From the original post
10 use cases for JEV in your marketing workflows
your agents write drafts, build briefs and collect research + much more...
give jev that material, your reference files and specific questions to answer
your workflow uses those answers to request changes, organize work or send it for human review
here is how you can apply it:
1. check drafts against your voice and brand rules
give jev the draft, your voice DNA file, anti slop rules and examples of approved and rejected work
ask separate questions about voice, clarity, brand fit and patterns you want to avoid
your workflow can send acceptable drafts for sign-off and return the others to the writing agent
attach the failed criteria so the agent knows what needs to change
2. review ads for stop, scale or hold decisions
give jev performance data from your reporting system, including spend, conversions, cost per acquisition and return on ad spend
include the reporting period, campaign goal and your rules for making changes
ask whether each ad should stop, receive more budget or continue unchanged
send the recommendation and its source data to the media buyer for review
use the same reporting period and metric definitions across the ads you compare
3. decide when an ad needs a refresh
give jev the ad’s performance history and results from other ads in the same campaign
include how often people see it, click rates, conversion rates and recent changes to targeting or budget
ask it to choose an action:
> refresh: keep the concept and create variations
> replace: develop a new concept
> leave: continue without changes
> review: ask a person to inspect the results
your workflow can use the answer to open the appropriate production brief
4. organize competitor ad research
have your research agent collect ad copy, video transcripts, creative descriptions and landing-page content from Meta Ad Library research
give jev defined categories for the hook, format, offer and audience awareness level
ask it to classify each ad and check whether the landing page supports the ad’s promise
save the classifications beside the source links
your briefing agent can then find relevant examples and inspect changes in the offer or message
5. review creative briefs before production
give jev each brief alongside the campaign goal, audience, offer, brand rules and production limits
define separate criteria for:
> hook clarity
> brand fit
> offer clarity
> fit with the campaign goal
> similarity to work already in production
use the scores to rank briefs for review or return them to the briefing agent
attach the criteria that need attention before the team starts filming, designing or editing
6. decide which content ideas to develop
give jev incoming notes, transcripts and research alongside your audience needs, campaign goals and existing content
ask what each item covers, whether it fits an active campaign and whether you have already published the idea
your workflow can then:
> open a brief for useful material
> link a duplicate to the existing content
> save incomplete material for later
> send uncertain items to an editor
7. prioritize SEO work and review internal links
give jev your pages, target queries, current search results and business priorities
ask it to assess how well each page answers the query and where useful information is missing
use those assessments to prioritize pages for an update
for internal links, provide the source page, destination page and proposed link text
ask whether the destination helps the reader answer a relevant question or complete the next step
approved suggestions can enter the implementation queue with both page links attached
8. decide whether a news story deserves a response
give jev the story, source, publication time and context about your brand, audience and active campaigns
ask whether the story has a useful connection to your audience and whether your brand has something relevant to contribute
use the answers to choose:
> research: collect more evidence and prepare a brief
> skip: end the task
> review: ask an editor to decide
9. qualify incoming leads
give jev the form response, your ideal customer criteria and the account information you already have
ask it to assess the fit and select the next step:
> sales: send the lead to the sales team
> self-service: send the relevant product information
> nurture: add the lead to an appropriate follow-up sequence
> review: request missing information or human review
keep the answers to each qualification question beside the lead record so sales can inspect the assessment
10. check completed agent work before sign-off
give jev the finished content alongside the brief, approved claims, offer details, source material and channel requirements
ask a separate question for each requirement
does the content include the required offer details?
are its claims supported by the supplied sources?
does it meet the channel requirements?
your workflow can return failed items to the responsible agent with the requirement attached
passed work moves to sign-off, and uncertain claims go to a person
start with one task and examples your team has already reviewed
compare jev’s answers with those reviews before letting your workflow act on the results
How Jev fits in the loop
- Agent collects runtime context or input state from external sources.
- Bounded decision options are formatted into Jev System One questions.
- Jev scores each candidate option and returns a probability for it.
- Deterministic code executes the selected action or routes to specialized models.
How to reproduce
- Read the author's original post on X at https://x.com/shannholmberg/status/2101967092321026445.
- Inspect the described workflow architecture and bounded decision points.
- Test Jev System One decision queries against the provided task context.
Why this build matters
Demonstrates real-world Jev for SEO experimentation by @shannholmberg with verified community reach of 21.8k views.
Limitations
- Implementation details are based on the author's public post on X.
- Performance figures and benchmarks are author-reported community claims.
- Production deployments may require custom calibration and policy thresholds.