Real-world AI, explained simply — with code, use cases, and zero fluff. <br/><br/><a href="https://aipractitioner.substack.com?utm_medium=podcast">aipractitioner.substack.com</a>
The AI Practitioner Podcast
Claim This Podcastby by Lina Faik
17 episodes
Updated Daily
Accepts GuestsHas Sponsors
Podcast Overview
Real-world AI, explained simply — with code, use cases, and zero fluff. <br/><br/><a href="https://aipractitioner.substack.com?utm_medium=podcast">aipractitioner.substack.com</a>
Language
🇺🇲
Publishing Since
7/31/2025
1 verified contact email on file for The AI Practitioner Podcast
Pitch yourself as a guest, propose sponsorships, or reach out directly to the host.
Recent Episodes
July 30, 2026
PODCAST — Evaluating Google ADK Agents: From Execution Traces to Regression Tests
<p>Prefer reading instead? The full article is available <a target="_blank" href="https://open.substack.com/pub/aipractitioner/p/evaluating-google-adk-agents-from?r=49ttp&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true">here</a>. The podcast is also available on <a target="_blank" href="https://open.spotify.com/show/6MROBKvrjx0Mey8tHud5LX">Spotify</a> and <a target="_blank" href="https://podcasts.apple.com/us/podcast/the-ai-practitioner-podcast/id1830285899">Apple Podcasts</a>. Subscribe to keep up with the latest drops.</p><p>Building agents that take real-world actions is impressive, but ensuring they continue to behave correctly as they evolve is a much harder challenge. Manual reviews of execution traces and subjective “vibe checks” quickly become a development bottleneck.</p><p>In this fourth episode of the Google ADK series, the focus moves from deployment to evaluation and quality assurance. </p><p>You’ll learn:</p><p>* <strong>What should be evaluated in an agent system?</strong> Distinguishing final-response quality from tool use, execution trajectories, state handling, and end-to-end task success.</p><p>* <strong>How Google ADK represents evaluations.</strong> Understanding eval sets, evaluation cases, invocations, evaluation configs, and the metrics that operate on them.</p><p>* <strong>How to create, run, and inspect ADK evaluations.</strong> Recording evaluation cases, configuring metrics, executing evals from the CLI or Python, and interpreting the resulting scores and reports.</p><p>👉 Enjoyed this episode? Subscribe to <strong>The AI Practitioner</strong> to get future articles and podcasts delivered straight to your inbox: <a target="_blank" href="https://aipractitioner.substack.com/">aipractitioner.substack.com</a></p> <br/><br/>This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit <a href="https://aipractitioner.substack.com?utm_medium=podcast&utm_campaign=CTA_1">aipractitioner.substack.com</a>
July 10, 2026
PODCAST — Deploying Google ADK Agents: From Local Script to a Managed Cloud Runtime
<p>Prefer reading instead? The full article is available <a target="_blank" href="https://open.substack.com/pub/aipractitioner/p/google-adk-multi-agent-orchestration?r=49ttp&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true">here</a>. The podcast is also available on <a target="_blank" href="https://open.spotify.com/show/6MROBKvrjx0Mey8tHud5LX">Spotify</a> and <a target="_blank" href="https://podcasts.apple.com/us/podcast/the-ai-practitioner-podcast/id1830285899">Apple Podcasts</a>. Subscribe to keep up with the latest drops.</p><p>Multi-agent systems become valuable when they can reason and coordinate, but they become even more valuable when they are production-ready and can run reliably as a service.</p><p>In this third episode of the Google ADK series, the focus moves beyond agent design and orchestration to deployment. Using the same multi-agent writing pipeline from the previous articles, this podcast explores how to turn an ADK agent into a cloud-native service with durable state, secure credentials, observability, authentication, and a clear path for client applications.</p><p>You’ll learn:</p><p>* How to choose between Agent Engine, Cloud Run, and GKE for hosting an ADK agent</p><p>* How to externalize sessions, artifacts, memory, secrets, and model access for production</p><p>* How to test the deployed service, stream responses, configure CORS, and connect a frontend</p><p>👉 Enjoyed this episode? Subscribe to <strong>The AI Practitioner</strong> to get future articles and podcasts delivered straight to your inbox: <a target="_blank" href="https://aipractitioner.substack.com/">aipractitioner.substack.com</a></p> <br/><br/>This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit <a href="https://aipractitioner.substack.com?utm_medium=podcast&utm_campaign=CTA_1">aipractitioner.substack.com</a>
