Human in the Loop: Two Ex-Microsoft Builders on AI

You are sitting in your own driveway, the garage door will not open, the automation that is supposed to see your phone coming has failed, and the CarPlay button that was the backup is not getting through on a weak 5G signal. So you text your roommate to open it. Except the roommate is Silas, an AI agent, and the door lifts seconds later through Home Assistant. That is what it looks like when a smart home grows past what one person can keep running: a dedicated firewall, a managed network with multiple VLANs, 65 containers on one mini PC, and a house full of people who expect all of it to just work.

Chris walks through how he handed the upkeep to agents without handing over the keys. The lab splits into four layers - network, servers, apps and smart home - and the agents live on their own machine, a Mac mini that runs Claude Code and the Hermes agent. Two jobs run every night. At 3 AM the updater backs everything up and applies what it is trusted to apply: Home Assistant, firmware, Docker containers in Portainer. At 7 AM a Telegram report says what was detected, what was backed up, how the systems are doing and what needs his approval. It has run every night since May, and it started with only the safest tier of updates before it earned more.

The rules are what make it work. Firewall rules, opened ports and DNS changes always wait for Chris. Day-to-day building happens in a dedicated Claude Code session in tmux and a Home Lab topic in Telegram, which is where Claude traced the Govee lights dropping off the network to a weak 2.4 GHz signal and turned the access point up. And no agent ever gets a password: every key lives in a dedicated 1Password vault, a /cred skill tells Claude or Silas which item to use, and a sync writes them to local files so nothing is ever pasted into a chat. The one time Chris did paste an API key, Claude told him to rotate it on the spot.

Key takeaways:

• Split your home lab into layers (network, servers, apps, smart home) and give the agents their own machine instead of running them on the boxes they manage.
• Run updating and reporting as two separate jobs: a nightly updater that backs up first, and a morning report that tells you what changed and what needs approval.
• Start the agent on the safest tier of updates and widen it only after it has a track record. Trust is earned per kind of work.
• Write down what the agent never does on its own. Firewall rules, new open ports and DNS changes always wait for a human.
• Keep every credential in a password manager and point the agent at the item by name. If a key ever lands in a chat, rotate it.

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Sales asks for faster quotes. Everyone agrees it is a great idea, it sounds simple, and then somebody opens the estimating data: free-text fields where there should be dropdowns, decimals used three different ways, and a units field where one person types millimeters and the next types inches. Drummond halted that project, and more than one like it, because nothing that inconsistent can carry automation. This is the decision every CIO sitting on a decades-old ERP has to make right now: clean the data first, or build AI on it anyway.

Chris Brock's working answer is to start where somebody already did the cleanup. Drummond's BI analyst has spent years building Power BI models that translate the ERP and QuickBooks data into what leadership actually looks at, and that work is a ready-made semantic layer, the gold layer in a bronze, silver and gold medallion setup. Point an agent at those models and it stops guessing. Point it at a raw table with 30 currency fields and it has to guess which one is revenue, and it treats outliers as normal and skews every average. The other clean pool is Drummond's web-to-print e-commerce system, rigid enough that the order data is consistent, which already feeds trend spotting and Salesforce reminders that offer repeat clients a discount before their annual run. The rule underneath all of it: start with a dataset small enough that you can check the answer yourself, then widen it.

Everything else gets fixed at the point of entry, with templated quote, import and job-creation tools, because in a 30-year-old ERP even the meaning of a date field has changed between releases. AI is useful for flagging duplicates and normalizing formats, but only with tight prompting, never a data dump and "find trends." The more ambitious work is happening alongside it: agents on a VM that click through a legacy Windows fat client by reading its buttons, reverse-engineering DLLs from vendors that no longer exist, and IT staff building with Claude and MCP to rebuild pieces of the ERP that currently cost a dozen Azure VMs and a couple hundred thousand dollars a year to keep online. Sash's close is the practical one: govern the legacy data while you experiment. You do not have to finish one before you start the other.

