Expertise
Multi-Agent Review
Independent agents that check each other before a person looks
Overview
Multi-agent review puts a second, independent agent between the first agent's output and the human. Blind duplicate runs, tie-breakers for disagreements, and reviewer agents that see only the contested cases turn raw output into a short list a person can finish.
Positioning
One agent checking its own work is not a review. Independence is what makes a second opinion worth having: a different prompt, a different context, and often a different vendor.
What this means in practice
The pattern we run most: two blind runs on the same task, compare, auto-accept agreement, send disagreements to a third run that sees both answers, and hand a reviewer agent only what is still open. Plans get the same treatment, with a second vendor reading them before the owner does.
Why this matters
On 19,548 Qur'an word matches, this shape accepted 83% automatically and left 362 items for a person. Without it, the person reviews all 19,548 or trusts an unchecked run.
What we deliver
A review panel wired into your agent workflow
Comparison and scoring scripts with their tests
A reviewer queue that shows only unsettled items
Knowledge paths
Related insights
Insight / Multi-Agent Review
An overnight build of 27,713 hadith, and the rule that keeps models out of the rulings
How cheaper agents built Musnad Ahmad unattended in one night, how three independent checkers and a second-vendor plan review caught what they got wrong, and why a scholar's ruling is never filled in by a model.
Insight / Multi-Agent Review
Two blind agents and a tie-breaker: matching 4,887 Qur'an words to four translations
A multi-pass agent pipeline made 19,548 word matches, accepted 83% automatically, and left 362 for a reviewer agent and the owner's final read. What the review caught, and what changes next.
Insight / Multi-Agent Review
Verifying 6,236 AI-drafted verses with two vendors and a 120-verse sample
How a complete Roman Urdu Qur'an went from an agent's draft to a shipped edition: lint, two independent model verdicts pinned to the text, an owner's random sample that found two wrong words, and the sweeps that found nineteen more.