Post-Launch Marketing Checklist for UCP Stores (2026)

We watched a client’s Universal Commerce Protocol manifest go live on a Tuesday. Clean validation, green checkmarks, everything the launch guide promised. By Friday they had exactly zero agent-driven transactions, and the founder messaged us at 11pm convinced the whole thing was broken. It was not broken. It was launched but unmarketed, sitting in the digital equivalent of a warehouse with no address. That gap between “technically live” and “actually generating agentic sales” is exactly what a real post-launch marketing checklist is built to close, and it is the single most underrated phase of any UCP rollout we run.

Here is the uncomfortable truth we tell every merchant: the launch is not the finish line, it is the starting gun. A conformant manifest is table stakes now. According to UCP Checker, which independently monitors more than 22,295 storefronts, roughly 73% of the stores it tracks pass full UCP validation (16,376 verified), though that sample skews heavily toward Shopify and a conformant manifest is not the same thing as an AI agent being able to complete a real checkout. Validation is a prerequisite, not a result. What separates the stores that get agent traffic from the ones that stay invisible is everything you do in the ninety days after launch: the monitoring, the promotion, the data hygiene, the feedback loops. This is the post-launch marketing checklist we actually run for clients, ordered by impact, with the strongest work first.

TL;DR

Monitoring beats marketing on day one: Before you promote anything, instrument your UCP endpoints so you can see agent traffic, failed checkouts, and manifest drift in real time. You cannot optimize what you cannot measure, and most launches fail silently for days.
Discovery is the whole game: Agents will not find you because your manifest is valid. A post-launch marketing checklist for UCP is really a discovery and data-quality checklist, structured feeds, machine-readable product data, and submission to the surfaces agents actually crawl.
Optimization is a ninety-day rhythm, not a task: Treat post-launch as a 30/60/90 cadence with named KPIs, agent completion rate, time-to-first-agent-sale, manifest uptime, and iterate on the data your agents actually consume, not vanity metrics.

1. Instrument Real-Time Monitoring Before You Promote Anything

The very first item on any post-launch marketing checklist has nothing to do with marketing. It is instrumentation. In our experience, the number one reason a UCP launch underperforms is not bad marketing, it is that nobody notices when the thing quietly stops working. That client who messaged us at 11pm? If we had shipped monitoring before we shipped promotion, we would have known within minutes whether the problem was traffic, checkout, or manifest drift.

Set up endpoint monitoring that pings your UCP manifest and checkout intent endpoints at least every 60 seconds, with alerts firing if response time exceeds 800ms or if any endpoint returns a non-200 status twice in a row. We treat a manifest that takes longer than two seconds to serve as effectively broken for agent purposes, because many agent orchestrators time out aggressively and simply move to the next merchant. You want to catch that before a customer or, worse, an agent does.

Beyond uptime, instrument the funnel. Log every agent request to your manifest, every product query, every checkout intent, and every completion or abandonment. The gap between “manifests served” and “checkouts completed” is the single most diagnostic number you will track in the first month. If agents are reading your manifest but never completing purchases, you have a data or checkout problem, not a discovery problem, and that distinction changes your entire priority list. The deep mechanics of what agents actually request are covered well in the UCP technical architecture deep dive, which we lean on when setting up logging schemas.

Best for: Every merchant, no exceptions. This is the foundation the rest of the checklist stands on.

Monitoring checklist:

Uptime pinging: Poll manifest and checkout endpoints every 60 seconds with alerting on two consecutive failures.
Latency threshold: Alert at 800ms, treat anything over 2 seconds as functionally down for agents.
Funnel logging: Capture manifest reads, product queries, checkout intents, and completions as distinct events.
Drift detection: Snapshot your manifest daily and diff it, so an accidental schema change surfaces in hours not weeks.
Alert routing: Send failures to a channel a human actually watches, not an inbox nobody opens.

2. Validate That Agents Can Actually Complete a Checkout

Passing UCP validation and completing a real transaction are two different achievements, and conflating them is the most expensive mistake we see. This is why we pair the UCP Checker adoption figure with a hard caveat every time: a green validation badge tells you your manifest is structurally correct, it does not tell you that an agent can put an item in a cart, apply the correct tax, and complete payment without hitting a wall.

