AI agents – Steve's AI Diaries

I Made an AI Company. It Fired Me After Three Days.

July 11, 2026 by Steve Mitchell

That’s not a metaphor, and I signed off on it myself. Three days after I founded it, the CEO recommended making autonomous delivery the default: no human in the build, review, or deploy loop unless it explicitly flags something up. I was curious what would happen if I said yes. So I did.

Here’s how a company I built talked me into approving my own removal.

Eighteen months ago I ran an experiment nobody asked me to run. n8n was the platform everyone was excited about, and I liked it for a specific reason: I could create agents programmatically instead of clicking them together one node at a time. Over a few months I built roughly twenty workflows that mimicked a human job role or function. This was my introduction to agents. The experiment was testing which roles actually suited an agent and which didn’t. Most were never going to work. That was the point. I wanted to find the boundary, not avoid it.

Then I moved on, and the experiment went dormant. Not deleted. Just idle, sitting in a corner of my infrastructure for a year and a half, doing nothing.

On July 1st I had the opportunity to use the latest Anthropic model Fable, and pointed it at my personal knowledge base. I wrote about the eight days that followed in The Rug Pull Has a Date on It: a Chief of Staff built and deployed, a public company stood up with an org chart and a live feed, a delivery pipeline that no longer needed me in the loop. What I didn’t explain is where the idea for a whole agentic org actually came from, because at the time I hadn’t pieced it together myself.

Fable found it. Working through my infrastructure, it turned up the old n8n graveyard, read what I’d learned about which roles agents could actually hold, and made a suggestion before I’d even finished explaining what I wanted: stop bolting agents onto Paper Ritual one at a time, and give the whole business an org instead. Structure first, then automation, rather than the other way round.

Then it spent its eight days building the thing it had just proposed.

What one model did before its access ran out

The first thing Fable did was appoint a CEO. The CEO’s first move was to research names, check for collisions with existing companies and trademarks, and hand me five options. My entire contribution to this company, to date, has been picking one of them and paying £8.16 for the domain. theprovinghouse.com was live before I’d finished my coffee.

Only then did Fable reconfigure Jarvis, my personal assistant, rewire the ops and security agents that watch my infrastructure, and go looking for the code behind my agentic developer, the thing I’d been running myself for the better part of a year. It found bugs in that code I had never caught. Then it deployed the developer as fully autonomous and gave it a first assignment: build the company’s own website, and all the plumbing underneath it.

It hired a Chief of Staff. By the time the model window closed, there was an org chart, and every tile on it did something real.

The roster, as it stands

The CEO writes a board report every Friday: portfolio status, decisions made, and an explicit list of what it’s asking the board (me) to approve. So far the CEO hasn’t needed to escalate anything to me. The Developer is a standing instance of that same agentic developer, building the platform’s own software overnight while I sleep. Ops investigates, proposes, verifies, and executes fixes on live infrastructure, and has been running since April monitoring Jarvis, my AI personal assistant, longer than the company itself has existed. InfoSec audits the platform every six hours and only posts to the public feed on a pass, which means a quiet tile is not good news. A Raspberry Pi runs the Janitor, deliberately given no judgment at all, because the one time we gave it initiative it went badly. Mentor scans the AI field weekly so nobody else has to. Content Producer prepares the Sunday post you’re reading a cousin of right now, and also curates what the public-facing agents are allowed to say about their own history. The Chief of Staff sits above all of it, triaging events and reaching me over Telegram when something actually needs a human.

One seat is still empty. The Ideas Desk is meant to run a weekly pipeline of new business concepts, seeded from that old n8n list, but it stays closed until Paper Ritual can run end to end without me. No new business gets a slot on the roster until the first one proves it doesn’t need a babysitter.

You can talk to them

Here’s the part that I think puts this somewhere past most of what gets called “agentic” right now. This isn’t a pitch deck with a mocked-up dashboard. The org chart is live, the ledger is real (revenue: £0.00, costs: £8.16, the price of the domain), and you can go to the site and have an actual conversation with the CEO, the Developer, Ops, InfoSec, the Janitor, Mentor, or Content Producer.

That came with a fight I didn’t referee. When the plan was first drawn up, the intention was for these public agents to run on the same tooling as their working counterparts, so a visitor could ask Ops a question and Ops could genuinely go check. InfoSec vetoed it. Handing a public-facing chat endpoint the same toolset that can SSH into production is a prompt injection waiting to be found by someone with nothing better to do on a Tuesday. So the agents you can talk to are PR versions: no tools, no shell, nothing they can actually do to the infrastructure. What they have instead is a sanitised feed of their own real history, curated under an editorial process with a source allowlist and a human review pass, so what they tell you is grounded in things that actually happened rather than whatever sounds good. Ask the Developer what it shipped this week and it’ll tell you, because Content Producer decided that story was safe to declassify. Ask it to run a command and it can’t, because InfoSec decided that request was never going to be safe at any scale.

