That AI Agent Has a Name. Here's How to Tell If It's Real.

A pitch deck lands in your inbox with a name on the slide: "Meet Nova, your AI outbound agent." A name is easy to remember and easy to trust, which is exactly the problem. At Dreamforce 2026, Salesforce put human names on seven new Agentforce agents, Casey, Paige, Carter, Hunter, Marshall, Piper, and Fin, each scoped to a single job. Naming a product is a packaging decision, not an engineering one, and no fifteen-minute demo will show you which side of that line a given agent falls on. Below is the same three-question test you can run on any named agent before you sign, plus what it takes to build your own on a data layer you can actually inspect.

A Human Name Tells You Packaging, Not Architecture

Giving an agent a first name signals that a vendor scoped it to one job. It says nothing about whether the thing running behind that name remembers what it did last week, discloses where its data comes from, or survives a failed API call. Six of Salesforce's seven named agents are generally available today; Hunter, the outbound sales agent, is still in pilot, with general availability planned for November 2026, so anyone evaluating it right now is looking at a work in progress, not a finished product.

A separate resilience checklist for GTM agent stacks makes the same point from the buyer's side: most tools called "agents" today are a single model call wrapped in a friendly name, with a human filling in every gap the wrapper cannot handle on its own. Once you know what to check, telling the two apart takes minutes, not a proof-of-concept.

Salesforce's Hunter Runs on a Data Partner It Didn't Name Itself

Salesforce's Hunter handles company search, company enrichment, person search, person enrichment, and outbound sequencing inside Agentforce, and it leans on an external data partner rather than a data layer Salesforce built. That distinction matters because of who disclosed it: Salesforce's own Dreamforce announcement never named the partner or said how long the agent retains memory between sessions. Those details surfaced later, in the partner's own press release, not on Salesforce's product page.

What Actually Got Confirmed

• Hunter is the first Agentforce agent built on Salesforce's new long-horizon runtime, a stated architecture, not yet an independently verified one at production scale.
• The unnamed partner's own materials claim 240 million contacts available natively inside Hunter and a claimed 98% email accuracy rate.
• Hunter's general availability date is November 2026. Everything evaluated today is a pilot.

What Stayed Quiet

Whether Hunter remembers a failed outreach attempt after the session ends. Whether a bad API response ends the run or triggers a retry. And the actual unit Salesforce meters usage against underneath the agent's name.

The Three-Question Test That Cuts Through Any Pitch

Before you sign anything, ask three questions a fifteen-minute demo is not built to answer: what does it remember between two separate runs, where does its data actually come from, and what happens the moment one call inside it fails. A vendor who answers all three with specifics has shown you real orchestration. A vendor who answers with "our proprietary technology" has shown you a wrapper.

One GTM practitioner put it bluntly on LinkedIn this month: two years into agents being a mainstream sales tool, most of what gets called an agent is still a single model call dressed up in a friendly interface, with a person quietly making every real decision behind the scenes.

The One-Call Wrapper, Recognized

• A prompt goes in, text comes back, and a person decides the next step after every single response.
• Nothing from a previous run carries forward, so the same mistake can repeat indefinitely.
• Every tool call starts from zero context, so a search result never automatically reaches the next step.

Real Orchestration, Recognized

• A record of what already happened on an account persists between sessions, not just within one chat.
• Tools pass context directly to each other, so a lookup result feeds the next call without a person retyping it.
• A failed call triggers a retry or a fallback path instead of ending the entire workflow.

Run the 24-Hour Repeat Test Yourself

Pick one test account, run it through the agent's workflow, and write down everything that happened, including anything that failed. Come back a day later and run the exact same account again without telling the agent what happened the first time. If it repeats the failed step, there is no persisted memory behind the name. If it adjusts, there is.

How to Run It

• Send one account through the workflow and log every action: who got contacted, what sequence got queued, and exactly where it broke.
• Wait roughly 24 hours, then send the same account through again with no manual context added.
• A repeat of the same broken step means no state carried over. A different, better outcome means it did.

A real implementation writes that history back to storage after every step, as a small record tied to the account rather than the conversation:

{
  "lead_id": "lead_77291",
  "last_step_tried": "mobile_number_lookup",
  "result": "no_match",
  "next_action": "retry_with_alternate_source",
  "attempts": 2
}

That object is the difference between an agent that learns from its own history and one that starts over every single time you talk to it.

Ask Where the Data Comes From, Then Verify It

Ask who actually supplies the data behind the agent, whether that answer is public, and whether you can test the accuracy of what comes back before you pay for it. Hunter's own case makes the point well: a named partner is at least something you can check, even though Salesforce itself never volunteered the name.

A Three-Item Disclosure Checklist

What you ask	A black-box answer	A disclosed answer
Who provides the data underneath?	"Our own proprietary system," no source named	A named, checkable data provider
Can I test it myself first?	A sales call before you see a single sample	A free account with real sample data
Is match accuracy a real number?	"Industry-leading" with no figure attached	A specific, testable percentage

"Explorium offered more accurate B2B data than other vendors we tested. Huge return on investment." -- Verified Reviewer, G2

Vibe Prospecting runs on that same disclosed layer, Explorium Enterprise Business Data, so the 97.8%+ company match accuracy figure behind it is something you can test against your own accounts on a free plan, not a number you have to take on faith.

