Building Better Coding Agent Harnesses at @LangChain we're thinking hard about the science of harness engineering + open research on what works & doesn't A quick peak on our deepagents X Terminal Bench 2.0 work, shoutout to @alexgshaw & Harbor (they're great). Broad research goals: 1. Find general purpose agent improvement recipes 2. Measure which design changes most affect model performance and how 3. Measure if/how models are non-fungible in their harness Some previews on what worked well: - Self-Verification & Iteration as first class citizens. Models are very good at self correction if they get a feedback signal, but they often won't participate in this loop. So designing prompts & deterministic hooks to force them into this helps a lot. - Automated Context Engineering: Pre-fetching some environment context up front avoids discovery errors for tools/files. - Large scale reflection over Traces is a powerful general recipe for stratifying errors + validating proposed improvements We'll be releasing a blog and research artifacts soon on all of this! Will return to measure more vectors of harness design + use codex-5.3 If you're interested in effective harness engineering and building great coding agents, would love to hear from you