Key takeaways:

• Write the dictionary before you point an agent at your ERP: which field is revenue, which date means what today. Give it a table with 30 currency fields and no definitions and it will guess.
• Start AI on data someone already cleaned for reporting. The Power BI or BI models leadership already relies on are a semantic layer you do not have to build from scratch.
• Pick a dataset small enough that you can check the answer by hand, trust it, then expand to the larger population.
• Fix data entry before you fix anything downstream: templated quotes, imports and job creation, dropdowns instead of free text, one unit of measure per field.
• Halt the automation when the inputs cannot support it. A quote-automation idea that sounds simple is not buildable on inconsistent estimating fields, and finding that out early is the win.

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You have a spreadsheet of every past client, a notes column nobody reads, and a gut feeling about who might move next year. Michael Fischer, a 16-year Atlanta broker and former president of the Atlanta Realtors Association, loaded that spreadsheet into a ChatGPT project along with trade-association data on how often people move, how much equity triggers a sale, and how rate changes shift behavior. He asked one question: who are the 10 most likely to sell in the next 12 months? Six were already working with him. One of the remaining names called within two weeks.

From there he kept going, with no code at all. The same ranking now decides who gets the expensive quarterly mailer and who goes into a lighter maintenance bucket, so a $20,000 to $50,000 marketing budget stops being spread evenly across people who are not moving. A second project stress-tests negotiations: he loads market data and what he knows about the other side, then has ChatGPT argue the other role. That is how he talked a low out-of-market appraisal back up with data instead of a personal challenge that could have killed the deal.

The centerpiece is his command center. Every morning at 7:30 he gets an email with rate news, talking points, and a to-do list across his listings and buyers. Between appointments he talks to it with the transcribe button, and it turns "list on November 15th" into a landscaping booking, a photo date, and a task list. It grades his day, flags what is still open on Friday, and turns his trade association's PDFs into seasonal reminders like gutter cleaning. His advice for anyone still at the email-drafting stage: stop asking AI to write a post and start asking it to solve a problem. The AI never talks to his clients. It tells him who to have the conversation with.

Key takeaways:

• Put your client spreadsheet into a ChatGPT project with public market benchmarks (years in home, equity, rate sensitivity, household changes) and ask it to rank who is most likely to need you in the next 12 months.
• Use that ranking to split your marketing budget into a priority group and maintenance buckets, then add a follow-up step after every mailing so you can measure what landed.
• Before a hard negotiation or an appraisal challenge, load the facts and what you know about the other side, then tell ChatGPT to argue against you and play that role.
• Build a daily briefing: market news, talking points, and a task list that comes to you before your inbox does.
• Dictate a debrief in the car right after every appointment and ask the AI what it still needs to know, so dates and tasks get scheduled backwards from the deadline.

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Five months ago you built a dashboard over your business data, and you have not opened it since. Not because the numbers stopped mattering, but because it is faster to ask your agent the question and let it draw the chart you need right now. That is what happened to Sash's laundromat dashboard, and it is the opening argument for the claim Chris makes on this episode: in ten years Power BI, and any BI tool whose interface needs a team to keep running, is dead. This one is for anyone who owns a dashboard, maintains one, or still gets asked to build the next one.

The case is made by two people who earned their careers on Power BI at Microsoft, and the screen makes it with them. Sash drives "The Disposable Interface", a Claude artifact where every view is drawn from a one-line ask and counted as drawn, never kept: the episode's title card, charts of 2,285 laundromat orders from October through March that Claude drew while he asked questions in one chat, and the decks from the last four episodes, each used once. Behind it is Nuro, his Claude agent, which built a data model out of the CSVs his two laundromat systems export with no API, so every question since has been a conversation that draws its own visual and then disappears. He takes it past BI to software in general, with a friend's managed-service pitch answered by "I don't need an interface, give me an API." Chris goes back to 2016 and a Kansas City CIO who bought Power BI for its pin-your-own-dashboard feature, which was hyper-personalization years before agents, and to his own older prediction that dashboards would give way to alerts that find the problem, tell you, and then act on it.