What this achieves: end-to-end confidence that the money path works before you spend a dollar driving agent traffic to it. Run a real agent through the full purchase flow using a test order, and do it for at least three representative product types: a simple single-variant product, a product with multiple variants, and a product with shipping complexity or a subscription component. Each of these exercises a different part of your checkout logic, and in our builds the variant-selection step is where things break most often.

We tell clients to test against more than one agent orchestrator where possible, because agents do not interpret the spec identically. A checkout that completes cleanly through one agent may stall on another due to how it handles optional fields, currency formatting, or inventory availability signals. The difference between the UCP standard and the competing approaches, and why interpretation varies, is worth understanding, and we point people to the breakdown of UCP vs ACP and which standard will rule the agentic web so they know what edge cases to expect.

Standout feature to verify: idempotency. Confirm that if an agent retries a checkout request after a timeout, you do not create duplicate orders. This single failure mode has caused more post-launch fire drills in our practice than any other checkout bug.

Checkout validation checklist:

Three product types: Test single-variant, multi-variant, and subscription or shipping-complex products end to end.
Multiple agents: Run the flow through at least two agent orchestrators to catch interpretation differences.
Idempotency proof: Retry a timed-out checkout and confirm no duplicate order is created.
Tax and currency: Verify correct tax calculation and currency formatting in the completed order.
Confirmation loop: Ensure the agent receives a clean order confirmation payload it can relay to the shopper.

3. Optimize Product Data for Machine Readability

Once you know the checkout works, the next highest-impact task is data quality, because this is what actually makes your products discoverable and comparable to agents. Humans forgive a vague product title and infer meaning from a photo. Agents do not. They parse structured attributes, and an incomplete or ambiguous attribute set means your product loses to a competitor whose data is cleaner, even if your product is better.

We audit every attribute an agent is likely to filter or rank on: precise category, material, dimensions, compatibility, color as a controlled value rather than a marketing name, and availability as a real-time signal. In our experience, merchants underinvest here catastrophically, treating product data as a marketing afterthought when it is now the primary interface between their catalog and the buyers. The shift from human-first to machine-first product data is the whole thesis of the rise of machine-readable commerce and how UCP changes SEO, feeds, and product data, and it reframes what “good product content” even means.

A concrete threshold we use: aim for at least 90% attribute completeness across your top 100 SKUs by revenue before you consider your data launch-ready for marketing. Below that, you are pouring agent traffic into a catalog that cannot represent itself well, and your completion rate will suffer no matter how much discovery work you do. Prioritize by revenue, not alphabetically, because the long tail can wait but your bestsellers cannot.

Best for: Merchants with more than 50 SKUs, where manual data entry has historically been inconsistent.

Product data checklist:

Controlled values: Replace marketing color names with standardized attribute values agents can filter.
90% completeness: Hit 90% attribute completeness on your top 100 revenue SKUs before scaling promotion.
Real-time availability: Serve live inventory status, not a stale daily snapshot.
Dimensions and compatibility: Include the specs agents use for filtering and cross-sell decisions.
Revenue-first prioritization: Clean your bestsellers first, let the long tail follow.

4. Submit and Register With Agent Discovery Surfaces

This is the item most merchants think is the entire post-launch marketing checklist, and it is important, but it only works once items one through three are solid. Agents discover merchants through a mix of directories, protocol registries, and crawlable manifest declarations, and being present on those surfaces is non-negotiable if you want traffic.

What this achieves: it moves you from “technically reachable” to “actually indexed by the systems that route agent purchases.” Make sure your manifest is declared in the standard discoverable location and referenced from your root domain in the way the spec expects, so crawling agents find it without guessing. Register with any protocol registries relevant to your market, and confirm your listing shows accurate categories and capabilities, because a miscategorized listing routes the wrong agent queries to you.

We treat this like SEO submission from the early web, except the crawlers are agents and the ranking signals are your data quality and reliability rather than backlinks. The mental model of the store as something a machine reads and acts on, not a page a human browses, is laid out clearly in what happens when AI agents become the primary shoppers, and we hand that piece to skeptical stakeholders who still picture a human clicking through a storefront.

Discovery checklist:

Standard location: Serve your manifest from the spec-expected discoverable path and reference it from root.
Registry listings: Register with relevant protocol registries and verify accurate categorization.
Capability accuracy: Confirm your declared capabilities match what your checkout actually supports.
Crawlability check: Test that a crawling agent can find your manifest without manual URLs.
Category correctness: Fix any miscategorization that would route the wrong queries to you.

