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…

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 weren't good enough so we built chains and other architectures as a proxy as the models got better, the "right" way to build the most agentic systems changed pretty dramatically which meant the frameworks (like langchain) had to change pretty dramatically to keep up but now the models are good enough where "running the model in a loop calling tools" is actually starting to work there is still a lot to build (async subagents, multiagent systems) but the foundation is more solid. rather than frameworks that are moving like quicksand, i genuinely think these agent harnesses are the first stable building block we can build on top of them going forward, instead of rearchitecting them

Related

• The ‘agent harness’ is essentially the new runtime. It’s like a lightweight OS around an LLM—handling memory, tool calls, retries, and contr…
• anyways, try out deepagents https://github.com/langchain-ai/deepagents
• Agent harnesses are spark LangSmith is databricks
• Open Harness, separated from model providers is a critical architectural pattern.
• tldr > evals are the new training data. instead of updating weights, you're updating the agent harness > problem is agents are famous cheate…

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