Leah MessengerMost AI advice tells you what's possible, but not a lot of it tells you what to actually install there and then.
So here's a straight answer for ya: five AI agents, one from each corner of the marketing stack — experimentation, AI search, content management, conversion, and creative. Not necessarily the five that look the coolest in a demo, but the five that change what a team gets done in a week (in a good way, obviously).
Every vendor will hand you a list of ninety things their AI tool can do. That's like looking at a 20 page menu at a restaurant when you're really hungry... it's overwhelming! The question I care most about is which ones actually change how the work gets done.
Tara Corey|SVP, Marketing, Optimizely
FYI for each agent, we've covered who it's for, what you give it to get it up and running, what comes back, and where it might not be the agent for you.*
*(We do have a lot of ready-made agents for marketers though — and you can even build your own with Optimizely Agent Platform... no coding required).
...otherwise known as "what makes our lives easier".
Context-awareness: it understands your brand, goals, and audience
Integration: it works inside the tools you already have
Governance: brand compliance and quality control are built in, not bolted on
Scale: it holds up across complex teams and multiple markets
Measurable impact: it improves performance (and you can actually prove it)
We decided to go with five of the best agents for B2B marketing, to match the five corners of the stack:
What you give it: An experiment with at least one variation already built, then either a selection from its suggested ideas or a plain-language description of the specific change you want to make.
What comes back: A built-out variation. Not a plan for one, not a recommendation, but the actual changes applied to the page as a preview, complete with a before-and-after comparison and reasoning for each change.
Good to know: This experimentation agent works from variations you've already built, which means it needs something to react to (whether that's previous experiments you've done, news ideas you've got, or inspiration from the Experimentation Ideation Agent).
Who's it for: Anyone whose experimentation velocity is capped by build time rather than ideas.
Over the past few months, as tokenomics and AI efficiency became more important, we made a concerted effort to optimize how the agent actually runs. Prompt caching. More apt inference levels. JS validation upgrades. Critical performance work.
The result we're seeing: 43% fewer credits consumed per agent run.
As cost per test fell, velocity climbed across the board. Post-dev agent adoption, newer programs ran 206% more experiments. Mature, long-standing programs still ran 32% more..."— Sathya Narayanan, Senior Director, Product Management, Optimizely
@generative_engine_optimization_auditor
What you give it: A URL (literally, just copy-paste a URL)
What comes back: A detailed audit of how ready that page is to be found, understood, and cited by AI search. It covers crawler accessibility, Core Web Vitals, scheme, content structure, and citation signals.
Good to know: It’s built for single-page depth. Because AI search engines rank and cite specific passages, it focuses entirely on the mechanics of one URL at a time so you know exactly why that page gets cited (or skipped).
Who it's for: Anyone whose CMO has asked why the brand doesn't show up in ChatGPT and who doesn't yet have a real answer (and boy, do I feel that).
We were optimizing for a search engine that was busy being replaced. The audit didn't tell us we were bad at SEO — it told us we were good at the wrong thing."
— Francesco Montesanto, Senior SEO GEO Manager, Optimizely
What you give it: A content type, a parent location in your CMS, and a source — eg. a URL, a document, or an image
What comes back: A populated content item, live in Optimizely CMS, structured to match that content type — not a draft sitting in a chat window you then copy across, but the actual page, already in the CMS where it belongs.
Good to know: It builds directly into the content types you already have in Optimizely CMS. It won't invent unapproved design components, so the finished page matches your exact design system from the second it hits the tree.
Who it's for: Teams whose publishing bottleneck is the mechanical, manual work of getting approved content into the CMS.
✨ Bonus highlight ✨: Talking of building pages with agents... does limitless 1:1 personalization sound like something you'd like? Let's say you have 500 target accounts for your most recent ABM campaign; what would you do if you could also have 500 personalized landing pages in minutes to help? Scream? Yeah, me too. Introducing: 1:1 personalization at scale — for real this time, thanks to a series of AI agents (including this one).
What you give it: A page URL, but you also have the option to add a specific constraint (like a mobile-only assessment, for example) if you want to narrow things down
What comes back: A prioritized list of conversion fixes scored across six dimensions and ordered by estimated impact. It screenshots the page and reads the HTML, so its reasoning is based on what's actually there rather than what you've described.
Good to know: It ranks by potential impact, meaning it’s built to start a test rather than end a debate. The best way to use it is as the fuel for your experimentation backlog, letting real traffic prove the recommendation right.
Who it's for: Anyone staring at a page they know underperforms but without a ranked list of reasons.
It gave me the top three fixes for our most important landing page. We verified the changes through A/B tests and were able to double the conversion rate in paid search."
— Dana Nielson, Senior Manager, Campaign Marketing, Optimizely
@social_media_image_generation
What you give it: The platform, the format, and a description of what you're looking for — go ahead and input a reference image to restyle within your brand guidelines
What comes back: An image at the exact pixel dimensions for that platform, with no resizing or rebuilding the same asset five times across five channels.
Good to know: It takes your brand rules and dimensions literally. Feed it your specs and brand guidelines, and it holds the visual line across every network without drifting off-palette or improvising a new aesthetic halfway through a campaign.
