Nicolay Gerold works all day and night on AMP, one of the most interesting coding-agent harnesses out there.

If you’re building with coding agents, this conversation will help you understand:

* when to trust the model,

* when to build harnesses around it,

* which model is worth paying for,

* which programming languages gives the agent better feedback, and

* when to take the keyboard back.

Coding-agent products are living inside a blender. Opus 4.8 to Fable changes what the model can be trusted with, eats a workflow, and suddenly the best product decision is to delete code.

AMP had handoff because long agent threads used to get messy. Compaction would lose the plot, the model would make worse decisions, and the product needed a way to move the work somewhere cleaner. Then compaction got better. The model ate the feature. AMP killed it.

Builders inherit the annoying product test: does this harness code help inspect, verify, recover, or merge model work, or is it just babysitting yesterday’s model?

Nico and Hugo riff on why loop engineering is overrated (and when to use it), why Fable is the first model with real engineering taste, and why you should stop writing Python code today and start writing TypeScript and Rust for all your AI Engineering workflows.

You can also find the full episode on Spotify, Apple Podcasts, and YouTube.

👉 Want to build agents from the ground up? Registration is open for Build AI Agents from First Principles, a live workshop on the loops, tools, context, harnesses, and engineering decisions behind useful AI agents. You'll learn how to design agent systems from first principles, with enough structure to decide which harness patterns your product actually needs. Sign up today with vg-code for 10% off 👈

In This Episode

* Coding-agent harnesses today: compaction, sandboxes, review flows, and the features frontier models are starting to absorb.

* Why AMP keeps deleting its own features when models get better.

* The test for every harness feature: does it make the agent’s work easier to inspect, verify, or recover from?

* Local agents, cloud sandboxes, and where each fits when bugs, issues, logs, or customer feedback turn into code changes.

* Background agents without auto-merge fantasy: how useful work comes back as branches, checkouts, or review candidates.

* Loop engineering in practice: tight loops with clear objectives, broad loops that create review overload, and where builders should draw the line.

* When deterministic code beats an AI step, and when a single agent with the right tools can replace brittle orchestration.

* The TikTok problem for coding: hundreds of agent threads, fragmented attention, and why loop engineering can become a trap.- The TikTok problem for coding: hundreds of agent threads, fragmented attention, and why loop engineering can become a trap.

Resources

* AMP

* AMP Owner’s Manual

* Nicolay Gerold’s Show Us Your Agent Skills dossier

* Clio: Privacy-Preserving Insights into Real-World AI Use

* TigerBeetle TigerStyle

* How to Build A Coding Agent with Nico and Hugo

Build AI Agents From First Principles

👉 Want to build agents from the ground up? Registration is open for Build AI Agents from First Principles, a live workshop on the loops, tools, context, harnesses, and engineering decisions behind useful AI agents. You’ll learn how to design agent systems from first principles, with enough structure to decide which harness patterns your product actually needs. Sign up today with vg-code for 10% off. 👈

How You Can Support Vanishing Gradients

Vanishing Gradients is a podcast, workshop series, blog, and newsletter focused on what you can build with AI right now. Over 70 episodes with expert practitioners from Google DeepMind, Netflix, Stanford, and elsewhere. Hundreds of hours of free, hands-on workshops. All independent, all free.

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