AI agent builds, measured against what they replace
Builds measured against the path they replace, including where they lose.
Every case study here reports the same things: what was built, what it measured, and the conditions under which the approach is the wrong choice. The break-even is stated in the open rather than buried, because an engagement that starts with an honest floor is the only kind worth scoping.
The measurement method is specified in the AEQ specification, and the full accounting behind these numbers is in the research papers.
- Case StudyThree Architectures, One Model, One Question SetA pre-registered run of the AEQ method: same model and data across three agent architectures, measured with reasoning on and off, including the arm where the layers disagreed.Read the case study
- Case StudyA Platform's Worth of EAM Workflows in Seven ToolsWhat a commercial EAM module list actually costs to rebuild as AI agents, and where the break-even against per-seat licensing sits.Read the case study