Who it's for: Social and campaign managers losing hours a week to format wrangling, and small teams without design capacity on standby.
(And yes, we used it on this very blog.)
There's a pattern worth naming, because it tends to be the argument that actually lands with a CMO.
Most marketing disciplines are public. How to frame a hypothesis, how schema markup works, what makes a page convert, how to structure a competitive read — it's codified and sitting in the training data. So when you hand an agent a URL and a rough idea, the expertise is already in the room. The agent isn't inventing a method but just applying one the model knows.
That's why a good agent feels disproportionately clever for how little you give it: you're renting expertise that was always there, already existing, and already being used elsewhere.
But if you look (closer) at the five agents above, you'll see two audit, one generates, and two change something in a system you actually own. The distinction matters, because public expertise is the cheap half. The valuable half is an agent with permission to act inside the platform where your work already lives.
That's what an Agent Platform means in practice, and it's the part a collection of disconnected AI features can't replicate.
Context matters, and accessibility to context takes your AI to the next level.
Fun fact: Only 19% of B2B marketers are running a single integrated AI platform — for everyone else, it's a patchwork system. 5% are running more than 7 disconnected tools a week!
Single agents solve single problems, and the interesting part starts when you chain them together.
A workflow agent stitches several agents into one sequence, so the output of the first becomes the input of the next and the whole thing runs as a single job. You build it by dragging agents into order — no developer, no integration work.
The conversion loop is the clearest example: run Page Conversion Optimization on an underperforming page, take its top recommendation into the Variation Development Agent to build the variation, then run Experiment Conflict Checker to confirm it won't collide with something already live. Three agents running as one workflow, taking you from audit to live test.
Or the experimentation loop: Experiment Planning turns a URL and an idea into a full test plan with sample-size guidance, Experiment Backlog Prioritization scores your backlog on PIE so the loudest stakeholder doesn't set the roadmap, and Experiment Value Estimator projects annualized impact in dollars — the agent that turns a budget defense into a budget conversation.
Planning agents that don't cross-check against live tests are a known weak point across this whole category, and chaining is how you close that gap.
We're looking forward to a fully automated end-to-end flow [with Optimizely Agent Platform]: plan in CMP with it, create content with it, test on CMS with it, and have it iterate automatically. Multiple agents running the whole process.
John Habib|Director, Content Strategy, Diligent
Five is a good constraint and sounds great for a listicle, but... it's a slightly dishonest one (we have too many favorites).
📈 If you're reporting monthly: GA4 Web Traffic Report Generation pulls live GA4 data into an executive-ready report with period-over-period metrics and recommendations.
The tool allows every marketer, including those at the highest levels of our company, to ask natural language questions like 'what companies are on our homepage' and generate consistent marketing reports with comments every month. No one wants to vibe-code a new dashboard every month. Instead, our AI is repeatable, scalable, and democratizes access to knowledge.
Michiel Dorjee|Director, AI Innovation & Digital Marketing, Optimizely
✏️ If briefs are on the bottleneck: Campaign Brief Generation produces a standardized brief where each deliverable is listed separately, so each becomes its own task in Optimizely CMP. Creative Brief Generation does the same for creative teams and agencies.
💡 If you're planning content: Content Ideation returns ten ideas built from live trend research and competitor intelligence at once — angles with reasons attached, not a topic list.
🔨 If you're making agents yourselves: Agent Builder lets you build your own agents without a dev ticket — you write the instructions, connect the tools and data, and test it in the same place your team already works.
We built our first agent with Optimizely, which took some time to get right. Now we've got 12 or 13 agents, and I can build a simple one in under an hour.
David McAughtrie|Head of Content Strategy, Cyncly
🌎 If you're working across languages: Content Translation handles cultural and grammatical nuance rather than just swapping words and hoping for the best.
#️⃣ If you're running paid social: LinkedIn InMail Copy Generation writes to LinkedIn's ad specifications, character limits included.
For our demand generation efforts, we created an agent to write LinkedIn InMail copy. We fed it our successful promo emails, and it produced on-brand, compliant, high-converting message ad content — turning a time-intensive creative process into a streamlined workflow.
Dana Nielsen|Senior Manager, Campaign Marketing, Optimizely
And not forgetting the unglamorous ones that people run on the daily:
Meeting Preparation (@meeting_preparation)
Content Summary (@content_summary)
UTM Creation (@utm_creation)
Image Alt Text Generation (@image_alt_text_generation)
Everything above is an agent you run; you pick it, give it an input, and get an output back.
Virtual Teammates go a step further. A Virtual Teammate is a bundle — tools, skills, agents, and workflow agents packaged into a single autonomous AI colleague with a defined job.
A Virtual Teammate doesn't wait to be opened, prompted, or instructed. It runs proactively in the background (or even while you sleep) inside the systems your team already uses — noticing the competitor pricing page changed and starting the analysis, seeing the campaign brief land in CMP and drafting the assets, catching the experiment hitting significance and writing the readout before anyone asks.
A human still signs off (and that part doesn't change... and shouldn't) but the work is already done and waiting when you get there, rather than starting when you remember to ask.
That's the real shift the agents above are building toward: from tools you operate to teammates who show up.
AI in marketing isn't about replacing teams. Nope. Instead, it's about multiplying what they can do.