Great breakdown that LLM + harness equation makes it click instantly!

Where's memory kept in that breakdown, inside the harness or counted as its own layer above it?

A useful way to explain Google's harness equation: replace the model and ask what still belongs to your application. Customer state, tool contracts and completion criteria should survive the swap. If they don't, a model upgrade becomes a workflow rewrite.

Great explainer AI Agent = LLM + Harness is the clearest definition of what makes agents actually work!

This is a helpful definition. The harness is where many practical safety decisions live too, including permissions, memory, tool boundaries, monitoring and what happens when the model is unsure.

What is an agent harness? @grok

Which harness controls should limit tool permissions when agents operate unattended?

Sounds like a recipe for AI success 🍰🤖! LLM+Agent Harness = Win combination, can't wait to tune in and see how @lukeschlangen and @smithakolan make it work, What do you guys think? 🤔

what part of the breakdown actually changes how you build

Great explanation making agent harnesses much easier to understand

Great breakdown finally makes the agent harness concept super clear

The wrapper around the model is doing more work than people admit

Great breakdown, agent harnesses finally explained simply

The harness is where reliability becomes a product surface: tool permissions, state checkpoints, retries, and traces. I’d benchmark those separately from the base model so regressions stay attributable.