The ‘agent harness’ is essentially the new runtime. It’s like a lightweight OS around an LLM—handling memory, tool calls, retries, and contr…

The ‘agent harness’ is essentially the new runtime. It’s like a lightweight OS around an LLM—handling memory, tool calls, retries, and control flow. @hwchase17 im excited about agent harnesses because i think are the first stable agent abstractions we can build on top (which is why we're investing so much in deepagents) we always wanted to run llms in a loop and have them call tools (remember autoGPT? that's all that was) but the models…

im excited about agent harnesses because i think are the first stable agent abstractions we can build on top (which is why we're investing s…
here's how we're improving our base harness, you can apply these same lessons to hill-climbing for your application-specific harness!
Another banger article from the @LangChain team! Harness evolution combined with specialist local models will be the way forward undoubtedly…
Agent harnesses are spark LangSmith is databricks
tldr > evals are the new training data. instead of updating weights, you're updating the agent harness > problem is agents are famous cheate…
As agent systems scale, the control layer becomes the main source of complexity. What starts simple turns into coordination and composition …

Source: Harrison Chase (X) | 2026-04-10

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