
Open Source
Agentic Engineering Harness as an Open-Source Project
Initiator and maintainer
Relevance
Why this case matters
The case shows how problem understanding, responsibility, decisions, and implementation came together in this context.
Impact
Built a publicly installable Skills and harness system for evidence-backed autonomy across product clarification, development, delivery, operations, and learning.
What carries forward today
The repository is the public, installable part of my Agentic Operating Model: agents work across the product and delivery flow with versioned rules, Skills, checks, review loops, and explicit responsibility boundaries.
Defensible proof
MIT
Publicly reusable
Source, methods, and Skills are inspectable and installable on GitHub.
27 Skills
Launch catalog
Standalone product, engineering, harness, and craft Skills at public launch.
4 clients
Installation smoke test
Codex, Cursor, Claude Code, and Gemini CLI are verified in the repository.
Where this experience creates value
- For developers and teams that want to trust agents with more than isolated coding prompts.
- For product organizations that connect high autonomy with DDD, TDD, agile testing, DevSecOps, operations, and inspectable learning.
Case context
Overview
The Agentic Engineering Harness is not a prompt pack. It is my public working
model for using agents reliably across the software product flow: from signal
and triage through domain understanding, prototypes, implementation, and review
to release, operations, outcome review, and the next learning step.
The central goal is maximum evidence-backed autonomy. For understood work,
agents carry the loop through tests, quality boundaries, independent review,
commit, and authorized push. People remain responsible for strategy, investment
boundaries, new domain semantics, high risks, and genuine authority boundaries.
Responsibility
Activities
- Standalone Skills for product craft, frontend craft, backend craft, DDD, TDD, testing, compliance, harness design, and agent work
- Harness templates for single repositories, multi-repository systems, and multiple teams
- Versioned infrastructure desired state and executable compliance rules: IaC plan/apply, GitOps-near reconciliation, drift signals, and policy as code with clear human accountability
- A product and delivery model built around value streams, small vertical slices, disposable prototypes, and production learning
- Rules for Git-owned evidence, current toolchains with cooldown, review loops, rollback, observability, and agent stewardship
- Thin adapters for different agent hosts without hiding their differences behind false uniformity
Operating mode
Methodology
Many agent setups transfer old planning and approval logic onto faster tools:
first a complete specification, then implementation, then tests, then handover.
The harness follows how product and engineering work actually evolve: shared
understanding, the cheapest decisive experiment, vertical TDD slices, real
feedback, and versioned learning.
The repository deliberately remains an evolving, inspectable open-source
project. It is not a finished promise of an autonomous software factory and not
a retrospective AI label for older work.
Technical context
Technology stack
The tools are not the point by themselves. What matters is which system layers had to work together.
Practices
7Data & AI
1Tools
5CI/CD & Delivery Pipelines
3DevOps
2Next step
If you need similar responsibility, we can identify the next useful leverage point directly.
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