I got tired of AI agents silently failing in production, so I built a runtime control layer for them
Summary
The author built a runtime control layer to address the problem of AI agents failing silently in production environments.
Similar Articles
I built a durable runtime for agent swarms
The author built a durable runtime for orchestrating agent swarms, enabling reliable coordination of multiple AI agents.
I got tired of agents that could do anything, so I built the layer that stops them
The author built Scyvera, a runtime enforcer tool that governs AI agents by enforcing declared permissions and logging decisions to address governance gaps in production systems.
Is there a good execution layer for agents, or is everyone building this themselves?
The author explores challenges in building execution layers for AI agents to handle retries, partial failures, and verification when interacting with multiple apps, and inquires about existing solutions or community practices.
We added an enforcement layer to our AI agents in production — here's what we learned about the failure modes nobody talks about
The author discusses critical failure modes encountered when deploying AI agents in production, emphasizing the prevalence of prompt injection, the necessity of real-time governance and audit trails, and the requirement for ultra-fast kill switches. Treating enforcement as infrastructure rather than an afterthought is presented as the key to maintaining control and compliance.
@_avichawla: https://x.com/_avichawla/status/2071897559287955680
The article discusses that the real challenge in AI agents is not building them but running them in production, and proposes the need for an operating system layer to manage fleets of agents, akin to how an OS manages software processes.