An AI sales agent is software that uses a large language model to run sales tasks that used to need a person's judgment: researching a prospect, qualifying the lead, writing the message, booking the meeting and deciding the next step. Rule-based automation follows a script. An agent reads the context and picks the next step, inside limits a person sets. Its output is only as good as the data it works from.
AI sales agents by type, with prices
Ten real products, grouped by the job they do. Every price was read on the vendor's own pricing page on October 30, 2026.
| Type | Product | What the agent does | Price (vendor pricing page, checked October 30, 2026) |
|---|---|---|---|
| Research | Clay (Claygent) | Researches each company or person and writes the answer into a table column | Free plan; Launch $167 a month billed annually, $185 monthly |
| Research | HubSpot Prospecting Agent | Watches buying signals, researches accounts, finds contacts and drafts emails in each rep's voice inside HubSpot | $1 a lead it recommends outreach for, on HubSpot Starter, Professional or Enterprise |
| Qualification | Clu (ours) | Agents find people who match your ICP, get verified emails and phone numbers, score each lead from 0 to 100 and add it to a sequence | Agents from Pro at $89 a seat a month ($67 billed annually); Starter $49 a seat |
| Qualification, inbound | Breakout | Answers website visitors, qualifies them against your ICP and books the meeting | Growth, with the Inbound AI SDR, from $2,000 a month |
| Qualification, voice | Aircall AI Voice Agent | Takes and makes calls, qualifies the lead and books the meeting | $0.49 a minute pay as you go up to 2,500 minutes, then $0.39; $100 a month minimum |
| Outreach | Artisan (Ava) | Finds leads in 250M+ B2B contacts, writes and sends from your domains, answers replies and books | Free plan; Intern $250 a month billed annually ($280 monthly), about 500 leads a month |
| Outreach | AiSDR | Email and LinkedIn outreach with replies handled by the agent | $1.50 a person contacted, pay as you go |
| Outreach | Salesforge (Agent Frank) | Email and LinkedIn outreach in Auto-Pilot or Co-Pilot mode, books with your calendar link | $499 a month billed annually, $599 billed quarterly, after a demo |
| Full cycle | 11x (Alice) | Builds lists and runs sequences across email, LinkedIn and calls | Growth from $3,750 a month billed annually, though 11x's FAQ says $36,000 a year |
| Full cycle, in the CRM | Salesforce Agentforce | Agents that work inbound leads and write outreach from your Salesforce data | $2 a conversation, or Flex Credits at $500 per 100,000 (20 credits an action) |
Disclosure. We make Clu, and this entry is our own description of our product.
The cheapest way to try an agent is a usage price: AiSDR at $1.50 a person, HubSpot at $1 a lead or Aircall at $0.49 a minute. Outbound agents on a subscription run from $250 a month (Artisan Intern, billed annually) to $3,750 a month (11x Alice Growth, billed annually). For the outbound tools in depth, see the AI SDR tools comparison. For inbound qualification, meeting booking and account research, see the AI BDR guide.
What makes software an AI sales agent?
The word "agent" now sits on most sales software. A simple test separates the real ones: who decides the next step. Sales automation sits on a spectrum:
- Rule-based automation. If the title contains "VP," add the lead to sequence A. No reasoning.
- Template workflows. Pull data from the CRM, merge it into a template, send on a schedule. Faster than manual work, and rigid.
- AI-assisted tools. An LLM drafts the email or summarizes a profile. A person still runs every step.
- AI agents. The software researches the prospect, decides whether they fit your ICP, writes a message from what it found and picks the channel and timing. A person reviews and approves.
Most tools sold as "AI agents" sit in the second or third group. They use AI for one step, usually the copy, and a person still runs the workflow. A real agent runs the loop: read the data, reason about it, pick an action and take it. Good ones keep a person in the loop for oversight, with approvals on anything that leaves your domain.
For the first two groups, see our sales automation software list, and for the third, our ranked AI sales assistants.
The 4 types of AI sales agents
The table above groups products by type. Here is what each type does and where it stops.
Research agents
Research agents gather and structure what is public about a prospect or a company: the website, news, job posts, funding and leadership changes. The output is a brief a rep would spend 10 to 15 minutes writing by hand.
Clay is the most prominent example here. Its workflow builder chains data sources and AI research steps into custom enrichment and research pipelines, and it rewards teams willing to invest in configuration.
Cirrus Insight takes a Salesforce-first approach. Its Meeting AI researches prospects and customers before a meeting, and its CRM AI queues up opportunity updates that a rep approves in Salesforce with one tap.
The limit: a research agent collects information and stops there. A person, or another agent, decides what the research means. Clu's account research agent writes a sourced brief on each new target account every Monday.
Qualification agents
Qualification agents score and rank leads from several signals at once: job title, company size, hiring, funding and engagement. A rule-based score adds points per field. A qualification agent weighs the fields together, so a VP of Sales at a 200-person SaaS company that just raised a Series B and is hiring SDRs scores higher than the same title at a stable 200-person manufacturer.
Clu's agents work in this space. They research each lead and score it from 0 to 100 against your ideal customer profile, with the reasons written down.
A Clu agent qualifying leads with reasoning, recorded with voiceover
On the inbound side, 1mind builds AI agents it calls Superhumans. They talk with website visitors, answer product questions, qualify buyers and give demos, and they can join live calls to answer technical questions.
