AI agent development focuses on production feedback and security auditing

New developments in AI agent management focus on improving production feedback loops and enhancing security through specialized tools.

To improve AI agents after deployment, teams must bridge the gap between AI engineers and site reliability engineers. Effective improvement requires a continuous cycle of running models, observing behavior, curating data, improving the system, and evaluating results. This process helps identify failures and ensures that production traces are converted into actionable evaluation cases.

In terms of security, the agent-ledger has been released as a free, open-source MIT plugin for Hermes Agent. The tool provides a security and audit layer by recording tool calls in a local SQLite database before execution. It aims to answer what an agent has done and how risky its behavior is by scoring actions against the OWASP Top 10 for Agentic Applications 2026. The plugin can detect issues such as tool misuse, unexpected code execution, cascading failures, and rogue agent behavior through its ledger and posture scoring system.