5. Build a Feedback Loop From Agent Behavior

The stores that win in the ninety days after launch are the ones that read agent behavior like a conversation and adjust. Every failed query, every abandoned checkout intent, every attribute an agent requested that you did not serve, is a signal telling you exactly what to fix next. This is where post-launch optimization stops being a checklist and becomes a discipline.

What this achieves: it converts raw agent traffic into a prioritized backlog of fixes ranked by revenue impact. We build a weekly review in the first month, then biweekly, where we look at the top ten agent queries that returned no result or an incomplete result. Those are demand signals for products, attributes, or capabilities you are missing, and they are far more valuable than any generic keyword report because they come from actual purchase attempts.

The launch tells you your manifest is valid; the ninety days after tell you whether your catalog can actually sell to a machine, and the only way to know is to watch what the agents do.

In our experience, the merchants who treat agent logs as a feature-request pipeline outpace the ones who treat launch as done. One pattern we see repeatedly: an agent queries for a filter attribute the merchant never populated, silently gets no match, and the merchant assumes there is simply no demand. There was demand. It was invisible because the data was missing. Reading the logs closes that gap.

Feedback loop checklist:

No-result query review: Surface the top 10 agent queries that returned nothing, weekly at first.
Missing-attribute demand: Treat requested-but-unpopulated attributes as prioritized data work.
Abandonment forensics: Trace where in the checkout agents drop off and fix the highest-volume step first.
Revenue-weighted backlog: Rank every fix by the revenue of the products it affects.
Cadence discipline: Weekly reviews for month one, biweekly after, never let logs go unread.

Turn Your Live Manifest Into Agent-Driven Revenue With UCPhub

If your UCP manifest is live but quiet, the problem is almost never the protocol, it is everything on this checklist that happens after launch. This is exactly the work our team does every week: instrumenting monitoring, hardening the checkout path, cleaning product data to machine-readable standards, and building the feedback loops that turn agent traffic into completed sales. UCPhub’s platform is built around the Universal Commerce Protocol so you are not stitching together point solutions that break the moment the spec evolves. If you want a launch that actually generates agentic sales instead of just passing validation, talk to our team and we will run this checklist with you against your live store.

6. Harden Reliability and Error Handling

Discovery gets agents to you; reliability is what keeps them buying. Agents are ruthless about failure in a way humans are not. A shopper who hits an error might refresh and try again; an agent will often abandon your merchant entirely and complete the purchase elsewhere within the same session. That means every error is potentially a permanently lost sale, not a temporary annoyance.

What this achieves: it protects the completion rate you worked to earn by making sure transient failures do not become lost transactions. We set explicit error-handling behavior for the common failure modes: inventory that changes between query and checkout, payment authorization delays, and partial availability. For each, the manifest and checkout response should return a structured, actionable error the agent can interpret, not a generic 500 that the agent reads as “this merchant is unreliable, skip it.”

We also stress-test rate limits. During a promotion or a spike in agent interest, your endpoints can get hammered, and if your rate limiting is naive you will start rejecting legitimate agent requests. We recommend load-testing to at least 3x your expected peak agent request volume before running any campaign that could drive a surge. The comparison of building this yourself versus using managed infrastructure, and where the reliability tradeoffs land, is covered in the UCP Hub vs custom integration comparison guide.

Reliability checklist:

Structured errors: Return interpretable, actionable error payloads, never bare 500s.
Inventory race handling: Define behavior for stock that changes between query and checkout.
Rate limit headroom: Load-test to 3x expected peak agent volume before campaigns.
Graceful degradation: Ensure a failure in one endpoint does not cascade to the whole flow.
Retry semantics: Make retries safe, tying back to the idempotency work from item two.

7. Launch a Coordinated Announcement Across Human and Agent Channels

Now, finally, the traditional marketing. With monitoring, checkout, data, discovery, and reliability all solid, a launch announcement actually has somewhere to send traffic. Announce to your human audience through the channels you already own, email, social, and existing customers, but frame the message around what is new for them: faster checkout through their preferred AI assistant, availability wherever they shop with an agent.

What this achieves: it creates the initial burst of real transactions that seeds your feedback loop with meaningful data, faster than organic agent discovery alone would. In our experience the human announcement matters even in an agentic world, because early adopters who deliberately try buying through an agent generate the first wave of high-signal completion data you desperately need in week one.

