Three safeguards for automation that scales without breaking
Treat your AI agent like a bright intern on their first day. If you don’t give them a brief, they’ll reorganize the stationery cupboard and accidentally ship to the billing address. AI agents don’t fail because they’re “dumb”; they fail because the inputs are. A data contract is the induction pack: it tells carriers exactly what to supply — delivery timeframes, capacity limits, service restrictions, and real-time status updates. That isn’t just tidying data; it’s creating a shared language so automated choices are fast and, crucially, sane. Joke’s on us if we skip the briefing and then complain the intern can’t read our minds. Because the next question is: have we also told them what to do when things go sideways?
So, coach the intern because Black Friday is coming. Order volumes spike across Europe; operators log record parcel weeks and locker networks hit new highs. Finland's Posti alone handled ~1.5 million parcels in Black Week 2024, while InPost processed 322 million parcels in Q4 and nearly 14 million in a single pre-Christmas day. Out-of-home capacity is both a gift and a choke point.
The question isn't whether you'll handle the volume; it's whether your systems will make the right decisions without you. Think of checkout like a well-designed choice architecture. The customer sees three options: home, locker, and service point. Behind each sits structured intelligence: carrier availability, cutoff times, and current capacity. The agent isn’t guessing; it’s routing to the best available promise in real time, not clinging to a static rule dreamt up three quarters ago. That’s the agent-ready translation: from “customer picks a carrier” to “system picks the carrier that won’t embarrass you tomorrow.”
And when that data lights up a constraint — capacity tightening, cutoffs looming — the difference between a tidy reroute and a raging fire is whether your rules are written down or merely hoped for… which is where the runbook earns its keep.
The data contract. Structured carrier intelligence
AI agents need clean, standardized data to make routing decisions. When carrier APIs return unstructured responses, vague statuses, inconsistent formats, and missing delivery windows — automation fails.
A data contract is the proper induction pack. It spells out what carriers must provide — delivery timeframes, capacity limits, service restrictions, and real-time status updates. That’s not mere “normalization”; it’s a shared language that lets automated decisions be fast, legible, and sane.
Picture checkout with three options: home, locker, service point. Behind each is structured data — carrier availability, cutoff times, and current capacity. The agent doesn’t guess; it routes to the best available promise in real time, instead of clinging to static rules written three quarters ago.
Agent-ready translation: from “customer picks a carrier” to “system routes to the best option based on live capacity and delivery promise.”
And once your “intern” can speak the language, the next test is tougher: what do they do when the world misbehaves?
The runbook. Exception rules that scale
An AI agent is a superb intern on a good day and a bewildered tourist on a bad one. Routine? They glide. Then comes a carrier outage, a customs hiccup, or a last-minute capacity squeeze, and without instructions, they’ll start “solving” the problem by rearranging the mugs. A runbook is the grown-up manual: predefined scenarios, clear escalation paths, and explicit triggers for what to do next.
Peak season turns edge cases into the main act. ICS2 Release 3 landed across EU borders in September 2025, and 15 member states requested enforcement extensions through year-end — translation: more compliance friction at the worst possible time. With a documented protocol, the agent doesn’t dither; it reroutes on rules and keeps promises intact while humans sleep.
Example: A carrier marks 500 parcels as “delayed - pending customs data.” The agent checks the runbook, identifies this as an ICS2 compliance issue, and automatically switches subsequent orders to compliant carriers while alerting the operations team.
Get the playbook right and your intern starts looking suspiciously like management. But there’s a sharper question lurking: how do you prove the rules are working in the wild, not just on paper…
The kill-switch. Controlled rollback
Every good AI agent needs a red button. Not because you don’t trust them but because, eventually, even the brightest intern will enthusiastically over-index on the wrong signal. When automation starts over-allocating to a saturated carrier, misrouting high-value shipments, or triggering cost overruns, you don’t negotiate; you roll back — fast — without switching the whole system off and lighting candles.
A kill-switch isn’t binary drama; it’s granular control. Think of it as dimmer switches for reality: pause automation for a specific carrier, lane, or customer segment while humans take the wheel. The rest of the machine keeps humming, under manual oversight, until the glitch is diagnosed and the agent’s incentives are brought back into polite society.
Example: An AI agent starts routing 70% of parcels to lockers based on capacity data, but customer complaints spike. The operations team triggers a partial rollback. Automation continues for standard parcels, but high-value and express orders default to manual approval.
Build this muscle and you don’t fear errors — you bound them. Which begs the next, slightly uncomfortable question: how do you spot trouble early enough to hit the button before the dashboard starts smoking?
Why this matters for peak 2025
Shoppers have quietly raised the bar while we were busy optimizing banners. Seventy-six percent say a good returns experience makes them more likely to buy again. Meanwhile, real-time tracking and having multiple delivery options sit at the top of the wish list in Geopost’s E-shopper Barometer — “tell me where my parcel is now and give me choices” has become table stakes.
An AI agent-ready system just… does this: it auto-picks the right carrier in the moment, nudges customers before a delay becomes a Twitter thread, and routes returns intelligently without turning your ops team into a festive call-center choir. It’s the difference between promising certainty and engineering reassurance. If you can manufacture certainty at peak, customers repay you with trust — and trust compounds.
Which leads to the uncomfortable question every dashboard is too polite to ask: if reassurance is the product, are you measuring it or merely shipping parcels?
Pre-peak checklist
Think of this as your pre-flight. Before Black Friday hits, run three drills:
- Data Contract – Treat it like the intern’s briefing pack: confirm every carrier supplies structured delivery data (timeframes, capacity, service levels). No format, no flight.
- Runbook – Write down the panic before the panic: document exception scenarios and agent responses (carrier outages, customs delays, capacity limits). If X happens, do Y.
- Kill-Switch – Practice a granular rollback: prove you can pause automation for specific carriers and routes without nuking the whole system.
AI agent-ready commerce doesn’t replace human judgment; it bottles it, so it scales when volumes surge. Get these safeguards in place now and peak turns from crisis theater into opportunity engineering.
Author
Thomas Bailey
Product Innovation Lead, nShift
Thomas plays a key role in shaping how new features and platform improvements deliver real value to customers. With a background spanning product, tech, and go-to-market strategy, he brings a pragmatic view of what innovation looks like in practice and how to make delivery experiences work harder for your business.
