes existing architecture guidance and cloud best practices rather than starting enterprise design from scratch.
What you can do after
Operate/configure enterprise generative-AI integrations with retries, exponential backoff, caching, security, and deliberate risk management.
How it connects
Connects to cloud best practices, retry mechanics, caching, security, cost and ROI, and AI governance.

This explainer was generated by Manning's knowledge graph from the text of Generative AI in Action. The section links point to the parts of the book it draws on.