July 2, 2026
PODCAST — Google ADK Multi-Agent Orchestration: Delegation, Human-in-the-Loop, Callbacks, and Plugins (Part 2)
<p>Prefer reading instead? The full article is available <a target="_blank" href="https://open.substack.com/pub/aipractitioner/p/google-adk-multi-agent-orchestration?r=49ttp&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true">here</a>. The podcast is also available on <a target="_blank" href="https://open.spotify.com/show/6MROBKvrjx0Mey8tHud5LX">Spotify</a> and <a target="_blank" href="https://podcasts.apple.com/us/podcast/the-ai-practitioner-podcast/id1830285899">Apple Podcasts</a>. Subscribe to keep up with the latest drops.</p><p>Multi-agent systems become powerful when they can adapt, but they become production-ready only when that adaptation is controlled.</p><p>In this episode, we continue our Google ADK series and explore how ADK brings flexibility, structure, and reliability to multi-agent execution. We move beyond static workflows into dynamic orchestration: letting an agent decide which agent should act next, when a tool should be called, when a task should be escalated, and how the full execution path can be observed.</p><p>You’ll learn:</p><p>* <strong>How agents delegate work dynamically,</strong> including when to introduce Human-in-the-Loop review</p><p>* <strong>How to build orchestration logic</strong> <strong>beyond built-in workflow agents,</strong> using custom agents, agent-as-tool patterns, and callbacks</p><p>* <strong>How plugins help apply shared rules and behaviors</strong> across every agent in a system</p><p>👉 Enjoyed this episode? Subscribe to <strong>The AI Practitioner</strong> to get future articles and podcasts delivered straight to your inbox: <a target="_blank" href="https://aipractitioner.substack.com/">aipractitioner.substack.com</a></p> <br/><br/>This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit <a href="https://aipractitioner.substack.com?utm_medium=podcast&utm_campaign=CTA_1">aipractitioner.substack.com</a>
17 total episodes available
Similar Podcasts
Discover related shows you might enjoy
Deep-dive analytics for The AI Practitioner Podcast
Frequently asked questions
Have a different question and can't find the answer you're looking for? Reach out to our support team by sending us an email and we'll get back to you as soon as we can.
- What is The AI Practitioner Podcast?
- How often does this podcast release new episodes?
This podcast updates daily.
- Where can I listen to this podcast?
This podcast is available on 4 platforms including Apple Podcasts, Spotify, and more. You can also use the RSS feed directly.
- Does this podcast accept guests?
No, this podcast does not typically feature guests.
Legal Disclaimer
Pod Engine is not affiliated with, endorsed by, or officially connected with any of the podcasts displayed on this platform. We operate independently as a podcast discovery and analytics service.
All podcast artwork, thumbnails, and content displayed on this page are the property of their respective owners and are protected by applicable copyright laws. This includes, but is not limited to, podcast cover art, episode artwork, show descriptions, episode titles, transcripts, audio snippets, and any other content originating from the podcast creators or their licensors.
We display this content under fair use principles and/or implied license for the purpose of podcast discovery, information, and commentary. We make no claim of ownership over any podcast content, artwork, or related materials shown on this platform. All trademarks, service marks, and trade names are the property of their respective owners.
While we strive to ensure all content usage is properly authorized, if you are a rights holder and believe your content is being used inappropriately or without proper authorization, please contact us immediately at hey@podengine.ai for prompt review and appropriate action, which may include content removal or proper attribution.
By accessing and using this platform, you acknowledge and agree to respect all applicable copyright laws and intellectual property rights of content owners. Any unauthorized reproduction, distribution, or commercial use of the content displayed on this platform is strictly prohibited.