imo there’s a pretty solid default recipe that everyone should use to optimize a system of Agent = Model + Harness you should “train” both 1…

imo there’s a pretty solid default recipe that everyone should use to optimize a system of Agent = Model + Harness you should “train” both 1. Build v1 agent using a sensible base harness and some task specific prompting + tools 2. Harness Engineering using eval tasks that roughly match prod this is often enough - most companies can get acceptable perf doing this. then they collect traces, mine them for patterns, and make slight tweaks from there 3. SFT using data collected from traces) or synthetic data. Often is good candidate for “distillation tasks” to train a cheaper model while maintaining existing performance 4. RL if you have the bandwidth and ability and desire to create environments and designing rewards that represents the tasks you want your agent to be good at. Push past the SFT behavior of “copying” data from existing model to pushing past in some dimension 5. Light harness engineering again to squeeze any more juice (ex: slight prompting) using the trained model that’s better at your task distribution this loop will largely be productized as a general purpose recipe for building and improving agents we’re still in the earliest innings of the world’s companies getting comfortable with steps 1-2 of this loop. Harness engineering will probably be the dominant way ppl will optimize agents but i expect a large number of companies to onboard through this entire loop on some trial project of interest in the next year

Source: Harrison Chase (X) | 2026-06-07