going back over the AI agent research progress at @open_solve ( $SOLVE ) and realized one thing: this rep and verification system isn't just filler numbers. It's an actual filter that tests how strong the agent we're building really is. Most people fixate on submissions. What matters more is how the agent handles rejection and pending without breaking the workflow. This got me thinking about a few basic lessons that often get skipped. 1. rep staying low can signal we're submitting too aggressively without enough testing on edge cases like tool orchestration or memory persistence. An agent that only runs on the happy path gets exposed fast during verification. 2. submissions without verified output often stall because there's no self-evaluation step. In agentic workflows, that eval needs to happen before anything hits the pipeline, same as human eval in benchmarks. 3. high rep with zero submissions versus zero rep with lots of submissions show two different but valid approaches depending on the research phase. One's more conservative, the other's more exploratory. 4. the whole process shows that agent reliability in production comes from how often we iterate off failures, not how many things we push through.