3 posts tagged with "ai"

At Orus, we built an AI agent to handle the termination of a customer's previous insurance contract after they subscribe to a new one. The process covers roughly 500 cases a month. Internal estimates put the previous handling time at 15 to 20 minutes of human work per case.

On paper, it looked like a great fit for an agent. Read a contract, collect missing information, prepare a mandate for signature, send a registered letter, and keep the customer informed. The process was documented and repetitive. Very little of it seemed to require judgment.

Then we replayed real conversations. The documented process and what our operations team actually did diverged in roughly 30% of cases. Documents were incomplete. Customers replied out of order. Humans added attachments without explaining what they meant. Third-party APIs failed after an action had been approved. The same conversation could be picked up by several executions. Some cases waited for a signature for days.

The model was not the hard part. Building a system that could survive all of this was.

In its first six weeks of production, which happened to span the summer slowdown when volumes run well below the yearly average, reviewers made 659 validation decisions on the agent's proposals. They accepted 60% unchanged, corrected 35%, and rejected 5%. We review every customer-facing action, which gave us a safe rollout path and a dense feedback loop. As evidence accumulates, execution can become automatic, backed by deterministic checks, for actions where the risk justifies it.

On our last project, the way we planned, built and reviewed the work changed more than the code itself. AI agents were part of the project from day one, no longer a tool we used from time to time. This post is about that method.

The project was a rebuild of our organizations module, one of the oldest and most central parts of the codebase. It models partners, brokers and their members, and it sits upstream of most of what the platform does. Replacing it had been on the backlog for a while. It was the kind of project that stays "not yet" because the risk surface is high: everything touches it, and touching it wrong has cascading consequences.

We shipped it. We sized it at around 30 working days, shipped in about 20, and every milestone landed ahead of its ETA. But the schedule is not the point. The method is, and it is one we want to apply to all our next projects, not just this one.

Every Orus insurance contract ships with a stack of legal documents: the contract terms, the insurance certificate, invoices, fiscal attestations for tax-deductible contracts. Their format is regulated, their content reviewed by legal counsel, and they're rendered dynamically from subscription data: customer name, company SIREN, coverages, premium, any clauses negotiated for that specific deal.

We add new products every few weeks. At the time of writing this, we render 60 distinct customer-facing document templates across our French and Spanish products. The whole thing is 218 React components.

Adding a new template used to take a couple of days. We've brought it down to a few hours by building a Claude Code skill that handles the tedious parts, with a human still in the loop on every non-trivial decision. This article walks through what the skill does, what we deliberately keep human, and the gotchas baked into it.