Linear Digressions

Whether you work best solo or thrive in a team, you know collaboration is complicated — and it turns out AI agents face the same tensions. This episode dives into multi-agent systems, exploring how networks of AI agents can overcome the individual limitations of a single model, and what the research says about when collaboration actually helps versus when it just adds noise. Think scaling laws, but for teamwork.

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Website: https://lineardigressions.com
Apple Podcasts: https://podcasts.apple.com/us/podcast/linear-digressions/id941219323
Spotify: https://open.spotify.com/show/1JdkD0ZoZ52KjwdR0b1WoT
Substack: https://substack.com/@lineardigressions

Knowing when an AI agent has failed sounds straightforward — until it isn't. Agents have a frustrating habit of finishing confidently while quietly doing the wrong thing, or looping endlessly without ever crashing in an obvious way. This episode tackles one of the thorniest problems in the agentic world: evaluation. If failure is hard to see, how do you measure it systematically? And how do you know when your agent is actually working?

Despite what the marketing hype might suggest, AI agents are far from infallible — and if you've ever actually used one, you already know this. Today's episode dives deep into the many, varied, and sometimes surprising ways AI agents can fail, from subtle reasoning errors to cascading task breakdowns. It's episode six in the show's ongoing season arc on AI agents, and failure modes turn out to be a surprisingly rich topic worth unpacking in detail.

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Website: https://lineardigressions.com
Apple Podcasts: https://podcasts.apple.com/us/podcast/linear-digressions/id941219323
Spotify: https://open.spotify.com/show/1JdkD0ZoZ52KjwdR0b1WoT
Substack: https://substack.com/@lineardigressions

When tackling a complex, multi-step task, even the smartest AI agent can fail without a solid game plan. This episode dives into the research around agentic planning — how agents move beyond simply reacting to what's in front of them and instead model a path forward, explore different routes, and course-correct when things go sideways. It's a subtler problem than memory, and a fascinating one: can an agent actually *think ahead*? Tune in to find out what the research says.

Context windows are powerful — but finite, and surprisingly easy to overwhelm. When an AI agent is tackling a long, complex task, the information it needs has to fit inside that limited real estate, and research shows that anything buried in the middle tends to quietly disappear. So how do you design a system that actually *remembers* what matters? This episode digs into memory management for AI agents, from foundational computing concepts to practical lessons from tools like Claude Code.

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Website: https://lineardigressions.com
Apple Podcasts: https://podcasts.apple.com/us/podcast/linear-digressions/id941219323
Spotify: https://open.spotify.com/show/1JdkD0ZoZ52KjwdR0b1WoT
Substack: https://substack.com/@lineardigressions