AI Systems Brief with Krish

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.



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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



A left turn looks simple—until a van blocks your view and a pedestrian might step into the road. How does an AI driving system turn camera images into a decision?

Follow one imagined intersection through perception, uncertainty, possible futures, trajectory planning, onboard inference and feedback. Then learn why a convincing driving clip is not the same as a reliability test—and what AI builders can borrow from closed-loop evaluation.

AI Systems Brief with Krish — Episode 2

Audio-first: the explanation works without watching. The video adds original animated teaching diagrams.

Important: This episode discusses Tesla FSD Supervised, which requires an attentive driver ready to take over. It is an engineering explanation, not a driving tutorial or an assessment of an unsupervised service. The intersection and animations are conceptual—not actual Tesla footage, UI, or a disclosure of proprietary architecture. Cover artwork is AI-generated. The 44-feet example is arithmetic, not a Tesla latency benchmark or stopping distance.

CHAPTERS

00:00 How does a left turn become an AI decision?

01:50 Supervised does not mean autonomous

02:25 Images are not understanding

03:51 Why motion needs more than one frame

04:32 Hidden information is not a clear road

05:58 More than one possible future

07:50 A trajectory is more than a line

08:56 What end-to-end actually means

09:48 Training versus onboard inference

11:17 Why timing matters

12:53 Feedback: act, observe, repeat

13:39 Open-loop versus closed-loop testing

14:41 What this means for AI builders

15:15 Three lessons to take away

17:14 Next: finding the right source with RAG

SOURCES

Tesla FSD Supervised: https://www.tesla.com/support/fsd

Tesla engineering overview: https://www.tesla.com/AI

Tesla Model 3 manual: https://www.tesla.com/ownersmanual/model3/en_us/GUID-2CB60804-9CEA-4F4B-8B04-09B991368DC5.html

Public sources checked October 16, 2026. Vendor descriptions are not independent validation of performance.

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Follow AI Systems Brief with Krish for practical explanations of how AI systems work.

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AI Systems Brief with Krish — Episode 1

A short prompt can trigger a whole workflow. Follow a hypothetical hiking-boot return request through context gathering, tokens, prefill and decoding, KV caching, scheduling, tool calls and validation. Learn why a faster model does not always mean a faster useful result—and what builders should measure instead.

All return-policy details and timing numbers are illustrative, not a live store interaction or measured benchmark. Original explanatory animations accompany Krish's narration. The audio is self-contained.

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CHAPTERS

00:00 After you hit Send

01:36 The model and the application

03:12 Context, tokens and prefill

05:52 How the answer is generated

07:30 KV caching

09:04 Scheduling and the clocks

10:43 When the assistant acts

12:04 The ten-second example

13:39 Practical builder takeaways

15:15 The whole journey

SOURCES

https://developer.nvidia.com/blog/mastering-llm-techniques-inference-optimization

https://huggingface.co/docs/transformers/main/kv_cache

https://huggingface.co/docs/transformers/v5.14.0/continuous_batching_architecture

https://docs.nvidia.com/nim/benchmarking/llm/metrics

https://www.anthropic.com/engineering/building-effective-agents

#a2zai #AISystems #LLM #AIEngineering