Harrison Chase cites Jev as an example decision model for agent harnesses
Speaking at Profound's NYC office about building agent harnesses, LangChain founder Harrison Chase named plugging in faster, cheaper decision models like Jev as a key advantage of a general-purpose harness.
The 🐐 @hwchase17 paid a visit to @profound HQ in NYC today to talk about building harnesses at the frontier. Takeaways: 1/ He has one of the sharpest definitions of what a harness is: the orchestration logic that gets the right context to the model at the right time. 2/ An off-the-shelf harness is better to start with compared to a model-specific SDK, as it has both extensibility and model flexibility. Combining access to faster and cheaper decision models like Jev is a great example. 3/ A context layer that only ingests sources into a structured set of context files is no longer enough. A great harness needs a context layer that relies on "online learning," where agents review interactions, surface gaps, and suggest updates continuously. 4/ Developing great Evals is essential for 1) defining a benchmark for what "good" looks like internally and 2) enabling hill-climbing to improve the harness. 5/ Agent tracing and observability close the loop. By evaluating each step in an agent run, teams can tell whether a harness is improving or adding friction. Awesome session, thank you Harrison for coming in!