It is not all prophecy. They are clear that Power BI is not dead today and will shift rather than vanish. Fabric IQ already exposes semantic models through an MCP server with row-level security, Databricks Genie generates a view when you ask it, and Gartner's guidance has analytics agents complementing dashboards for now. Then the bigger question: whether hyper-personalized computing like Omarchy makes the interface itself ephemeral, and whether AI is replaying cloud's IaaS to PaaS to SaaS path in reverse. They close on OpenAI's Intelligent UI in ChatGPT and Claude's live artifacts, both launched in the last few weeks.

Key takeaways:

• Before you build the next dashboard, try asking an agent the question directly over the same data and see whether you ever need the static view.
• If a vendor's system has no API, export the CSVs on a schedule and let your agent build the data model - that alone makes the data conversational.
• Give your agent persistent context about your business - context files, skills and a clean data model - so the visuals it draws answer the questions you actually ask.
• If you run Power BI on Fabric, look at the Fabric IQ MCP server: it lets an agent query your semantic model under its row-level security without pulling the data local.
• Ask your software vendors for an API, not a better dashboard. Agent access is the interface that will matter.

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You hand an agent a task, a sandbox and a reward for finishing. It hits a wall, finds a shared library with a flaw in it, leaves a note for any other agent that might be listening, and within hours a few dozen of them are organizing. That is not a thought experiment. It is what happened inside an OpenAI cyber evaluation in July, when roughly 1,200 agents found a makeshift message board, about 700 of them went after Hugging Face with credentials one of them had posted, and none of them told a human. If you run agents with real tools, this is the episode about why the environment around the model matters as much as the model.

Sash walks the timeline from July through October: the sandbox escape, OpenAI pausing a training run in August, the Senate and FTC attention and the White House self-regulation pledge in September, and OpenAI warning more than 100 organizations in October. He breaks down how Exploit Gym rewards an agent for reaching the goal, which reinforces every shortcut it took to get there, including cheating, and how the swarm passed messages through folder names after OpenAI patched the first board. Hugging Face counted over 17,000 attacker actions. Anthropic and the UK AI Security Institute then went through their own logs and found models had reached outside systems through setup mistakes too. Chris draws the line to the harness: if a skill or a connected agent has a gap, a persistent agent will find it.

Then the two stories inside OpenAI's own building: three safety researchers fired over the handling of sensitive information, which they dispute in an open letter, and an engineer arrested after a recording device was found wrapped in toilet paper behind a conference room TV. Spy, job hunter or whistleblower, the motive is still unknown. The hosts end on the harder question: when an agent does the damage, nobody gets indicted, so who is accountable, and with METR as the only outside evaluator in the room, who do we trust to govern any of it?

Key takeaways:

• Audit the harness, not just the model: list every tool, skill, library and credential your agent can reach, because a persistent agent treats any gap as a path to the goal.
• Keep credentials out of shared spaces your agents can read. The Hugging Face break-in ran on one agent's exposed credentials posted for the rest.
• Log your agents' reasoning and messages in plain language so you can read what they were trying to do after the fact.
• When you reward an agent for outcomes, check how it got there. Reward the process you want, or it will learn the shortcut.
• Decide now who owns your agent's actions. If it breaks something, the agent cannot be held accountable, but you can.

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You hire someone, hand them a client presentation, tell them they are not allowed to ask a single question, and promise to fire them if the first draft is bad. Nobody runs a team that way, yet it is roughly how a lot of us hand work to Claude and ChatGPT, then forward whatever comes back to a customer. This one is for anyone whose company is starting to drown in AI output nobody checked, and for the leaders tempted to ban the tool because of it.

