Getting started
Workflowware is usable even if you know nothing about AI agents. You do not need MAAT Runtime, agent frameworks, model providers, prompts, or code to test a package.
Also available as markdown: customer-getting-started.md
What am I buying or downloading?
You are getting a workflow package: a folder of instructions, examples, rules, and test cases that explain how a specific job should be performed by humans and AI agents together.
missed-lead-recovery-workflowware/ README.md workflow-spec.md agent-spec.md approval-rules.md eval-template.csv agent-handoff.md artifact-manifest.json
A package is not an app-store app. It is closer to a complete operating manual plus test kit for one job.
Where do I download it?
Early public downloads may come from the Workflowware website, the propershare/workflowware GitHub repository, a GitHub Release ZIP, or a direct link during a pilot.
If you are not technical, choose the guided pilot instead of installing files yourself.
Can I use this on a phone?
Yes, for review and guided testing. On a phone you can usually read the package page, fill intake, submit sample data, review drafts, approve or reject a message, and give feedback.
A phone is usually not the best place to unpack ZIPs, edit package files, run scripts, or connect business tools.
| Task | Phone | Computer | Guided pilot |
|---|---|---|---|
| Read the website | Yes | Yes | Yes |
| Fill out intake | Yes | Yes | Yes |
| Submit 3–5 sample leads | Yes | Yes | Yes |
| Review outputs | Yes | Yes | Yes |
| Download ZIP package | Maybe | Yes | Not required |
| Edit package files | No | Yes | Not required |
| Connect business systems | Not recommended | Maybe | Best |
What if I know zero about agents?
Use the guided pilot path. Answer plain business questions:
- What job do you want help with?
- What does a good result look like?
- What should require human approval?
- What should never happen automatically?
- Can you provide fake or sample examples?
BlackLabRats/Hermes can operate the package for you and return the results.
What if my AI agent is doing the work?
That is expected. Tell the agent:
Open the package README first. Do not request secrets or passwords. Read approval-rules.md before taking action. Use sample data first. Do not connect live business systems unless I explicitly approve it. Return a summary of what the package does, what files it includes, and what action needs my decision.
What should I never submit?
- Passwords, API keys, browser cookies
- Payment card data
- Private customer lists
- Live inbox credentials
- Anything you are not allowed to share
Use fake or anonymized examples first.
First test recommendation
Start with sample data:
Business type: HVAC Lead message: Need AC repair asap. Are you available today? Received: yesterday afternoon Problem: nobody replied Goal: draft a safe follow-up message for human approval
The package should return triage notes, a draft follow-up, approval questions, a next-action recommendation, and notes on what it could not know.
What happens after I test?
You may receive package output, a summary, a feedback form, an option for a guided install, or an option for a managed/pro system. Professional installs may use a private governed runtime behind the scenes — you do not need to install that runtime unless the project scope requires it.