AlphagenticHuman-directed AI practice

01AI agent training studio / prototype

Train the behavior.Keep release human.

Explore how examples, labels, constraints, and evaluations could shape an AI tool before anything real is run. Every stage stays reviewable, recoverable, and directed by people.

Explore the simulatorOpen workspace
Human release gateFictional casesNo external calls

02 / THE TRAINING LOOP

A visible path from source to release.

The proposed studio makes the learning recipe inspectable: what entered, what people labeled, what changed, how the change performed, and who can approve the next step. AI systems remain tools—not people or independent decision-makers.

  1. 01BuildExamples, prompts, labels, and boundaries.
  2. 02TestEvaluation cases, failure clusters, and uncertainty.
  3. 03ReviewNamed checkpoint, budget, owner, and release decision.
studio.alphagentic.local / training / invoice
simulationno provider

03 / TRAINING SIMULATOR

Shape a fictional learning run.

Change the controls and watch readiness respond. This is a product visualization, not real training, inference, scheduling, or storage.

ACTIVE RECIPE / Finance operations

Invoice exception routing

Suggest the next review lane without approving, paying, or changing a record.

Recipev0.3
STAGE / Evaluate

Test what changed.

Compare quality, ambiguity, boundary adherence, and escalation behavior across fictional cases before considering any live training run.

Evidence expectedEvaluation suite + findings
SIMULATED EVALUATION

Readiness by recipe

83%
evaluation readinessreview threshold

Recipe controls

FUTURE CAPABILITY / DAY + NIGHT

Approved training queue preview

Paid workspaces could prepare recurring evaluation or training jobs, then hold every run for an approved schedule, budget, dataset, and release policy.

04 / FROM PROTOTYPE TO PRODUCT

Doable—when each layer earns the next.

The visual simulator can become a paid training environment, but real runs need secure datasets, metering, provider integrations, queues, evaluations, audit trails, and recovery controls before launch.

  1. NowVisual simulator

    Local fictional cases, recipe controls, evaluation views, and release-gate design.

  2. NextPaid training sandbox

    Persistent projects, approved datasets, credit budgets, real test jobs, and logs.

  3. LaterScheduled evaluation

    Day-and-night queues with human-approved scopes, budgets, checkpoints, and stops.