Repeated prompts overfitted the agent, hiding regressions. Evals score for equivalence — the same app can be built in multiple valid ways.
A no-code platform that now lets users build full-stack web and mobile apps through AI prompts, with the most established builders running apps that transact millions of dollars.
The engineering team tested their app-building Agent manually against a finite set of scenarios, which caused overfitting to go-to test prompts and...
Visual programming platform for building web and mobile applications without code.
AI observability and evaluation platform that helps developers build, test, and monitor LLM-powered applications.
Bubble's Agent evaluation is part of this use case:
Related implementations across industries and use cases
Prompt iterations rubber-banded: each engineer's fix overcorrected the last. Evals are now a merge requirement—no eval, no commit.
Steep learning curves caused high user churn. Now, a native AI agent lets builders fluidly mix text prompts and point-and-click tools.
Debugging scattered across individual notebooks meant week-long fixes. One shared platform now catches errors before customers do.
Prompt iterations rubber-banded: each engineer's fix overcorrected the last. Evals are now a merge requirement—no eval, no commit.
Teams couldn't manually review hundreds of daily AI hotel calls. Audio models now evaluate raw recordings, routing exceptions to humans.
Scattered data and basic coding tools bottlenecked engineers. A 9-agent AI workflow shifts them from writing code to directing AI teams.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.
Repeated prompts overfitted the agent, hiding regressions. Evals score for equivalence — the same app can be built in multiple valid ways.
A no-code platform that now lets users build full-stack web and mobile apps through AI prompts, with the most established builders running apps that transact millions of dollars.
The engineering team tested their app-building Agent manually against a finite set of scenarios, which caused overfitting to go-to test prompts and...
Visual programming platform for building web and mobile applications without code.
AI observability and evaluation platform that helps developers build, test, and monitor LLM-powered applications.
Bubble's Agent evaluation is part of this use case:
Related implementations across industries and use cases
Prompt iterations rubber-banded: each engineer's fix overcorrected the last. Evals are now a merge requirement—no eval, no commit.
Steep learning curves caused high user churn. Now, a native AI agent lets builders fluidly mix text prompts and point-and-click tools.
Debugging scattered across individual notebooks meant week-long fixes. One shared platform now catches errors before customers do.
Prompt iterations rubber-banded: each engineer's fix overcorrected the last. Evals are now a merge requirement—no eval, no commit.
Teams couldn't manually review hundreds of daily AI hotel calls. Audio models now evaluate raw recordings, routing exceptions to humans.
Scattered data and basic coding tools bottlenecked engineers. A 9-agent AI workflow shifts them from writing code to directing AI teams.
Sequential AI testing bottlenecked development. Engineers built a concurrent, code-first pipeline to evaluate agent responses in seconds.
Tournaments running simultaneously meant an hour of manual checks each. AI agents now run them in minutes, freeing the team to be proactive.
Large AI training jobs meant fighting for preemptible slots or leaving campus. Marlowe gave any lab guaranteed multi-node access on demand.