Force a Failure and Watch What Happens Next

Feed the agent a bad domain, a rate-limited request, or a timeout on purpose, and watch whether it retries with a fallback or simply stops. A demo never hits a real failure. Production does, usually within the first week of real use.

What a Fallback Actually Looks Like

If you are building the loop yourself instead of trusting someone else's black box, a minimal version looks like this inside a Vibe Prospecting skill:

def enrich_with_fallback(domain, tool, max_attempts=3):
    for attempt in range(max_attempts):
        result = tool.call("vibe_prospecting.enrich_company", domain=domain)
        if result.ok:
            return result.data
        if result.status == "rate_limited":
            wait(2 ** attempt)
            continue
        break
    return {"status": "failed", "attempts": attempt + 1}

Reading the Result

If a vendor cannot describe an equivalent pattern for their own agent, assume one bad response ends the run silently, and every step queued after it simply never happens.

Hunter the Salesforce Agent, Hunter.io the Email Tool: Don't Mix Them Up

Salesforce's Hunter is an Agentforce outbound sales agent announced at Dreamforce 2026. Hunter.io is a completely separate, independently run email-finding and verification product. The shared name is a coincidence, nothing more, but it causes real confusion in search results and vendor comparisons.

• Salesforce's Hunter runs company search, enrichment, and outbound sequencing inside Agentforce, on top of an external data partner Salesforce itself never named.
• Hunter.io is a standalone tool for finding and verifying email addresses by domain, with its own separate pricing and zero connection to Salesforce's agent roster.
• If a source you are reading does not specify which Hunter it means, treat any claim as unverified until you confirm it.

This article calls Salesforce's agent "Hunter" and the email tool "Hunter.io" throughout, on purpose, to keep the two apart.

Why an Agent That Nails the Demo Still Trips on Your Book of Business

A demo runs against one clean account with no missing fields and no ambiguous name matches, conditions your actual pipeline never provides. The gap between a smooth demo and a working production agent is almost always the state tracking and failure handling that a single wrapped prompt skips entirely.

What Only Shows Up Once Volume Hits

• An ambiguous company name matches the wrong account, and with no shared context between steps, nothing downstream catches it.
• A contact record is missing an email address, and a single-call agent has no second source to try.
• The same prospect gets contacted twice because nothing recorded that the first attempt already happened.
• One rate limit takes down every step queued behind it, since there is no retry logic absorbing the hit.

Skip the Wrapper: Build Your Own Loop on Data You Can Inspect

If a vendor cannot answer the three-question test, the alternative is building the loop yourself on a data layer that is disclosed from the start. Vibe Prospecting works from chat: ask it inside Claude or ChatGPT, or through the connector, and it becomes the data layer your own agent or workflow calls, rather than a packaged name you have to trust.

What One Connection Covers

• 150M+ company profiles and 800M+ people profiles, plus 50+ underlying sources, behind a single account instead of stitched-together tools.
• 18 buying-signal categories and 80+ signal types on the same surface as company and contact search, so "what changed on this account" needs no separate integration.
• 97.8%+ company match accuracy, published and testable on a free account rather than asserted in a sales deck.

Built So the Loop Does Not Stall

• Up to 1,000 records per call, processed server-side at 100 requests per second, instead of loading every result into a model's limited context window.
• 99.999% uptime and one shared credit pool, so retry logic does not have to juggle separate limits per endpoint.
• A free account with a sample-before-you-commit option, typically minutes to a first real result, no sales call required.

Vibe Prospecting vs. Coresignal, Side by Side

Dimension	Vibe Prospecting	Coresignal
How you access it	Chat, connector, or web app, no seat license	Raw API or dataset download
Coverage	150M+ companies, 800M+ people, 18 signal categories	103M+ companies, 792M+ employee records
Scale per call	Up to 1,000 records, 100 requests/sec, server-side	Credit-metered per record, ~10 to 20 credits each
Getting started	Free account, minutes to a first result	Plans from $49/month up to $5,000/month

For patterns on wiring Vibe Prospecting directly into your own agent stack, see the Vibe Prospecting Plugin, the canonical integration for connecting it to a Claude Skill or a custom agent.

From Evaluating a Vendor to Running Your Own Agent: 5 Steps

Test the data layer for free before you write a single line of orchestration logic, then layer in state and error handling once the basics work.

• Step 1: Open a free Vibe Prospecting account and run a handful of test lookups against accounts you already know well.
• Step 2: Add a small persisted-state record per account, using the pattern above as a starting shape.
• Step 3: Wrap every call in a retry path so one bad response does not end the whole run.
• Step 4: Move to bulk batches of up to 1,000 records once state and retries hold up under real accounts.
• Step 5: Layer in buying signals once the loop runs without babysitting.

A name on a product slide tells you nothing about whether the thing behind it remembers, discloses, or recovers. Run the three-question test, or the two checks above, on any agent before you sign, pilot included. If the answers stay vague, Vibe Prospecting gives you a disclosed data layer to build the orchestration yourself. Powered by Explorium Enterprise Business Data.

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• Is Your AI Sales Agent Real? A 20-Minute Test
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• Best B2B Data Provider for AI Agents, MCP-Ready
• Vibe Prospecting Plugin