🦄 ai that works: Agentic Backpressure Deep Dive
​In our next installment of advanced coding agent workflows, we'll explore some alternatives to research for improving results from coding agents. Code and web research is great for understanding the current codebase and finding documentation, but neither of these things is as concrete, and can still lead to hallucinations or incorrect assumptions.
​In this episode, we'll talk about learning tests and proof-driven-dev - writing small PoC programs and tests that lay the groundwork to confirm understanding of external systems, before you get deep into implementation.
​This will extend our previous conversation about agentic backpressure and building deterministic feedback loops to help coding agents work more autonomously.
​Meet the Speakers🧑‍💻
​ Meet Vaibhav Gupta, one of the creators of BAML and YC alum. He spent 10 years in AI performance optimization at places like Google, Microsoft, and D. E. Shaw. He loves diving deep and chatting about anything related to Gen AI and Computer Vision!
​Meet Dex Horthy, founder at HumanLayer and coiner of the term Context Engineering. He spent 10+ years building devops tools at Replicated, Sprout Social and JPL. DevOps junkie turned AI Engineer.