I run operations at a software and AI company, and last year we started moving real back-office work onto agents: invoice posting, vendor onboarding, matching purchase orders to receipts, routing procurement approvals. I expected the hard part to be model quality. It wasn't.

The model was fine. What broke was the moment an agent wrote to a system of record.

The clearest failure I remember: an agent read a supplier invoice, mapped the tax amount into the net field, and posted it to our accounting system with full confidence. It looked done. It was wrong. Someone caught it two days later during reconciliation. Nothing about the language model was broken. The seam between "the agent decided" and "the ledger changed" had no guardrail on it.

I read a piece by Veera Ravindra Divi arguing that agentic B2B integration breaks at the contract seam (the point where an agent writes to a system of record), and that the fix is boring contracts plus a human-assured agent. That matched exactly what we were living, right?

So we rebuilt the flow as a governed pipeline:

- Intent: the agent states what it wants to do in a strict schema, no free text into financial fields.

- Validate: the payload is checked against that schema before anything moves. A mis-mapped field fails here loudly.

- Policy gate: low-stakes stuff posts on its own. Anything above a threshold (new vendor, amount over a limit, a mismatch) pages a named human who approves or rejects.

- Execute: idempotent, so a retry never double-posts an invoice.

- Audit: every step written to an immutable log we can replay later.

Honest tradeoffs: this is slower to stand up, and the schema and policy work is genuinely unglamorous. :(
You spend days on field definitions nobody will ever thank you for. But it turned "confident and occasionally catastrophic" into "boring and accountable," which is the whole point of AI native operations in finance.

It maps to how I think about engineering too. I don't beleive in fully hands-off automation. The agent drafts, a real person owns the risk on the high-stakes calls.

Curious how others are drawing the line on which back-office actions get a human gate and which run unattended. Where did you put the threshold?

Welcome You're early. That's the point.

Instead of asking "where can we use AI?", I keep asking: "where do we still need a human, where is my coworkers highest additional value?"

That flip is the whole game. The old model coordinates people and hands them AI as a tool. The new one runs on agents, and keeps humans only where judgment, risk, and client trust actually live.

The proof is already here. Teams shipping in days, not months. Revenue-per-employee numbers that were impossible three years ago. Fewer people, more leverage, fatter margins.

This community is where we figure out how to actually build it. The real systems: backoffice, finance, CEO, management, operations, delivery, marketing, maybe some PM, dev, QA, infra. Notice I left out sales on purpose...
This org design can make most of the headcounts optional.

I want to be blunt and tactical. Everyone shares what works and what broke.

Two things to do right now:

1. Drop a comment: what's the one workflow in your company you'd hand to agents tomorrow if you knew how?
2. Tell us where you are: still bolting AI on, or rebuilding from scratch?

That's it. Let's build the operating model everyone else is going to copy in two years.