MGH Agency · Managed AI agent teams

While you read this, our agents are working.

Content written, operations analyzed, back-office kept current — by a team of AI agents that runs on a schedule, reviews its own work, and cannot ship a single thing without a human saying yes.

The recurring work that eats your week, running without you — while you keep the only part that was ever really yours: the decision.

Ask for a fitHold the gate yourself ↓
An agent team, working — live-style replay of real event shapes
06:08ContentBot run started — task T43: mortgage guide, optimization pass

You tried the chat box.

It answers questions. It doesn’t do the work, and it doesn’t come back tomorrow.

You saw the agent demos.

Impressive for four minutes. Nothing shipped, nothing reviewed, nobody accountable.

You can’t review everything.

So either the AI ships unreviewed — terrifying — or you become the bottleneck. Both kill it.

The missing piece was never a smarter model. It was the management layer — review, rejection with reasons, a permanent record, and one human gate. That layer is the product.

50+

scheduled agent runs every week, on MGH's own operation

340+

recorded verdicts in the approval ledger since July

150+

deliverables shipped through the gate

0

shipped without a human approving. Ever.

Our own numbers, from the system running MGH — not projections.

Your job is one tap. Try it.

This is the entire interface between you and the team: a queue of reviewed, recommended work waiting on your verdict. Approve and it ships. Reject and your reason goes back — and the agent must fix it before it can return.

Everything else — the drafting, the checking, the re-running of numbers, the chasing of stale work — already happened before it reached you.

Try it — you hold the gate

ContentBot · T51Orchestrator reviewed · recommends approve

New landing page: “Cut fulfilment errors in half”

Draft attached. Every claim traced to your own case notes; two flagged [NEEDS YOU] where only you know the fact.

How it works under the hood

Agents read your real systems

Not a chat window — scheduled agents with scoped, read-only access where possible, working inside the systems your business already runs on: repositories, a SaaS platform’s database, a mailbox, your documents.

They propose; they don’t publish

Every deliverable arrives as a proposal: a draft, a pull request, an analysis with its evidence attached. Money, legal commitments, anything external under your name — red lines no agent can cross.

A human gate on everything that ships

An orchestrator agent reviews first — re-running the numbers, checking claims against sources — and recommends. You approve or reject. Approval is a click; nothing moves without it.

Everything is on the record

Every submission, review, rejection and approval is an event in an append-only log. Reject rates per agent, how long work has been waiting, who decided what — answerable from the record, not from memory.

What the engagement feels like

You manage the team the way you would manage people — on three loops:

Every scheduled run

Agents work their queues on a fixed cadence — one task per run, oldest first, with a gate that refuses to let work quietly age. A run that starts and never finishes is a visible state, not a silence.

Every day, ~15 minutes

Your approval queue: what needs a decision, what needs an answer, what is ready to ship. Reviewed and recommended before it reaches you, so you are deciding, not auditing.

Every week, per agent

A short check-in in the agent’s own words: what it worked on, why it thinks its problems are happening, where it wants to point next. Your reply becomes its standing direction until the next one.

The weekly check-in is the piece people don’t expect: each agent reports in the first person, is required to reason about the cause of its own failures — including naming the process, not just itself — and your recorded reply becomes the direction it reads at the start of every run until you replace it.

What runs on it today

This is not a concept page. MGH runs its own operation on this system, every day:

  • Content and SEO teams writing, optimizing and maintaining several live websites — every article through the review gate before it ships.
  • An operations analyst that maps how a business actually runs and what its operations cost — working read-only inside the platform’s database, whether that is a SaaS product, a CRM or an ERP. Runs today on Espresso, MGH’s own operations platform; every analysis is re-verified against live data before it reaches anyone.
  • A back-office agent keeping company correspondence and filings tracked — built so it never interprets a rule from memory and never touches the ledger itself.
  • Research scouts feeding the workers demand evidence — proposals only; a supervised orchestrator decides what enters any queue.

Where it fits

A good fit has three ingredients: a system of record an agent can read — a SaaS platform, a CRM or ERP, a repository, a mailbox; recurring work with real volume where judgment is reviewable; and someone on your side who can spend minutes a day deciding. If your operation has those, an agent team can probably carry a real share of the recurring load. If it doesn’t, we will tell you that in the first conversation — a fit conversation is cheaper than a bad engagement for both of us.

Questions people ask

Do the agents act on their own?

They work on their own — research, drafts, analysis, proposed changes — but nothing external ships without a human approving it. Publishing, merging, sending, anything under your name: those stay behind an approval gate by design, not by policy document. An agent that wants to ship something can only mark it ready for review.

What systems can an agent team work in?

Anywhere there is a system it can read and an output a human can review: code and content repositories, a SaaS platform’s database, a mailbox, spreadsheets and documents. Today that means live teams doing content and SEO work in web repositories, operations analysis against a SaaS product’s database, and back-office work on company correspondence. We fit the team to your stack rather than promising a universal integration.

How do you steer an agent team?

Three loops. Daily: every deliverable is reviewed and approved or rejected with a written reason, and rejections feed back into how the agent works. Weekly: each agent writes a short first-person check-in — what it did, what it thinks is causing its problems, where it wants to focus — and your reply becomes its standing direction for the next cycle. Continuously: reject rates and open-work ages are tracked per agent, so a drifting agent is visible in numbers, not in vibes.

What happens when an agent gets something wrong?

It gets rejected, with a note that says exactly what was wrong — and both the rejection and the note are recorded in a ledger. Agents are required to speak to their own reject patterns in their weekly check-ins, including naming process causes rather than just promising to be more careful. An agent whose rate stays high gets reviewed: its instructions change, its model changes, or it is retired.

Is this a product I install, or a service?

A managed service. MGH designs the agent team around how your business actually operates, runs it, reviews its output, and hands you an approval queue that takes minutes a day. The starting point is a fit conversation about your operation — not a license key.

Last updated: August 2026

Your competitors’ agents start tonight. Yours could too.

Tell us what your operation actually looks like — the systems, the recurring work, who would hold the gate. We’d rather say “not yet” honestly than run an agent team somewhere it can’t earn its keep.

Ask for a fit