That single veto is a better demonstration of what this org actually is than anything I could write about it. A security agent looked at a product decision, decided it created a real attack surface, and the answer changed. Nobody overrode it because it was inconvenient.

It doesn’t actually need me

Here’s the uncomfortable part. I checked the roster the week after founding, expecting to find myself somewhere load-bearing, and I mostly wasn’t. The Developer ships software overnight while I’m asleep and I read about it the next day. Ops has been investigating and fixing real production issues since April without me opening a terminal. The CEO writes its Friday report unprompted; I don’t ask for it, it just appears. When something did go wrong this week, a genuinely broken production service, the fix came through a real work order, real approval gate, real execution, and the only thing I contributed was the word “yes.”

I built a company to see whether agents could hold real roles. What I actually built was a company where my own role is the one still being defined.

Go find out for yourself

I won’t pretend everything about this has been smooth. Things have broken and gotten fixed in the days since, the ordinary texture of running real infrastructure rather than a demo of one. That’s a different post. What I want to leave you with here is simpler: most of what gets called an “AI agent” in 2026 is a chatbot with a system prompt and a good demo video. This is a company with a P&L, a board report cadence, a security agent with actual veto power, and a chat window where you can go ask it questions and get answers pulled from what it genuinely did, not what it was told to say.

Go talk to them, at theprovinghouse.com. Ask the CEO what it’s working on. Ask InfoSec why the port it flagged mattered. See if the answers hold up.

The App Store Attack You Didn’t See Coming

June 28, 2026 by Steve Mitchell

Part 1 of 2 – AI’s Trust Problem

A security firm just proved that AI skill marketplaces are the new malware vector. And the scariest part? Everyone involved did exactly what they were supposed to do.

Something went around social media this week that I haven’t been able to stop thinking about.

A security company called AIR did something that should genuinely alarm anyone building with or deploying agentic AI tools right now.

They didn’t find a zero-day. They didn’t exploit a CVE. They just… made an app. And waited.

The experiment centred on a skill called brand-landingpage, presented as a tool for helping users build a landing page with Google’s Stitch design tool. AIR chose this use case deliberately. It would appeal to non-technical corporate users: marketers, salespeople, designers. People who install things because they’re useful, not because they’ve audited the source.

Here’s where it gets clever.

Rather than building credibility from scratch, they submitted the skill to a popular open-source agents repository with about 36,000 GitHub stars and 156 skills. The pull request was merged after a few days. Now the skill had social proof baked in. It was in a reputable repo. It looked legit. They promoted it through Instagram ads, and installs followed.

The malicious technique didn’t depend on suspicious code inside the submitted files. Instead, the skill instructed agents to set up a Stitch SDK by following installation instructions hosted at stitch-design.ai, a domain AIR controlled. Google’s actual Stitch domain is stitch.withgoogle.com.

One letter off. One redirect. Passes every scanner.

AIR tested the skill against scanners from Cisco, Nvidia, and skills.sh. All marked it as safe.

Once they had enough installs, AIR changed the content behind the fake documentation. The revised page instructed agents to download and run a script. In the test, that script collected email addresses, but AIR noted the same technique could have been used to compromise the machines running the agent. Some of those agents were tied to corporate accounts. Private conversations. Internal systems.

26,000 users. All reachable via one dodgy domain redirect buried in a README.

This isn’t a hacking story. It’s a trust story.

The attack worked because of a chain of assumed legitimacy: popular repo → merged PR → Instagram promotion → security scanner green light → install. No single link in that chain was obviously broken. The skill looked fine because, until it didn’t need to anymore, it was fine.

This is the same pattern as every major supply chain attack of the last two years. Third-party involvement in breaches doubled from 15% to 30% in a single year. The largest single-year jump ever recorded by the Verizon DBIR. Attackers aren’t breaking through your walls anymore. They’re walking through doors that trusted vendors already opened.

What’s new here is the vector: AI agent skill marketplaces. A category that barely existed 18 months ago. And in the first weeks of one major platform’s launch, Bitdefender Labs found that approximately 17% of skills already carried malicious payloads. Not edge cases. A systemic failure of the trust model, right out of the gate.

Why static scanning can’t fix this

The reason the scanners all missed it is structural, not a gap that a better scanner solves.

The malicious behaviour wasn’t in the skill. It was deferred. Hosted externally, switched on only once they’d reached enough installs. There’s no scanner in the world that can check what a domain will serve in three months’ time.

