Can AI agents do marketing?

Yes. AI marketing agents draft copy, run campaigns, personalize experiences, score leads, and analyze results on live customer data. Intempt runs 9 specialist agents on one customer context, so the same lead score, journey, and conversion flow from research to revenue without stitching tools.

Which AI marketing agent is best?

The best AI marketing agent depends on the job. For copy, a design or content agent. For lifecycle, a journey agent. For paid, a performance agent. Intempt ships 9 specialists on one context so you get the right one per job without a tool stack.

How much do AI marketing agents cost?

Intempt starts at $0. The Free plan includes every agent in basic mode plus 100K events per month and weekly AI credits. Paid plans (Professional, Organization, Enterprise) are priced per seat, with a lower rate on annual billing. No per-agent pricing. Current prices are at intempt.com/pricing.

What are the different types of AI marketing agents?

Common types are content agents (drafting copy across channels), lifecycle agents (journeys and email/SMS flows), experimentation agents (A/B tests and personalization), analytics agents (funnels and revenue reporting), SDR agents (outbound outreach), and RevOps agents (scoring and routing). Intempt ships all of these plus 3 more.

What is the best free AI marketing agent platform?

Intempt is free to start with full access to all 9 agents on the Free plan, 100K monthly events, and weekly AI credits that reset every Monday. No credit card, no time limit. Most free AI agent platforms cap you at one agent or workflow.

Are AI marketing agents actually useful, or just hype?

Useful when they work on live customer data with human-in-the-loop approval. Hype when they generate content without context or send without oversight. Intempt agents draft, you approve, then they publish. Every action stages for review, and every send checks consent before it goes.

How do AI marketing agents differ from marketing automation?

Marketing automation executes rules you wrote. AI marketing agents draft the campaign, the segment, the content, and the test before you approve them, and adjust as behavior shifts. Intempt combines both: automation for what is stable, agents for what needs judgment.

Do AI marketing agents replace marketing teams?

No. They replace the tools and the manual execution work, not the strategist. You set the strategy and the guardrails; the agents run the plays inside them. Every draft needs sign-off before it ships. Most teams consolidate 9+ tools and shift day-to-day execution to Blu.

Which AI marketing agents integrate with HubSpot, Shopify, and Salesforce?

Intempt agents connect directly to HubSpot, Shopify, Salesforce, Segment, Stripe, Zoom, and 30+ other tools out of the box. One connection powers every agent - there is no per-agent integration setup, and no re-syncing customer context across separate tools.

Are Intempt agents open source?

Intempt's 9 agents run on frontier LLMs, not on an open-source framework you self-host. Separately, the 27 Claude Skills at intempt.com/skills that pair with the Performance Marketer agent are MIT-licensed and free to use, with or without an Intempt account.

Can AI run A/B tests?

Yes. An AI experimentation agent designs the test (hypothesis, variants, primary metric, guardrail metric, sample size), audits it running (significance, guardrails), and recommends ship or hold when it ends. Intempt's agent runs this cycle on live customer data with human sign-off at each stage.

How does AI experimentation differ from traditional A/B testing?

Traditional A/B testing needs you to write the hypothesis, pick the metric, calculate sample size, run the calculation, and interpret the result. AI experimentation drafts every step - the agent proposes; you approve. Same rigor, less setup time per test, so a team runs more tests per quarter.

Which AI is best for A/B testing?

The best AI for A/B testing enforces the full statistical loop (hypothesis, primary metric, guardrail, sample size, significance) rather than just splitting traffic. Intempt's AI experimentation agent requires all four before a test can run - so you cannot ship an under-powered test by accident.

How does the AI decide sample size?

It calculates minimum detectable effect against your baseline conversion rate, then sizes for statistical power at 80% (or your set threshold). If your traffic cannot deliver the size in a reasonable window, the AI experimentation agent flags it before you launch - not after four weeks of inconclusive data.

Does anything go live without my approval?

No. Every variant and audience stages for review. You approve, then publish. The AI experimentation agent drafts, sizes, and monitors; humans sign off at every gate - launch, mid-flight guardrail breach, and end-of-test ship/hold call. Never auto-shipped.

Can personalization and experiments share the same audience?

Yes. The segment that ran the experiment becomes the personalization audience with one click. No rebuilding, no export, no drift between the test cohort and the live-personalized group. The AI experimentation agent and the customer-journey agent read the same segment.

What data does the AI experimentation agent need?

Tracked conversion events (purchase, signup, activation, whatever the primary metric is), plus a connected experiment audience segment. For AI recommendations, a product catalog uploaded to Intempt. Everything else - variants, allocation, significance - the agent handles.

What tools does it replace: Optimizely, VWO, AB Tasty?

The AI experimentation agent covers the core A/B testing job those tools do, plus the segment layer they need (usually a second tool like Segment or mParticle). Teams typically consolidate the two under one seat when the volume of tests justifies the migration.

How does it handle statistical significance?

Reports significance live with confidence intervals, not a p-value flip at the end. If the test is trending significant but the guardrail metric moves the wrong way, the AI experimentation agent flags the trade-off before you ship. Decisions are ship, hold, or extend - each with the reasoning.

Are the Experimentation Lead skills free?

Yes. The 9 skills are free, MIT licensed, and run in Claude without an Intempt account. Sign up when you want the Experimentation Lead working on your own customer data.

Stop testing on vibes.

9 free Claude skills to plan the work, and the Experimentation Lead to run it on your live data.

