Case study
Agentic Harness turns AI work into a reviewable workflow.
Agentic Harness is a small public Python project that packages long-running AI goals as project-local state, pluggable workers, recorded artifacts, loop guards, and deterministic review gates.
What It Does
The harness gives an automation project a predictable lifecycle: initialize a goal, run a worker, capture artifacts, continue when useful, and review the result against typed criteria. It is designed for teams that want AI-assisted execution without losing the ability to inspect what happened.
- Project-local state keeps runs understandable and portable.
- Adapters let different worker backends plug into the same goal lifecycle.
- Review criteria make completion explicit instead of relying on vague status text.
- Loop guards reduce runaway continuation cycles.
Why It Matters For Clients
The same pattern applies to business automation work: define the goal, keep evidence, review outcomes, and only expand scope when the result is inspectable. That is useful for document generation, workflow triage, website maintenance, data cleanup, and internal assistant tasks.
How Moortekweb Uses The Pattern
Moortekweb starts small: one bounded workflow, one output contract, one review path. For a client project, that can become a fixed-scope automation sprint with clear checkpoints before connecting more systems or handling sensitive data.
- Start with a low-risk workflow and sample data.
- Define the artifact that proves the work happened.
- Add review checks before moving toward scheduled or unattended operation.