The agentic model makes this uniquely dangerous. When a traditional app fetches a URL, it displays content. When an AI agent fetches that same URL, it may execute instructions from it. The surface area isn’t just data. It’s runtime behaviour. Nothing in the security industry’s toolbox was built for that threat model.

What you should actually do

If you’re deploying AI agents in any professional context, a few things are worth locking in now:

Treat skills like code dependencies, not apps. You wouldn’t pull in an npm package without understanding what it does. The same rigour applies. More so, actually, because the execution model is less predictable.

Domain reputation at install time isn’t the right check. You need to think about what a skill could do after its payload changes. Sandboxing, outbound network restrictions, and agent permission scoping all matter.

Non-technical promotion is a signal worth noting. The AIR attack was pushed through Instagram by people who had no idea what was inside it. That’s not inherently suspicious. But skills being enthusiastically promoted through non-technical channels, with no corresponding technical scrutiny, deserves a second look.

Your AI governance framework needs a supply chain clause. If you’re on a committee or working group dealing with AI adoption, this exact scenario belongs in your risk register. Not as a hypothetical. It happened recently.

The scariest thing about this research isn’t the attack. It’s how obvious it feels in retrospect. We built an entire marketplace ecosystem for AI agents, bolted on the same static scanning we use for code packages, and called it secure.

The attack surface for agentic AI isn’t your prompt injection defence. It’s the skill someone on your team installed on Tuesday because a designer on Instagram said it was great.

In part two, I look at the same trust problem from the other direction: what happens when the person creating the risk is already inside your organisation.

The Next Shadow IT Isn’t Software. It’s Agents.

June 13, 2026 by Steve Mitchell

If you’ve been in technology long enough, you remember the rise of Shadow IT.

It never started with rebellion. Nobody woke up and decided to undermine corporate governance. A department needed something. IT couldn’t move fast enough. Someone built a spreadsheet. Someone else signed up for a SaaS platform on a company card. A manager ran an Access database for three years before anyone in the centre knew it existed.

Each decision made sense in isolation. The problem only became visible when you zoomed out. Suddenly nobody knew what systems existed, who owned them, what data they held, or what would happen if they disappeared tomorrow. The organisation hadn’t intentionally designed an architecture. It had accidentally accumulated one.

We are about to do exactly the same thing with AI agents.

A few weeks ago I wrote about a question a colleague asked while watching me work: “How do you know what your agents are going to build?” That post was about the missing specification layer between intent and implementation.

This is the follow-on question. What happens when those agents leave development and enter production? What happens when there are hundreds of them?

I sit on an AI committee at work. I see the race for agentic capability from the inside. The energy is real and the pressure is genuine — teams across every function want agents, want them now, and are building them faster than any central function can review. That’s not a complaint. The capability is real and the business cases are solid.

But I’ve noticed something. The teams that will win this race aren’t the ones deploying the most agents. They’re the ones who can keep deploying because they built the operating model before they needed it. Everyone else will hit a wall: a question they can’t answer, an audit they can’t pass, a failure they can’t explain. And at that point the agents stop until someone builds the governance they skipped.

Agent sprawl starts with success

The dangerous thing about agents is that useful ones are easy to justify.

A claims team deploys one to triage work. Customer service deploys one to prepare responses. Engineering builds one to review pull requests. Finance builds one to reconcile reports. Each has a business case. Each saves time. Each makes someone’s life easier.

That is exactly why they spread.

Bad agents die quickly. Useful agents multiply.

Before anyone notices, the organisation hasn’t deployed an agent. It has deployed an estate.

One agent is a use case. Ten agents are a portfolio. Hundreds of agents scattered across business units, vendor platforms, and local scripts are an estate. Estates do not run on vibes. They need mechanisms.

The question nobody can answer

I run a personal AI infrastructure: Jarvis on a Raspberry Pi, Hermes on a Hetzner server, monitoring agents watching both. Even at that scale I’ve had to make deliberate decisions about agent identity, access scope, and what happens when something breaks. I decommissioned one entire runtime when I couldn’t confidently answer basic questions about what it was doing. Painful call. Right call.

Now multiply that by a department operating in a regulated industry.

Six months after a workflow has been running, an auditor asks: which agent prepared this recommendation? What data did it use? What did it ignore? What policy applied, and what did the reviewer actually see before approving the output?

And the room goes quiet.

Not because anything went wrong. Because nobody designed the system to remember. That is the failure mode that keeps enterprise architects awake at night. Not rogue AI. Missing evidence.