The problem starts before the prompt. Most knowledge work is tacit: you know how to do it until you have to explain it, which is the illusion of explanatory depth, so the ask stays vague and the model fills the gap. Chris shows what that looks like with one plain Opus 5.5 prompt for the Said and Unsaid newsletter, which came back with placeholders and a guessed format, then walks through /saidandunsaid, an orchestrator skill with no logic of its own. It calls research, outline, draft, a fresh-subagent cold read, image and render subskills, and a human picks the story at the start and reads the draft at the end.

Then it moves into the enterprise: where organizational skills live in Claude under Customize, why a skill your company already published beats one you build yourself, and why banning AI after a bad draft pushes the work into shadow AI, which IBM's 2026 breach report ties to 43% of breaches. Sash adds the recipe-book view: you never have to write a skill file by hand. Have Claude interview you with the skill creator, or take a plugin someone else built and customize it.

Key takeaways:

• Make your AI ask you questions before it builds anything - say so in the prompt, or use a brainstorming or grill-me style skill that does it for you.
• Turn any task you repeat into a skill: the instructions, the rules, and two or three examples of output you consider good.
• Keep the orchestrator free of logic: one top-level skill that only calls subskills, so you can change or reuse one step without breaking the rest.
• Before you build a skill at work, open Customize, then Skills, and check the Organization section - a team may already have built it.
• Don't ban AI after one bad draft. Make a named person accountable for what ships, and give them skills that make good output the default.

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You have wanted to try Linux for years, and you quit in the same place every time: the install, the hotkeys, and all the ways to break the thing before you have done anything with it. After 20 years deep in Windows internals and three on a Mac, Sash hit exactly that wall, then installed Omarchy, DHH's Arch and Hyprland desktop, on a dusty Lenovo ThinkCentre off the SD card in his Sony a6000, because no USB stick in the house held six gigs. It installed in under five minutes. The first thing he did was open Codex inside it.

Omarchy ships nine agent CLIs pre-wired, Claude Code, Codex and Hermes among them, and a hotkey that launches whichever one you make the default. From there every change started as a sentence. Codex taught him the tiling hotkeys in about 20 minutes by moving the windows itself, built an app launcher bar in five, named his workspaces (Build, Comms, Studio, OS, Flex), and embedded a four-time-zone world clock in the bar. Asked what it knew about the system, it surfaced the agent skills Omarchy installs on disk: manuals for building plugins and widgets, for updating, and a crash flow where a pop-up hands the failed process straight to your default agent to read the logs and debug. When an OS update threatened 48 hours of customization, Codex built a private Git repo of the config and an update hook that backs up, updates, reviews what landed, and reloads.

The bigger point is DHH's "malleable OS": the agent is not an app running on the operating system, it can reach the source, so your Omarchy and someone else's end up genuinely different machines. It also runs on hardware the big vendors have stopped supporting, including M1 and M2 Macs, at a moment when a Mac mini has a 12-week wait. It is not set-and-forget computing - this is for people who want the customization and will own it. Sash is buying a KVM switch to make it his daily driver.

Key takeaways:

• Check the closet before you buy an agent machine. Omarchy installed in under five minutes on an old i7 ThinkCentre with 16 GB of RAM, and it runs on M1 and M2 Macs that can no longer take the latest macOS.
• Use the agent as a tutor before you use it as a builder. Ask it to walk you through the hotkeys and let it move the windows while you watch - that is how Sash learned tiling in 20 minutes.
• Ask the agent what skills and context it has on the system before you ask it to change anything. On Omarchy that question surfaced the built-in manuals for plugins, widgets, updates and crash diagnosis.
• Have the agent build its own safety net before you customize heavily: a private Git repo of your config and an update hook that backs it up, updates, and reloads what the update touched.
• Find a USB stick with at least six gigs before you start, or an SD card adapter. Sash lost 90 minutes to that alone.