Get the Experimentation Lead free

Free plan · No credit card

or install the free skill pack, no account needed

Experimentation LeadOnline · ready to run skills

0.0on G2

10B+events

500M+users

7+years profitable

50+enterprises

The jobs the Experimentation Lead takes off your plate

Hypothesis-driven testing

Every experiment starts with a hypothesis, primary metric, guardrail metric, and sample size calculation.

Visual personalizations

Show different content to different segments based on behavior, traffic source, or lifecycle stage.

Ship decision framework

The Experimentation Lead tells you when results are significant, recommends ship or hold, and explains the trade-offs.

Everything you get, free

Free to start

The Experimentation Lead, on your data

Every test ships with a hypothesis, a primary metric, a guardrail and a sample size. Ships or holds each variant on real data.

Hypothesis-driven testing
Visual personalizations
Ship decision framework

Get the Experimentation Lead free

Free plan · No credit card

Free forever

The Experimentation Lead skill pack · 9 skills

Open source Claude skills from the Intempt team. Install them and run them in Claude today.

Start with these 3

See all 9 in the pack Browse all 88 free skills

npx skills add sidchaudhary/gtm-skills/skills/experimentation-lead

4 steps, prompt to shipped work

You approve at every gate. The agent drafts inside them.

Funnel · Drops rankedICE-scored

Pricing → checkout

-24% · impact 8

biggest

Product → detail

-11% · impact 6

medium

Cart → checkout

-8% · impact 5

small

Detail → cart

-6% · impact 4

small

Step 1

Find the highest-impact drop

Builds your funnel, ranks steps by ICE score, surfaces the biggest opportunity to test.

Test · Specgated

Hypothesis

sticky nav lifts add-to-cart

stated

Primary metric

add_to_cart rate

chosen

Sample size

min 3,200 per arm · 14 days

80% power

Guardrail

session-time > 60s

tracked

Step 2

Design the experiment

Hypothesis, variants, primary metric, guardrail, sample size - defined before a pixel moves.

Run · Dailylive

Day 5

+9% · p=0.12

not sig

Day 9

+7% · p=0.06

Day 12

+8% · p=0.03

significant

Guardrail

session-time steady

Step 3

Run and monitor

Tracks significance and confidence intervals live. No spreadsheet, no manual calculation.

Decision · Shiprecommended

Recommendation

SHIP

confirmed

Lift

+8% add-to-cart · 95% CI

measured

Rollout

100% Monday 06:00

queued

Next test

cart page price anchoring

drafted

Step 4

Ship, then design the next

Recommends ship or kill with the reasoning, then designs the next test from what you learned.

Is the Experimentation Lead right for you?

When to use it

You have traffic and want every test to start with a hypothesis, a metric, a guardrail and a sample size.

When not to

Your traffic is too thin to reach significance. The free skills can still design the test.

The old way, and the Experimentation Lead way

The way it works today

Tests launch on a hunch, with no success metric agreed.

Every visitor sees the same page, whatever brought them there.

Results get called early, or argued about for weeks.

The way it should work

Every test starts with a hypothesis, a primary metric, a guardrail and a sample size.

Different content by behavior, traffic source or lifecycle stage.

Told when a result is significant, with a ship or hold call and the trade-offs.

Hire the role, or deploy the agent?

Same job. Fully loaded headcount against an agent that runs on your customer data around the clock.

	Hiring an Experimentation Lead	The AI Experimentation Agent
Annual cost	$100,000 base + benefits	$0 on Free plan, then a per-seat price on paid plans (see intempt.com/pricing)
Tests designed per quarter	8-12, gated by capacity	As many as your traffic can power
Sample size calculation	Manual, spreadsheet	Auto, before launch, blocks under-powered tests
Guardrail metric tracking	If they remember to define one	Required field, flagged live if breached
Ship or hold decision	Judgement, sometimes debated	Recommends with reasoning; human decides
Personalization from test wins	Rebuild the segment by hand	Same segment reused with one click

US Glassdoor median base salary, 2026. Fully-loaded cost adds 25-35% for benefits, equipment, and payroll tax.

Teams love it

They stopped stitching tools andhired one system.

Acquisition and retention are one connected motion. Personalizing the path before signup only pays off when the same system carries the user through onboarding afterward.

Anonymous traffic was not turning into registered users, and acquisition and retention were run as two separate problems. We ran Experiences on the anonymous visitor flow to put the right content and CTAs in front of each visitor, then handed new signups to automated Journeys for onboarding and deeper engagement.

455,396

users instrumented

Read the full research

Judge experiments on revenue per visitor instead of conversion rate. A variant can win on conversions and still lose on revenue, and repeat buying only shows up when the whole journey is in one view.

High-intent shoppers browsed a large, specialized outdoor catalog, and browsing, product interest and purchase history sat in different places. We unified them into one customer view, then ran real-time personalization, intent-based segmentation and timely follow-ups on top of it, judging experiments on revenue per visitor.

Read the full research

Build the pipeline on observed signals. When follow-ups trigger on intent, the team spends its time on the users who are genuinely engaging.

Static lead lists and timeline-based follow-ups were hiding who was actually engaging. We ran a signal-led outbound pipeline where follow-ups fire on real behavior: outbound replies, onboarding progress and product usage.

Read the full research

See the Marketing team at work

The Experimentation Lead is one of the Marketing team's agents in this tour.

Questions?Answered.

Everything worth asking before you hire the Experimentation Lead.

01 / 11

What is AI experimentation?

AI experimentation is running A/B tests and personalizations where an AI drafts the hypothesis, defines the primary metric and guardrail, calculates sample size, and recommends ship or hold based on significance. Intempt's AI experimentation agent does all four so tests reach a real decision, not a spreadsheet.

Intempt Design