Improving Deep Agents with harness engineering

TLDR: Our coding agent went from Top 30 to Top 5 on Terminal Bench 2.0. We only changed the harness. Here’s our approach to harness engineering (teaser: self-verification & tracing help a lot). The goal of a harness is to mold the inherently spiky intelligence of a model for tasks we care about. Harness Engineering is about systems, you’re building tooling around the model to optimize goals like task performance, token efficiency, latency, etc. Design decisions include the system prompt, tool choice, and execution flow. But how should you change the harness to improve your agent? At LangChain, we use Traces to understand agent failure modes at scale. Models today are largely black-boxes, their inner mechanisms are hard to interpret. But we can see their inputs and outputs in text space which we then use in our improvement loops. We used a simple recipe to iteratively improve deepagents-cli (our coding agent) 13.7 points from 52.8 to 66.5 on Terminal Bench 2.0. We only tweaked the harness…