Platform · Methodology

Human in the loop, over the core methodology.

A machine loop runs in the centre and a human layer sits above it, reaching down to control three of the stages. The model proposes and the model judges, but the human sets the goal, owns the standard, and authorises every change.

HUMAN IN THE LOOP · sets the goal · owns the standard · authorises every change A Define OVER USE CASE B Refine OVER RUBRIC C Authorise OVER FEEDBACK LOOP 01 HUMAN Use Case The workflow to make agentic, framed with full context. 02 MACHINE Agent Setup Configure the agents, their roles and behaviour. 03 HUMAN Rubric The criteria output is judged against. Model drafts, human refines. 04 MACHINE Eval Score behaviour against the rubric, with an LLM as judge. 05 HUMAN Feedback Loop Apply approved improvements (upliftment), then loop back. Predefined Curated benchmark Live Data Catches drift UPLIFTMENT LOOP · re-enters Agent Setup
The Incitrix methodology as a closed loop: five stages, three human control points, and the upliftment loop that lets an agent improve over its lifecycle rather than degrade silently. Build · Orchestrate · Assure

The Incitrix methodology is a closed loop of five stages. Humans own three of them: the use case, the rubric, and the feedback loop. The detail is laid out in the sections below.

The human layer

Three control points, and only three.

The loop runs on its own most of the time. But at three points a human is in command. These are the moments where intent is set, the standard is owned, and change is authorised.

A
Define
Over Use Case

Owns the use case and the context that frames it. The loop starts from human intent, never from the machine alone.

B
Refine
Over Rubric

Refines and signs off the model-proposed rubric. The standard of judgement stays human-owned, not delegated to the model.

C
Authorise
Over Feedback Loop

Decides which Eval recommendations give effect to the upliftment. No change reaches the agent without sign-off.

The core methodology

A closed loop, not a pipeline.

Five stages run in a cycle. The machine sets up agents and evaluates them; the human frames the work, owns the rubric, and authorises what changes. Then it loops, so the agent is uplifted over its lifecycle rather than left to drift.

01HUMAN
Use Case

The workflow to make agentic, framed with full context.

02MACHINE
Agent Setup

Configure the agents, their roles and behaviour.

03HUMAN
Rubric

The criteria output is judged against. Model drafts, human refines.

04MACHINE
Eval

Score behaviour against the rubric, with an LLM as judge.

05HUMAN
Feedback Loop

Apply approved improvements (upliftment), then loop back.

Eval draws on two sources Predefined Data: a curated, known-case benchmark. Live Data: in-production signals that catch drift.
Governing principle
A model drafts and a model judges, but a human sets the goal, owns the standard, and authorises every change.

This is what makes the loop trustworthy rather than a closed, self-reinforcing automation.

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