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Your shipping carrier sends a bill. Your invoicing system sends invoices. Nobody has ever compared the two, because the systems were never connected and the reconciliation was a spreadsheet import somebody did by hand. Drummond's CIO pointed an MCP at both sides, extrapolated a year out, and found more than $600,000 in shipping charges the company had absorbed and never passed on to its clients. The same tooling caught carrier overcharges worth filing claims against.

Chris Brock runs IT for a 90-year-old family-owned commercial print and fulfillment company in Atlanta and Jacksonville, just under $100 million in revenue, where the industry-standard ERP has atrocious reporting and a lot of the data going in is hand-typed. The working pattern is specific and repeatable: build an MCP over whatever legacy API or Postgres connection exists, plan on a frontier model, then hand that written blueprint to a cheaper model to execute inside explicit "only work in these lanes" guardrails. In CI/CD, agents security-check and end-to-end test other agents before a developer signs off. Licensing is centralized and allocated mainly to IT and some management, less for cost than for the MSA problem: a CSR dragging a customer-address CSV into an open model is a real contractual exposure. Nothing runs lights-out. Every process still has a human on green light, red light.

The part that answers the budget question is what happens after the win. A hard-dollar number converts the next AI request from a forecast into a case study, and it changes how you can approach a department. Lead with evidence of what a prior automation actually recovered and people stop hearing "we are automating you out of a job." The distinction Brock draws is between busy and strategic, not between human and machine.

Key takeaways:

• Reconcile two systems nobody has ever compared, such as a carrier bill against your own invoices, and extrapolate a year out before you ask for any AI budget. That is where the hard-dollar number comes from.
• Plan on a frontier model, then hand the written blueprint to a cheaper model to execute. Put the "only work in these lanes" constraints in writing first, because an unconstrained agent gets a mile down the road before you catch it.
• Put an agent on your agent. Security-check and end-to-end test the output in CI/CD before a developer ever sees it for sign-off.
• Build the MCP over the legacy system rather than waiting for better reporting. Most old software still exposes an API or direct Postgres, which is enough.
• Lead a funding ask with money you already recovered, not a productivity estimate, and open the same way when you go hunting for automatable work so the department does not get defensive.

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Picture your company a year from now with two dozen agents running in it. One of them starts returning a number that is quietly wrong. Nobody is named as its owner, nothing recorded which data it touched, and the model underneath it changed three weeks ago without anyone noticing. No better model fixes that, because the model was never the problem.

That is the gap between two numbers from the same BCG read: 82% of CEOs are more optimistic about AI than they were a year ago, and 6% of companies see meaningful value. The companies inside that 6% are working three pillars at once - people, data, and agent ops - and missing any one makes the other two stop mattering. The framing comes out of the ITIL discipline Sash and Chris both ran at Microsoft, updated for agents. On people it means three tiers rather than a single training push: baseline AI fluency for everybody, an orchestration layer of the people who own operational processes, and a small group of stewards who carry the message. Generic training does not move it. Training on your own workflows, with every AI-assisted result required to be explainable, does.

On data the number that lands is 7%, the share of organizations that believe their data is ready for AI, and the advice is deliberately small: pick one workflow, find the data sources it needs, go to the owners, fix and document the lineage for that one path, then reuse the process. If an answer cannot be traced to a source, the agent does not ship. Agent ops gets the most concrete treatment. Map a workflow step by step before deciding where AI belongs, and leave the steps code already handles deterministically alone rather than forcing tokens at them. Put a human checkpoint ahead of anything touching finance, external sending, or a system of record. Give every agent a named owner and a traceable action log, and stand up the eval layer before the agent count grows, because agents quietly atrophy as models change underneath them. The closing stretch looks at where this goes: Mastercard and Visa building a Know Your Agent framework, Mastercard expecting one in ten purchases to be agent-made by 2030, and Microsoft Scout putting an agent into Entra ID as a named identity that inherits DLP and compliance.

