Evidence-based discovery replaces assumption-driven scoping by testing the actual system before writing recommendations: every editorial form exercised, the support history read, the codebase audited — so the proposal cites observed evidence instead of pattern-matching from past projects.
On a recent research-institution engagement, that meant browser-testing all 17 editorial forms on the live site, reading six years of support history, and auditing the existing Drupal codebase before a single recommendation was written. The resulting proposal ran 12,000 words, and every recommendation traced back to something observed: a form that broke, a ticket pattern, a dependency blocking the upgrade path.
AI agents make this economically possible — they do the comprehensive, tedious part while senior people spend their hours on judgment. Joyus Recon packages the same approach: architecture discovery from a single public URL up through full codebase access.