AI Tool Calling Explained: Why “Done” Isn’t Proof

AI tool calling can fail even after the action succeeds. Learn how timeouts, retries and idempotency affect AI agent reliability—and what evidence an agent harness needs before saying “done.”  What happens when an AI creates a task, the service succeeds, and the response disappears? In this audio-first episode, Krish Adavi walks through a small local experiment: a blind retry creates a duplicate, while a stable operation identifier prevents an extra effect in a service designed to honor it.  Learn how to separate permission from confirmation, interpret ambiguous timeouts, and define what “done” actually means in an agent harness.  The experiment is an isolated, in-memory, single-process simulation. It uses no LLM, live accounts, payments or external actions. It demonstrates a mechanism—not production reliability, persistence or concurrency safety. Video diagrams are explanatory illustrations, not captured production app screens.  Daily 5 AI newsletter: https://a2zai.ai/briefs YouTube: https://www.youtube.com/@a2zai-ai Instagram: https://www.instagram.com/a2zai.ai/  CHAPTERS 00:00 Your AI says “Done” 01:02 An answer becomes an action 02:27 The local experiment 03:03 Failure before creation 03:30 A lost confirmation 04:08 Why blind retries duplicate work 05:17 One operation, one stable identifier 06:49 Same key, changed intent 07:51 Recovering without safe retries 09:06 Permission versus confirmation 10:18 Give status words precise meanings 11:37 Test failures, not just prompts 12:56 Where the harness fits 13:23 Three practical takeaways  PRIMARY REFERENCES Anthropic tool-use overview: https://platform.claude.com/docs/en/agents-and-tools/tool-use/overview AWS Builders Library — Making retries safe with idempotent APIs: https://aws.amazon.com/builders-library/making-retries-safe-with-idempotent-APIs/ Inspected October 20, 2026. Local evidence: tool-demo.py and tool-demo-results.json.  #AIAgents #ToolCalling #AgentHarness #Idempotency #AIEngineering