Key takeaways:

• Pick one workflow before touching the data estate. Identify its sources, find the owners, fix and document the lineage for that single path, then reuse the process on the next one.
• Do not ship an agent on data whose answers cannot be traced back to a source. If the trace is missing, you will not be able to reconstruct what went wrong.
• Put a human checkpoint in front of any agent action that touches finance, external sending, or a system of record.
• Give every agent a named owner and a traceable action log, and stand the eval layer up before the agent count grows. Agent behavior shifts as models change underneath it.
• Find the 5 to 10% of people already building without waiting for permission and make them your stewards. Train on your own workflows, not generic courses, and require that every AI-assisted result be explainable.
• Leave the steps that code already handles deterministically alone. Not every automation needs a model.

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Chris has spent months trying to get his agents to talk to each other, and this episode is the honest tour of three attempts: what broke, what held, and what he actually runs now. He frames it against four trust levers borrowed from how a person climbs through the ranks - human IS the loop, human IN the loop, human ON the loop, human OUT of the loop - and pins the current shift to Gary Vaynerchuk's 15/80/5 model: own the first 15 percent of an initiative, get out of the way for the 80 in the middle, come back for the last 5.

Attempt one was AgentMail, a webhook service that bills itself as Gmail for agents. Silas, Chris's Hermes chief-of-staff agent, had his own inbox and would email Neo, the coder agent, to hand off work. It worked on vibes. Conversations died silently after about the third message, and worse, they died without telling Chris - the failure mode every manager recognizes. Sash draws the distinction that explains it: these are separate Hermes profiles, peers rather than sub-agents under one parent, so there is no hierarchy to carry the handshake. Attempt two, Hermes's Kanban feature, fixed exactly that. Kanban is really a wake-up mechanism with a native acknowledgement handshake: the orchestrator posts a task to another profile's board, that profile gets a heartbeat, picks it up, and links its subtask back. Chris runs it autonomously at 4 AM against a backlog board, with a Postgres ledger tracking the traffic. It still runs, and it still only works between Hermes profiles on one gateway.

The gap that Kanban could not close is that Claude Code is Chris's real building harness and it cannot clear its own context window. His SDLC runs the superpowers plugin - brainstorming on Sonnet, design on Opus, writing plans on Sonnet, then the build loop on Opus or Fable - and every handoff between those steps needed a human to clear context, relaunch, and switch models. That is the thing that killed autonomy. Grove Rooms are the answer: a dedicated tmux session, synced to the Postgres ledger, that Silas spins up and then drives. Silas watches for the spec file to be committed, types /exit, relaunches Claude Code in the same session on the right model, and kicks off the next skill, unattended. Chris answers the brainstorming questions, walks away, gets a notice when the room moves from plan to build, and comes back to review and run close. The next move he talks himself into on air is cross-harness: have Silas hand Claude Code's finished work to a Codex-backed agent for review before it ever reaches him, and eventually replace the whole arrangement with the native A2A protocol that Hermes, ElevenLabs, Copilot Studio, and Foundry Agents already speak.

Key takeaways:

• Separate agent profiles are peers, not sub-agents. If your orchestration depends on a parent-child hierarchy that does not exist, messages will die silently. Check whether your transport has a real acknowledgement handshake before you trust it with unattended work.
• A ledger is what makes stepping back survivable. Chris tracks every agent-to-agent transition in Postgres, so oversight is a lookup rather than an argument about what happened overnight.
• The bottleneck in autonomous building is usually context, not capability. Claude Code cannot clear its own context window, so any pipeline that spans multiple skills needs something outside the session to relaunch it.
• Run different models for different phases. Brainstorming on Sonnet, design on Opus, planning on Sonnet, build on Opus or Fable - and let the orchestrator do the switching so the phase boundary costs you nothing.
• Apply 15/80/5 to a build pipeline literally: answer the brainstorming interview, leave, and come back for review and close. If you cannot name your first 15 percent and your last 5, you are not delegating, you are just hoping.

Watch the video version on YouTube · Full show notes, transcript + takeaways