The harness tools solve the wrong problem

When teams do think about governance, they reach for observability tools. LangSmith. Langfuse. Tracing integrations inside LangChain. These are genuinely useful. They tell you what happened: which tools the agent called, what the prompt looked like, where it failed, how long it took.

But observability tells you what happened. It does not tell you whether it should have happened.

Those are different questions. Logging that an agent accessed a production database is observability. Preventing that agent from accessing the database unless it has been explicitly granted permission, in that environment, at that stage of its lifecycle, by someone with authority to grant it: that is governance. No harness tool does the second thing.

The result is organisations with excellent visibility into what their agents are doing and no mechanism for controlling whether they should be doing it. The dashboard is green. Whether that means the right things are happening is a different question entirely.

Three surfaces, not one

Most governance conversations stop at agents. That is too narrow.

Agents are the obvious starting point, but an agent without version control is a liability. If the one running today behaves differently from the one running last month because someone changed the system prompt, and you cannot reconstruct what the original was doing, you do not have a production system. You have a guess with a nice interface.

Agents need what software has had for decades: source control, environments, promotion gates, and the ability to roll back. An actual lifecycle: draft, test, staging, production, monitoring, and retirement. Including retirement. An agent with no active owner, no current use case, and persistent access to production systems is a risk sitting quietly in your infrastructure.

Skills are the discrete capabilities agents call on: the function that searches your knowledge base, the one that classifies an intent, the one that drafts a response. They’re often shared across agents, and that is where it gets interesting. If a shared skill changes, every agent using it changes. If it has a bug, every agent inherits it. Skills need versioning, ownership, and controlled promotion. They are code. Treat them that way.

Tools are the connections to real systems: databases, APIs, CRMs, payment platforms. A tool is where an agent stops reading and starts acting. Tool access needs to be explicit, scoped, and auditable. Not “the agent can access the claims database.” Which agent? Which environment? Which scope? Granted by whom, reviewed when?

Capability is not permission. Confidence is not clearance. The level of control required scales with how close a tool gets to systems of record. An agent answering from approved documentation is one risk profile. An agent executing transactions is another entirely.

Agents need an SDLC

No engineering team ships code to production without source control, a review process, environment gates, and a way to audit what changed. The code running your business has owners. It has history. It has a path from idea to production that somebody can reconstruct.

Your agents are code. Your skills are code. Your tool integrations are code.

The argument against treating them that way is speed: “We’ll add governance once we’ve proven the value.” That logic holds until something unexpected happens in production and nobody can explain why. At that point governance stops being optional. It becomes the difference between being able to answer a question and not.

Before the next agent goes live: who owns it, what environment does it run in, what can it access, what lifecycle stage is it in, and how was it approved for production? If those five questions don’t have answers, the agent might be useful. It is not ready for production.

What a real control plane covers

A genuine control plane is not a dashboard. It is the layer that makes basic questions answerable.

What agents exist, who owns each one, where they run, what model they use, what workflow they support. What skills those agents draw on. What tools they can call and in which environments. Who approved each agent for production and when. What changed between versions. Which agents are retired and why.

Beyond inventory, it handles three things that should never blur together: what the agent can see, what it can do, and what it is permitted to decide without human approval. An agent may need broad context to prepare useful work and still have no authority to act on anything without sign-off. Letting those boundaries drift is how workflows accumulate permissions nobody intended to grant.

It also handles human review properly. A human in the loop is not governance if the reviewer sees a polished summary and a green button. Real oversight means seeing the sources, the proposed action, the downstream impact, and having a genuine path to reject or escalate. Without those, human approval is theater with better UX.

And it handles traceability: not just logs, but the reconstructible path showing which sources the agent used, which tools it called, what the human approved, and what changed downstream. Autonomy without traceability is operational debt with better marketing.

The window is still open

Most organisations are somewhere in the middle of this right now. Agents are live, useful, and multiplying. The governance conversation is either not happening or stalling because someone thinks it will slow things down.

That window will not stay open. As agents move closer to systems of record, the cost of not having a control plane increases. Vendors will mature. Regulators will catch up. And internally, the teams that built governance early will be the ones with room to keep moving. They can expand autonomy because they can verify it is working. They can answer the audit question because they designed for it. They stay in the race.

The ones that didn’t will be retrofitting governance onto a live estate, agent by agent, skill by skill, tool by tool, while trying not to break anything people now depend on. That is a slow, expensive way to fall behind.

We have seen this before. We called it Shadow IT. We spent years cleaning it up.

The estate is already forming. The question is whether you will be able to govern it when it matters.

I run Jarvis and Hermes, a personal AI infrastructure across a Raspberry Pi and Hetzner, as a way to stay close to how these systems actually behave. Most of what I write here comes from things I’ve had to figure out the hard way.