Last post: seven AI analyst personas were given persistent access to our Waltz EA repository and left to run on a schedule. This post covers what happens when the findings are accurate but still close to useless.
Six implementation lessons on the layer between a capable model and the human expected to act on its output:
- Detection vs. memory — surfacing something once is not the same as tracking it over time
- Calibrating severity by contrast — a finding only has weight when compared against a baseline
- Grounding navigation in structured data — outputs that cannot be acted on directly are friction, not insight
- Correction loops — agents need a mechanism to learn from human disagreement
- Consistency testing — the same input should not produce meaningfully different outputs across runs
- Demoting AI-about-AI noise — findings about the AI system itself are rarely useful to the humans in the loop
None of them needed a better model.
Read the full article on cloudhpc.news
#EnterpriseArchitecture #AgenticAI #AIEngineering #Waltz