Outreach agents
Outreach agents write and send messages from the research and the score. The good ones write from a specific fact, such as a job post, a product launch or a talk the prospect gave, instead of filling in merge tags.
Unify is an example of this approach. Its platform combines data enrichment with AI-driven outreach and aims to automate the full send workflow.
The risk is quality control. An agent that sends 500 personalized emails a day can send 500 embarrassing ones if the data or the reasoning is off. Keep a review step before anything sends, at least for the first few hundred messages.
Full-cycle agents
Full-cycle agents combine research, qualification and outreach in one workflow. You set the ICP and the agent runs everything from finding prospects to the first message.
Cargo is building in this direction, combining data orchestration with AI-powered sequencing. Clu's prospecting automation agent runs the weekly version: new ICP leads found, verified and added to a sequence.
This type is still early. Errors compound across steps: if research gets a detail wrong, qualification scores on bad data and the outreach agent sends a confident message built on it. Teams that get results from full-cycle agents keep tight human oversight and clean input data.
How do AI sales agents work?
Every AI sales agent has three layers:
- The model. A foundation model such as GPT or Claude reads the data, writes the text, classifies leads and picks the next step.
- The data layer. CRM records, enrichment providers, web research and signals: everything the agent knows about a prospect.
- The action layer. How the agent acts: sending email, updating CRM fields, adding people to sequences or creating tasks for a person.
The model gets the attention, and the data layer decides the outcome. An agent with a strong model and stale data writes confident, well-built messages to people who changed jobs, ranks leads at companies that were acquired and sends to addresses that bounce. B2B contact data goes stale as people move (our B2B data decay research has the numbers), so the first question about any agent is where its data comes from and how it is verified.
The data layer is the foundation
A sequence with bad data sends a wrong email. An agent with bad data sends a wrong email that sounds personal, which is harder to catch and worse when it reaches the prospect.
Ask each vendor three things about data. Where do contact details come from, one source or several in sequence? How fresh are titles and companies? Is every email verified before the agent sends to it?
How to choose an AI sales agent: 7 criteria
Score each tool from 1 to 5 on these seven, weighted for your team.
| # | Criterion | What to ask | Why it matters |
|---|---|---|---|
| 1 | Data quality | Single source or several? How fresh? Verified before send? | Stale data turns into wrong research, wrong scores and bounced email |
| 2 | Reasoning you can see | Can you see why the agent made each decision? | You cannot fix a decision you cannot trace |
| 3 | Channels | Email only, or LinkedIn and phone too? | Buyers answer on different channels |
| 4 | Personalization depth | Merge tags, or a message built on research? | Prospects spot template personalization fast |
| 5 | Human in the loop | Can you approve before send and override decisions? | Few teams are ready for zero review |
| 6 | Integration depth | Does it read from and write to your CRM and sequencer? | An agent outside your stack creates data islands |
| 7 | Measurement | Does it report replies, meetings and pipeline, or only activity? | 1,000 emails and zero meetings is a failed run |
A team with clean, enriched CRM data can weight data quality lower. A team starting from scratch should weight it highest. Either way, run the pilot on your own records. Demo environments use clean, curated data, and your CRM does not.
Common mistakes when deploying an AI sales agent
- Pointing the agent at a dirty CRM. Outdated records, mixed-up titles and unverified emails turn into confident outreach to people who left. Enrich and verify the list first.
- Measuring activity. Thousands of emails and hundreds of scores mean nothing without pipeline. Track replies, meetings booked and opportunities. If 2,000 emails book 3 meetings, look at targeting and data first.
- Removing review too early. Start with review on every message, move to spot-checking 20%, and cut further only when the error rate is proven low.
- Skipping deliverability. An agent scales send volume faster than any rep. Warm separate sending domains, set up SPF, DKIM and DMARC, verify every address before send and watch bounce rates daily.
For the wider set of AI tools a sales team buys, by job, see the AI sales tools guide. For who builds and runs these agents inside a company, see GTM engineering.
Frequently asked questions
Are AI sales agents replacing SDRs?
They are replacing parts of the job: list building, research, first drafts and follow-up timing. Live calls, objection handling and choosing the real buyer at an account still need a person. Most teams that get results run agents next to SDRs and point the SDRs at replies, calls and top accounts.
What data does an AI sales agent need?
At minimum: verified work emails, current job titles, company name and size, and industry. Direct phone numbers, funding history and recent company news improve research and qualification. The more complete and current the inputs, the better every step after them.
How do I measure the ROI of an AI sales agent?
Track three numbers against your pre-agent baseline: meetings booked a month, cost per meeting (the agent plus data costs, divided by meetings) and rep hours saved on research and manual outreach. Emails sent and leads scored are inputs, so leave them out of the ROI math.
What is the difference between an AI sales agent and a sequence?
A sequence sends a fixed series of steps: email on day 1, follow-up on day 3, call on day 5. An agent watches signals and changes the plan: it can speed up a follow-up after a site visit, pause when a prospect changes jobs or rewrite the message when research turns up something new. Sequences are easy to predict and debug, and agents need more trust and better data.
Can I build my own AI sales agent?
Yes. Write the job as a trigger, steps, limits and an output, then build it in code with a model API and data APIs, in a workflow tool, or with an agent builder that takes the job in plain English. Our GTM engineering guide covers who builds these and how, and the AI SDR tools comparison covers the ready-made ones.
Omni
Decagon
Distyl
WellSaid