# While loops with tool calls

Page: https://stenobird.com/podcast/practical-ai/while-loops-with-tool-calls
Text version: https://stenobird.com/podcast/practical-ai/while-loops-with-tool-calls.md
Podcast: [Practical AI](https://stenobird.com/podcast/practical-ai)
Published: 2025-10-30T20:39:08+00:00
Episode link: https://share.transistor.fm/s/ca41b93c
Audio file: https://pscrb.fm/rss/p/dts.podtrac.com/redirect.mp3/media.transistor.fm/ca41b93c/a54b2afe.mp3
Processing state: processed
JSON: https://stenobird.com/v1/public/podcasts/practical-ai/episodes/while-loops-with-tool-calls
Duration seconds: 2685

## Resource

The shift from prompt engineering to context engineering is enabling a new era of autonomous agents. This discussion explores how tool calling and 'while loops' allow models to iterate through tasks like humans do, rather than following rigid flowcharts.

## Highlights
- Main idea: Prompt engineering is evolving into context engineering, where the focus is on managing the environment and tools available to the model
- Practical takeaway: Use a 'crawl, walk, run' approach to deployment, starting with small, scoped agents that can be easily validated
- Failure mode: Autonomous 'while loops' in tool calling make systems harder to test and keep on the rails compared to static chains
- Main idea: The rise of coding agents like Claude Code demonstrates how models can now handle complex, multi-step workflows with minimal human instruction
- Practical takeaway: Success in AI products increasingly depends on the 'non-engineering taste' and domain expertise applied to the context provided to the model

## Topics

Prompt Engineering, Context Engineering, AI Agents, Tool Calling, LLM Evaluation, Coding Agents, Structured Outputs, AI Development

## Chapters
- 1:00 — Introduction: Hosts Daniel Whitenack and Chris Benson welcome Jared Zoneraich, CEO of PromptLayer.
- 4:25 — From Prompting to Context Engineering: A look at how the core architecture of LLM applications is shifting from simple input-output to complex context management.
- 8:00 — The Evolution of AI Frameworks: Reflecting on how engineering approaches to LLMs have changed significantly over the last 18 months.
- 11:25 — The Challenge of Evaluation: Discussing the difficulty of building heuristics and evaluations for increasingly autonomous systems.
- 17:50 — The Power of Tool Calling: How native support for structured outputs and tool calling has replaced hacky prompting methods.
- 24:35 — Testing Autonomous Loops: Analyzing the difficulty of unit testing the 'while loops' created by agents that iterate until a task is complete.
- 38:10 — The Future of Coding Agents: How coding agents are transforming engineering workflows and democratizing AI product development.

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