{"podcast":{"title":"Practical AI","slug":"practical-ai","podcast_index_feed_id":444526,"rss_url":"https://feeds.transistor.fm/practical-ai-machine-learning-data-science-llm","website_url":"https://practicalai.fm","image_url":"https://img.transistorcdn.com/WMlp2ug34XB6LDJ3-vnzti_-_y144LUlFW0Xzzn3fss/rs:fill:0:0:1/w:1400/h:1400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS8wMTZi/ZWJmNWIwNDdmYTcw/NGJjMTExZjNjZmYy/M2ZjNS5wbmc.jpg","author":"Practical AI LLC","episode_count":357,"summary":"Making artificial intelligence practical, productive & accessible to everyone. Practical AI is a show in which technology professionals, business people, students, enthusiasts, and expert guests engage in lively discussions about Artificial Intelligence and related topics (Machine Learning, Deep Learning, Neural Networks, GANs, MLOps, AIOps, LLMs & more). The focus is on productive implementations and real-world scenarios that are accessible to everyone. If you want to keep up with the latest advances in AI, while keeping one foot in the real world, then this is the show for you!","last_synced_at":null,"page_url":"https://stenobird.com/podcast/practical-ai"},"episode":{"title":"While loops with tool calls","slug":"while-loops-with-tool-calls","published_at":"2025-10-30T20:39:08+00:00","page_url":"https://stenobird.com/podcast/practical-ai/while-loops-with-tool-calls","show_page_url":"https://stenobird.com/podcast/practical-ai","url":"https://share.transistor.fm/s/ca41b93c","audio_url":"https://pscrb.fm/rss/p/dts.podtrac.com/redirect.mp3/media.transistor.fm/ca41b93c/a54b2afe.mp3","summary":"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.","meta_description":"Explore the evolution from prompt engineering to context engineering, the rise of coding agents, and the challenges of testing autonomous AI loops.","key_points":["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"],"chapters":[{"start_ms":60000,"title":"Introduction","summary":"Hosts Daniel Whitenack and Chris Benson welcome Jared Zoneraich, CEO of PromptLayer."},{"start_ms":265000,"title":"From Prompting to Context Engineering","summary":"A look at how the core architecture of LLM applications is shifting from simple input-output to complex context management."},{"start_ms":480000,"title":"The Evolution of AI Frameworks","summary":"Reflecting on how engineering approaches to LLMs have changed significantly over the last 18 months."},{"start_ms":685000,"title":"The Challenge of Evaluation","summary":"Discussing the difficulty of building heuristics and evaluations for increasingly autonomous systems."},{"start_ms":1070000,"title":"The Power of Tool Calling","summary":"How native support for structured outputs and tool calling has replaced hacky prompting methods."},{"start_ms":1475000,"title":"Testing Autonomous Loops","summary":"Analyzing the difficulty of unit testing the 'while loops' created by agents that iterate until a task is complete."},{"start_ms":2290000,"title":"The Future of Coding Agents","summary":"How coding agents are transforming engineering workflows and democratizing AI product development."}],"topics":["Prompt Engineering","Context Engineering","AI Agents","Tool Calling","LLM Evaluation","Coding Agents","Structured Outputs","AI Development"],"duration_seconds":2685,"processing_state":"processed","actions":[{"name":"request_transcript","method":"POST","url":"https://stenobird.com/v1/public/podcasts/practical-ai/episodes/while-loops-with-tool-calls/transcription-requests","description":"Idempotently request low-priority transcript generation for this episode."},{"name":"read_markdown","method":"GET","url":"https://stenobird.com/podcast/practical-ai/while-loops-with-tool-calls.md","description":"Read the agent-friendly Markdown representation of this episode resource."}]}}