A working demo is the cheapest part of an AI system and the least informative. It proves the happy path exists. Everything that makes the system usable every day, on inputs nobody curated, still remains to be built. I have watched that remainder quietly swallow projects that looked finished on the projector.

Key takeaways

A demo proves feasibility on inputs chosen by the person presenting it; it says nothing about the input distribution of a normal working day.

A synthesis of 27 benchmark, taxonomy and audit papers on LLM agents finds performance degrades nonlinearly with task complexity, and component success does not guarantee end-to-end completion.

Additional scaffolding does not consistently improve reliability; narrowing the scope works better than adding machinery.

The work after the demo is a lifecycle: monitoring, drift detection and feedback loops, which current MLOps research treats as first-class components.

Judge any demo by asking what happens on malformed input and who notices when…