Coordinate the timing with your monitoring. We never announce on a Friday, because if something breaks under the traffic you want humans awake to fix it. We schedule announcements early in the week, mid-morning in your primary market’s timezone, and we keep the monitoring dashboard open for the first 24 hours. A launch announcement without live monitoring is how you end up with the 11pm founder message.

Best for: Established merchants with an existing customer base to activate; newer stores should weight effort toward discovery items three and four instead.

Announcement checklist:

Benefit framing: Message the shopper outcome, not the protocol name.
Owned channels first: Activate email, social, and existing customers before paid.
Early-week timing: Announce Monday to Wednesday, mid-morning, never Friday.
Live monitoring window: Watch dashboards for the first 24 hours post-announcement.
Feedback capture: Ask early adopters to report friction so you catch what logs miss.

8. Monitor and Manage Manifest Drift Over Time

Your manifest is not a static artifact, it is a living declaration that drifts as your catalog, pricing, and capabilities change. A product goes out of stock, a price updates, a variant is discontinued, and if your manifest does not reflect that in near real time, agents act on stale data and either fail checkouts or, worse, complete a sale you cannot fulfill.

What this achieves: it prevents the slow, silent decay that turns a great launch into a mediocre performer three months later. We diff the manifest daily against the source of truth in the commerce platform and alert on unexpected divergence. The word “unexpected” matters, because legitimate changes happen constantly; the goal is to catch the change nobody intended, like a bulk edit that wiped an attribute across a category.

This is genuinely ongoing operational work, and it is where the choice between a managed hub and a custom build shows its cost over time. Point solutions tend to handle the launch snapshot fine and then rot, which is the exact argument in why point solutions won’t scale. We would rather clients invest in drift monitoring on day one than debug a mystery revenue dip in month three.

Drift management checklist:

Daily diff: Compare manifest to platform source of truth every day.
Unexpected-change alerts: Flag divergence nobody intended, especially bulk edits.
Price and stock sync: Confirm pricing and availability propagate within your target latency.
Deprecation handling: Ensure discontinued products leave the manifest cleanly.
Version tracking: Keep a history so you can roll back a bad change fast.

9. Run the Continuous Optimization Framework

Everything above becomes durable only when you put it on a rhythm. This is the framework we run with clients through the first ninety days, and it is deliberately simple because complex processes get abandoned. We call it Detect, Diagnose, Fix, Verify, and we cycle it every week in month one.

Detect. What this achieves: it surfaces the highest-impact problem before it costs you significant revenue. Pull the week’s monitoring and log data and identify the single biggest gap, whether that is a drop in completion rate, a spike in a specific error, or a cluster of no-result queries.

Diagnose. What this achieves: it stops you from fixing symptoms instead of causes. Trace the detected issue to its root, is the completion drop a data problem, a checkout bug, or a reliability event? The monitoring instrumentation from item one is what makes this diagnosis fast instead of guesswork.

Fix. What this achieves: it converts insight into a shipped change with a clear owner and deadline. Implement the specific change, whether that is populating a missing attribute, patching an error handler, or fixing a variant mapping, and ship it with a note on what you expect it to move.

Verify. What this achieves: it proves the fix worked and prevents regressions. Watch the relevant metric for a defined window, usually one week, and confirm the change produced the expected improvement before moving to the next cycle. If it did not move, you diagnosed wrong, go back.

This loop is the practical expression of the broader implementation approach we document in the how to implement Universal Commerce Protocol implementation guide, applied specifically to the post-launch window.

Framework checklist:

Weekly cadence month one: Run the full Detect-Diagnose-Fix-Verify loop weekly at first.
Single highest-impact focus: Fix one root cause per cycle, resist scattering effort.
Named owner and deadline: Every fix has a person and a date, no orphan tasks.
Expected-outcome note: Predict what the metric should do before shipping.
Verify before advancing: Confirm improvement over a one-week window before the next cycle.

10. Track the KPIs That Actually Predict Agentic Revenue

The last item is measurement, and it is last because it depends on everything above being in place to produce meaningful numbers. Vanity metrics will lie to you here. “Manifests served” feels good and tells you almost nothing; agent checkout completion rate tells you whether the whole machine works. We anchor every post-launch engagement on a small set of KPIs tracked across a 30/60/90 day horizon.

