Story · Semantic Scholar
Towards autonomous urban drainage modelling: evaluating AI agent architectures for automated SWMM calibration (Semantic Scholar)
paper · Story page
One calibration workflow organised three ways: instructions consulted on demand, tool interfaces supplied in advance, one specialised agent per stage. All three produced models of equivalent accuracy, so the choice is cost and endurance, and the cheapest option got unreliable once the task ran long.
In plain words
- Researchers compared ways for artificial intelligence (AI) to adjust computer simulations of how water drains through cities.
- The AI followed instructions as needed, used prepared tools, or split the work among systems handling different stages.
- All approaches produced equally accurate simulations, but the cheapest became unreliable when tasks grew longer.
- For people planning city drainage, choosing an approach changes the cost and reliability of the work.
Appeared in
- Giving an agent the decoder's confidence never beat a plain deterministic gate
Sep 21, 2026 · in the sections
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