The New Stack: How to Turn AI Production Feedback Into Better Agents

What the Article Covers

The New Stack has published an article by Chen Goldberg titled "How to turn AI production feedback into better agents," dated Oct 10th 2026, 12:00pm. The title frames the central question facing teams that have moved AI agents beyond demos: how to capture the signals agents generate in production and use them to improve the agents themselves over time.

The Broader Agent Push

The article appears amid a wave of AI agent coverage across the publication's channels. Engineering headlines note that agents have made CI the bottleneck and argue that faster pipelines are the wrong fix. Operations coverage argues that AI changed how teams respond to incidents, but tabletops have not caught up, and that most AI incident tools still leave a key decision to humans.

Other pieces point at infrastructure gaps: agents stall without infrastructure context, data access slows agents down, and handing an agent credentials raises questions about what it inherits in the audit trail.

Verification and Runtime Concerns

Verification is a recurring theme in the surrounding coverage. Recent headlines note that Greptile, Cursor, and Devin agree that agents should run their code, and that what they run it against matters. A separate piece argues that agentic development hinges on verification, and that for cloud-native software this is a runtime problem.

Industry Moves

The article follows a busy stretch of vendor news tied to agents. AWS open-sourced an AI agent it says is 45% cheaper than Claude Code and Codex. Harness bought Augment's coding agents. OpenSearch veterans launched Infino for agent builders. SAP acquired TechWolf to feed more context into its HR agents, and its CEO is quoted describing the effort as having "not a room for error" as SAP puts AI agents in the back office.

Sources: The New Stack