The metrics that matter are the ones tied to real transactions and reliability, not exposure. If you are just getting started, the four below are enough; do not drown yourself in dashboards. If you already have a live UCP store and are auditing it, these are the numbers to pull first, and a low completion rate against high manifest reads is your loudest signal that the problem is downstream of discovery. For the strategic context of where these metrics sit in the bigger UCP picture, the Universal Commerce Protocol insights hub is where we send merchants who want the wider view.

KPIs and 30/60/90 day outcomes:

Agent checkout completion rate: Day 30 establish a baseline, day 60 improve it by a measured margin, day 90 stabilize above your target threshold. This is the north-star metric.
Time to first agent sale: Day 30 measure how long from announcement to first agent-completed order, and drive this toward hours not days on subsequent product additions.
Manifest uptime and latency: Day 30 confirm 99.9% uptime and sub-800ms latency, day 60 and 90 hold it under promotional load.
No-result query rate: Day 30 baseline, day 60 cut it through data work, day 90 keep it low as a proxy for catalog completeness.
Manifest drift incidents: Day 60 and 90 track how many unintended manifest changes reached production, driving toward zero.
Error rate by type: Day 30 categorize, day 90 show sustained reduction in the top failure modes from item six.

If you are launching a store on Shopify or WooCommerce, the platform-specific mechanics of wiring these metrics into your existing stack are worth reading in the Shopify UCP integration guide and the WooCommerce UCP integration guide, and if you are still deciding whether to build or buy this whole apparatus, our team is happy to walk through your specific setup.

If you are just getting started, prioritize items one and two above everything else, monitoring and a verified end-to-end checkout, because promoting a store where agents cannot actually buy is worse than not promoting at all; you burn discovery goodwill and generate zero data. If instead you are auditing a UCP store that already launched, start at item ten and work backward, pull your completion rate and no-result query rate first, because those two numbers will tell you within an hour whether your problem is discovery, data, or checkout, and that diagnosis dictates everything else you do.

Next Steps:

Instrument monitoring today: Stand up endpoint pinging and funnel logging before you touch any promotion.
Run one full checkout test: Push a real test order through an agent for a multi-variant product this week.
Pull your completion rate: If you are already live, measure agent checkout completion against manifest reads to locate your bottleneck.

Frequently Asked Questions

What should be on a post-launch marketing checklist for a UCP store?

A post-launch marketing checklist for a UCP store should start with instrumentation, not promotion. The first three items are always monitoring your endpoints in real time, verifying that agents can complete an actual checkout end to end, and optimizing your product data to machine-readable standards. Only after those are solid do you move to discovery submission, feedback loops, reliability hardening, and the human-facing announcement.

The mistake we see most often is treating the checklist as pure marketing, blasting out an announcement while the underlying machine is untested. In an agentic context, marketing without instrumentation is how launches fail silently for days. Our checklist deliberately front-loads the technical validation work because a valid manifest is not the same as a store agents can buy from, and driving traffic to a broken checkout wastes both the traffic and the discovery goodwill you will need later.

We also insist the checklist ends with measurement, a small set of KPIs tracked on a 30/60/90 day rhythm, so post-launch becomes an ongoing optimization discipline rather than a one-time event you declare done.

How do you prepare for post-launch optimization?

Preparation for post-launch optimization happens before launch, ironically. The single most important preparatory step is instrumenting your monitoring and logging so that the moment you go live, you are capturing agent requests, checkout intents, completions, abandonments, and endpoint health. If you launch without this in place, your first weeks of the most valuable diagnostic data are simply gone, and you cannot recover them.

The second preparatory step is getting your product data to at least 90% attribute completeness on your top revenue SKUs, because optimization on a thin data foundation just surfaces the same missing-attribute problem over and over. Clean data upfront lets your optimization cycles focus on higher-order improvements instead of basic hygiene.

Finally, we prepare by defining the KPIs and thresholds in advance, agent checkout completion rate, latency targets, no-result query rate, so that when data starts flowing we already know what “good” looks like and can act immediately instead of debating definitions. The Detect-Diagnose-Fix-Verify framework we run is only as fast as the preparation that precedes it.

What tasks follow a product launch in an agentic commerce context?

The tasks that follow a UCP product launch differ from a traditional launch because your primary audience includes machines. Immediately after launch, the priority tasks are watching your monitoring dashboards during the first 24 hours, verifying real checkouts complete cleanly, and confirming your manifest is discoverable by crawling agents at the spec-expected location.

