AI agents fail in production due to lack of observability, reliability, and self-improvement mechanisms

Developers building AI agents struggle to deploy them reliably because they can't see what's going wrong, agents make repeated mistakes without learning, and managing multiple agent frameworks requires custom infrastructure. Current solutions lack integrated monitoring, debugging, and self-improvement capabilities, forcing teams to build fragile custom harnesses or abandon agent automation entirely.