AI Coding Agents Can Do So Much More (ft Kiran)

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Coding agents are revolutionizing software development, but are you getting the most out of them? Many AI coding tools struggle with complex, long-running tasks, often getting lost in the middle of a project. In this episode of Tool Use, we sit down with Kiran, one of the core contributors to Slate, a research-driven coding agent built for the toughest production-level challenges.

Kiran shares a deep dive into how to properly set up your dev environment to maximize the performance of any coding agent. We discuss the critical role of feedback loops, clear development pipelines, and consistent codebase patterns in getting correct, reliable code from LLMs. You'll learn why models are great at following patterns but terrible at creating them, and how to structure your projects for AI success. We also explore what makes Slate different, including its unique approach to context management that keeps it on track during complex tasks, preventing the performance degradation common in other agents.

Find Slate here:

https://randomlabs.ai/

Connect with us 

https://x.com/ToolUsePodcast

https://x.com/MikeBirdTech

00:00:00 - Intro

00:00:54 - Setting Up Your Dev Environment for AI

00:15:18 - Is Software Engineering Dead?

00:18:20 - Why Coding Agents? The Origin of Slate

00:21:52 - What Makes Slate Different?

00:33:19 - The Bottleneck: LLMs vs Tooling

00:44:30 - Why LLMs Get "Lost in the Middle"

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