Technology Radar
In the previous Radar, we described the feedback flywheel as a way to improve a coding agent's harness from earlier sessions. Most developers update AGENTS.md files and agent skills when an agent gets something wrong, but what one developer learns may not be shared with the rest of the team. The feedback flywheel adds a question at the end of each session, asking whether anything should change in a shared artifact, along with a retrospective item where the team decides what to update. Feedback sensors show which checks keep failing, an agent suggestion box collects problems agents report and tools such as Backpass propose edits based on past sessions for a human to accept or reject. We're moving feedback flywheel to Trial because harness edits are now routine.
The flywheel needs to remove as well as add. Rules that only accumulate become agent instruction bloat, and a sensor that produces only noise should go. We recommend continuously checking which artifacts are still used and up to date. Context profiling shows whether edits are increasing what each session loads. Where a self-improving harness has an agent run the loop, the feedback flywheel makes it a team habit, with agents proposing changes and people choosing which changes to make.
Teams working with coding agents are increasingly adopting spec-driven development workflows. Whether they use a lightweight or opinionated framework, these workflows typically follow a similar flow of spec → plan → implement. The feedback flywheel extends this flow with an additional step focused on continuously improving the coding agent harness.
The approach is similar to retrospectives: teams capture successes and failures during a coding agent session and use them to improve the predictability of future sessions, which compounds over time. It’s a meta-technique where a human on the loop focuses on improving the feedforward controls such as curated shared instructions as well as feedback sensors for coding agents. Our teams have found this effective, as it is analogous to code refactoring. The next level looks more like an agentic feedback flywheel, where, based on accumulated feedback, the agent decides what improvements are necessary. For now, however, teams still need a human-in-the-loop to avoid context rot and noisy feedback that could lead the agent astray.
We suggest using this approach to evaluate the entire coding agent harness as your environment evolves, especially when adopting new models; what worked with one model may not be necessary with the next.
