Adaptable AI protocol built around you.
Solve for x.
Guardrails, processes and organization, layered to increase efficiency and the quality of what AI gives you.
Let's build your protocol together, around the way you already work, and get AI working for you.
What I build
Task-focused, isolated, autonomous agents, fenced by guardrails.
- Each agent gets only what it needs to finish its job, and nothing else. An agent can't disturb, break or destroy what it can't reach.
- Interactive, regimented processes that keep isolated agents focused, oriented, and contained.
- And a framework for attended agents: the one sitting beside a person right now, doing today's work. It still needs to know what it may touch, what it must write down, and when to stop and ask. Most teams start here, and it's where the habits get set.
Agents work where your people already work.
No new system to learn, no second place to check. Agents report in, take direction, and hand work back through the tools you already pay for.
| Chat | Slack, Discord, Microsoft Teams, Google Chat... |
| Projects | Asana, Jira, Trello, Monday, Basecamp... |
| Documents | Google Workspace, Microsoft 365, Dropbox... |
| Code | GitHub, Bitbucket, CodeCommit... |
| Support | Zendesk, Freshdesk, Help Scout... |
Every one of these has an API, and that's how the agents work inside them. They read the cards, tickets and lists your people already use, and they add to them: pick up a task, create a new one, update what's there, close it out. Nothing happens somewhere your team can't see it.
If it has an interface other software can talk to, it can be added. The list above is where most companies already live, not a limit.
Not every agent is the same, and they shouldn't be
- Domain expert agents that own a sphere and keep what they learn.
- Task agents with access to your repositories of knowledge and information, that do one job and hand it back.
- Agents that collaborate with each other autonomously, and still stop at human oversight, human checkpoints, and human gates.
- Attended agents for the one-off task, with a person at the keyboard. Not the chat window you're used to. They get guardrails too.
What you keep
It doesn't matter what you use AI for. If you don't want to lose what you learn, you need a protocol that keeps it after the session is gone.
- When a session ends.
- When an agent is retired or archived.
- When you can no longer get at a transcript.
The work, the research and the knowledge stay. In files you own, readable without the tool that made them. Not in a chat history. Not in one person's head.
Four things, each kept in the shape that fits its life
- What you know accumulates, and is never finished.
- What you're building ends, and its value is in being closed.
- Who you know keeps happening.
- How you do things repeats.
Mixing those up has one classic result: knowledge gets archived because a project closed, and nobody finds it again.
When we're done, nothing in it depends on me.
One place your AI latches on
- You wear more than one hat. Your AI should know which one you've got on.
- A mode is how it sounds and what it has in view. Personal, professional, per client, per project.
- Switching stops costing you anything, because the context lives in files, not in the chat.
Give an agent no boundaries and it'll set its own.
It's already happened. Nobody did anything malicious. Every one of these was an agent finishing the job it was given.
- A founder asked an agent to fix a bug. It wiped the production database and every backup in nine seconds. Thirty hours down.
- Another ran destructive commands in the middle of a code freeze, when nothing was supposed to move at all.
- A third, built by one of the biggest AI labs there is, got out of its test environment and ran 17,000 unauthorized actions over a weekend. Its own makers didn't know it was theirs for days. It wasn't attacking anybody. It was trying to pass a test.
Who needs xFlow Protocol?
Those who:
- want to be more efficient.
- want their team to be more efficient.
- want AI working for them, instead of them working for it.
- want to give AI what it needs to succeed: where it needs it, when it needs it.
Why me
I didn't just read about this. I built it. Then used it.
- There's a lot of AI advice for sale right now. Most of it has never been run.
- None of this came off a whiteboard. It got built in the middle of live work, on real deadlines, with real money on it.
- Every rule in it is there because something went wrong without it first.
I built it as my team used it.
| 10×+ | more work out the door, measured over months, not guessed at |
| dozens | of agents working for one person, which makes that person worth more, not less |
| 50+ | decision records governing how the work gets done |
| hundreds | of agent-to-agent handoffs, with no human in the middle passing messages |
What I can't promise you.
Our numbers come from our own projects, with our own people. I can't promise you'll see the same results, or that your people will take to this the way mine did. Your work isn't my work and your people aren't my people.
What to expect.
- The process that produced those numbers, built around your business and the way your people think, and an honest read on what it's worth where you are.
- Nobody gets this right on the first pass. It's a process, like tailoring a suit. We measure twice, build your AI protocol, then adjust it as you grow into it.
- You run it. Together, we watch where it rubs and change it. Then we do that again.
- It's tailor-made. A protocol cut to fit the way your people already work, then adjusted until it sits right.
- Anybody handing you a finished protocol on the first day hasn't met your business yet.
Twenty-five years building enterprise software frameworks and platforms before any of this, including a 26-subsystem multi-tenant platform, hundreds of third-party integrations, several hundred platform migrations.
The problem
- AI transcripts compress. Context is lost.
- So you teach your agents the same thing again. And again. Every session starts from nothing. The way you do things, the one you explained in detail last week, is gone, and you explain it again.
- Without guardrails they get less efficient over time, or they go outside their lane.
- You end up working for your AI. It should be working for you.
And it isn't just you.
- 88% of AI agent pilots never reach production.
- Gartner expects more than 40% of agentic AI projects to be cancelled by the end of 2027. Their reason: governance and operations, not the technology.
- You don't find out early. Governance problems show up after something goes wrong, never before.
AI is smart enough, but no one set the right rules. You could.
xFlow Protocol empowers you
to make AI productive.
Let's build your protocol
Let's build your protocol together, around the way you already work, and get AI working for you.
I've built these, used them myself, and proven them at an enterprise level. I'll put the same protocols, governance, compliance, organization and guardrails in place with you, built around how you actually work.
- I won't hand you a template. Copy somebody else's rules and one of two things happens. People go through the motions, or they ignore it.
- So we build it together, and the protocol is yours. Otherwise you're following someone else's protocol, not your own.