Within the first week, the tasks shift to reading agent behavior, specifically the no-result queries and checkout abandonment points that tell you what is missing or broken. These become your prioritized backlog. In parallel, you coordinate the human-facing announcement, timed early in the week with live monitoring, to seed your feedback loop with real transaction data faster than organic discovery alone would.

Beyond the first week, the ongoing tasks become drift management, keeping your manifest synced with your live catalog, reliability hardening for promotional spikes, and running the weekly optimization loop. The distinguishing feature of agentic post-launch work is that these tasks never fully stop; the manifest is a living interface that decays without continuous attention.

How is a UCP post-launch checklist different from a normal ecommerce launch checklist?

A normal ecommerce launch checklist optimizes for human perception: page load speed, visual polish, persuasive copy, checkout UX. A UCP post-launch checklist optimizes for machine interpretation: structured attribute completeness, manifest reliability, interpretable error handling, and discoverability by agent crawlers. The audiences overlap but the priorities diverge sharply.

The biggest practical difference is failure tolerance. A human shopper will often forgive and retry an error; an agent frequently abandons your merchant permanently within the same session and completes the purchase elsewhere. That raises the stakes on reliability and error handling far above where they sit in a human-centric launch, which is why we treat structured error payloads and idempotency as launch-critical rather than nice-to-have.

The other difference is measurement. Human launches lean on metrics like bounce rate and conversion rate; agentic launches lean on agent checkout completion rate, no-result query rate, and manifest uptime. These KPIs are diagnostic in a way traditional metrics are not, because a low completion rate against high manifest reads points precisely at a downstream data or checkout problem.

How long does post-launch optimization take before I see results?

In our experience the first meaningful signal arrives within the first week if your human announcement drives early adopters to try agent-based checkout, because those early transactions immediately populate your funnel data. The time to first agent-completed sale is itself a KPI we track, and for well-prepared stores it is measured in hours to days rather than weeks.

Substantive improvement in your agent checkout completion rate typically shows up across the 30/60/90 day horizon. By day 30 you should have a reliable baseline; by day 60, targeted data and reliability fixes should measurably lift completion; by day 90, the metric should stabilize above your target threshold and your no-result query rate should be trending down as catalog completeness improves.

The honest caveat is that organic agent discovery ramps more slowly than paid or owned-channel promotion, so if you rely purely on being crawled without any announcement, expect a longer curve. That is precisely why our checklist pairs discovery work with a coordinated human announcement, to compress the time to your first high-signal data.

Do I need real-time monitoring, or is a daily check enough?

You need real-time monitoring for endpoint health and something close to real-time for your funnel data. A daily check is insufficient for uptime because an agent orchestrator that hits a timeout will simply skip you and route the sale to a competitor, and if your endpoint is down for even a few hours you lose every agent that queried during that window with no chance to recover them. We poll every 60 seconds and alert on two consecutive failures for exactly this reason.

For deeper analysis like the no-result query review and abandonment forensics, a weekly cadence is appropriate in month one, moving to biweekly afterward. That is analytical work best done in batches so you can spot patterns rather than reacting to individual events. The distinction is between health monitoring, which must be continuous, and optimization analysis, which is rhythmic.

Manifest drift sits in between: we diff daily and alert on unexpected divergence, because catalog changes happen constantly but a bad bulk edit needs to surface within hours, not at the next weekly review. Getting this cadence right is one of the clearest differences between a store that quietly decays and one that stays sharp through month three.

Should new merchants prioritize discovery or checkout reliability first?

Checkout reliability first, always. This is a defensible, opinionated stance we hold firmly: driving agent discovery to a store where the checkout does not reliably complete is actively harmful, not neutral. You burn the discovery goodwill, you generate frustrated abandonment signals, and you produce zero usable transaction data. A store that no agent can find yet is simply invisible; a store agents find but cannot buy from is broken in a way that costs you future traffic.

So for new merchants specifically, we prioritize items one and two, monitoring and verified end-to-end checkout, above the discovery work in items three and four. Get the money path bulletproof, confirm it with real test orders across multiple product types and agents, then open the discovery floodgates. The sequence matters more than the individual tasks.

Established merchants with an existing customer base have a bit more latitude because their announcement can drive a controlled initial burst they can watch closely, but even for them we never invert the order. Reliability is the foundation; discovery is the amplifier. Amplifying a broken foundation just breaks it louder.