Why AI Coding Agents Lose Context After the PR
Harness, Wednesday, November 4th, 2026
Harness explains why AI coding agents lose context once a pull request enters the delivery pipeline and how to carry it back.
Harness says pipeline evidence such as failed builds, vulnerable dependencies, latency regressions and incidents rarely reaches the coding agent that made the change.
It describes context engineering as selecting and maintaining what an agent knows for a task, distinct from agent memory.
The post cites an Augment Code benchmark in which adding its Context Engine improved results by up to 80 percent and a weaker model with good context outperformed a stronger one without it.
Harness describes how its Code Context Engine and Software Delivery Knowledge Graph supply code and delivery context, and separates current from planned capabilities.