An AI coding agent can produce a convincing change and still leave your
team explaining the same conventions, checking the same things, and catching
the same mistakes. One engineer adds a prompt, another adds a rule, and the
next session starts the argument again. Everyone has a favourite setup.
The team still has the same problem. We are giving agents plenty of
feedback, but how much of it helps anyone beyond the person typing it?

From a forward-deployed engineer's perspective, I'll explore how teams can
build and improve a shared engineering harness: the tools, context, and
controls around their coding agents. We'll look at how to manufacture useful
backpressure so agents can correct their work before it reaches a human,
then use telemetry to find where that feedback is helping and where it is
creating expensive friction. I'll show how to refine noisy sensors, move
repeatable checks from model judgment into tools, and reduce token waste
without quietly handing more work back to reviewers. You'll leave with a
practical improvement cycle for your team: find a recurring failure, improve
the harness, measure the result, and share what works. The goal isn't more
rules. It's to stop paying for the same lesson twice.

AI-assisted engineering improves when teams treat the harness as a shared,
evolving engineering asset. Manufacture useful backpressure, observe what
happens across agent runs, identify recurring friction, and improve the
sensors and feedback paths. Evaluate whether each change reduces rework,
human review effort, and token cost without weakening acceptance standards.

Human attention is scarce, but the answer is not only to automate an
individual's repeated corrections. Turn useful discoveries into versioned,
evaluated team practices so the same lesson does not have to be learned by
every engineer in every session. Humans retain responsibility for intent,
customer fit, tradeoffs, and risk.

The harness supplies context, produces actionable evidence, and controls
when work may advance. Recurring structural checks can move from model
review into repeatable tools while humans and models retain responsibility
for judgment. The goal is useful feedback and justified friction, not the
largest possible collection of gates.