LLM-as-judge: automated quality scoring for agent outputs
Instead of relying on yourself as the sole judge of every
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How to use feedback to immediately improve AI content output
When you get feedback on an AI-generated output — say a tweet that sounds too formal — you have several options right away. You can update the underlying skill based on that feedback, have the agent immediately regenerate a new draft that s
Agent skills should be evergreen — continuously improved, not one-and-done
The first version of a skill is never the final version. A dry run might produce an okay first effort — maybe 50% of the way there — but the whole point is that skills should be evergreen. Every time a skill is used, it can improve: either
Why agent observability becomes critical when running at scale
Once you start running agents at scale, secondary capabilities like observability become really critical. It's not just about whether you can get one agent to do one thing — it's about how you oversee and run an entire business with many di