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APIs built for humans often break when consumed by AI agents. Documentation meant for developers does not help large language models (LLMs) or autonomous systems that expect machine-readable contracts, predictable outputs, and safe retries. As AI adoption accelerates, APIs must evolve to be machine-discoverable, deterministic, idempotent, and lifecycle-managed.
This talk introduces a five-phase API readiness framework, from discovery to deprecation, that helps you systematically evolve APIs for safe and reliable AI consumption. You will learn how to assess your existing endpoints, score their readiness, and apply modern patterns like function/tool calling, schema validation, idempotency keys, and lifecycle controls such as Deprecation and Sunset headers. Real-world case studies from leading platforms will show what “agent-ready” APIs look like in production.
What You Will Learn
How to assess your APIs for AI readiness using a structured scorecard
Five phases to evolve APIs: Discovery → Redesign → Versioning → Monitoring → Deprecation
Key practices: schema validation, idempotency, deterministic responses, and lifecycle guardrails
Common failure modes when LLMs and agents consume APIs
Practical security and governance measures aligned with OWASP LLM Top 10 and ISO/IEC 38507
Key Takeaways
API Readiness Scorecard and modernization checklist
Patterns for versioning, deprecation, and observability for AI-driven systems
Security and prompt-injection guardrails for safe agent integration
How to embed AI readiness audits into CI/CD governance
Who Should Attend
API architects, platform engineers, and software leads responsible for evolving existing APIs to support AI agents, automated workflows, and long-lived machine integrations.
