Everything Hard About Building AI Agents Today

Everything Hard About Building AI Agents Today

Author: Demetrios June 13, 2025 Duration: 47:02

Willem Pienaar and Shreya Shankar discuss the challenge of evaluating agents in production where "ground truth" is ambiguous and subjective user feedback isn't enough to improve performance.


The discussion breaks down the three "gulfs" of human-AI interaction—Specification, Generalization, and Comprehension—and their impact on agent success.


Willem and Shreya cover the necessity of moving the human "out of the loop" for feedback, creating faster learning cycles through implicit signals rather than direct, manual review. The conversation details practical evaluation techniques, including analyzing task failures with heat maps and the trade-offs of using simulated environments for testing.


Willem and Shreya address the reality of a "performance ceiling" for AI and the importance of categorizing problems your agent can learn to solve, or will likely never be able to solve.


// Bio

Shreya Shankar

PhD student in data management for machine learning.


Willem Pienaar

Willem Pienaar, CTO of Cleric, is a builder with a focus on LLM agents, MLOps, and open source tooling. He is the creator of Feast, an open source feature store, and contributed to the creation of both the feature store and MLOps categories.


Before starting Cleric, Willem led the open source engineering team at Tecton and established the ML platform team at Gojek, where he built high-scale ML systems for the Southeast Asian decacorn.


// Related Links

https://www.google.com/about/careers/applications/?utm_campaign=profilepage&utm_medium=profilepage&utm_source=linkedin&src=Online/LinkedIn/linkedin_page

https://cleric.ai/


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Connect with Shreya on LinkedIn: /shrshnk

Connect with Willem on LinkedIn: /willempienaar


Timestamps:

[00:00] Trust Issues in AI Data

[04:49] Cloud Clarity Meets Retrieval

[09:37] Why Fast AI Is Hard

[11:10] Fixing AI Communication Gaps

[14:53] Smarter Feedback for Prompts

[19:23] Creativity Through Data Exploration

[23:46] Helping Engineers Solve Faster

[26:03] The Three Gaps in AI

[28:08] Alerts Without the Noise

[33:22] Custom vs General AI

[34:14] Sharpening Agent Skills

[40:01] Catching Repeat Failures

[43:38] Rise of Self-Healing Software

[44:12] The Chaos of Monitoring AI


Hosted by Demetrios, MLOps.community is a space for honest, meandering talks about the real work of making artificial intelligence systems actually work. This isn't about hype or theoretical papers; it's about the messy, practical, and often surprising journey of taking models from a notebook into a live environment. You'll hear from engineers and practitioners who are in the trenches, discussing the tools, the frustrations, and the occasional breakthroughs that define the day-to-day. The conversations are deliberately relaxed, covering everything from traditional machine learning pipelines to the new world of large language models and even the intangible "vibes" of team culture and process. Each episode peels back a layer on what "production" really means, whether that involves deploying a predictive service, managing an agentic system, or maintaining reliability as everything scales. Tuning into this podcast feels like grabbing a coffee with colleagues who aren't afraid to dig into the technical nitty-gritty while keeping the tone conversational and accessible. It's for anyone who builds, manages, or is just curious about the operational backbone that allows AI to deliver value, offering a grounded perspective often missing from the broader conversation.
Author: Language: en-us Episodes: 100

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