“AI” has become a catch-all term. It’s been applied to everything from predictive lead scoring to grammar-checking bots to conversational avatars. This ambiguity has created confusion, and understandably so. 

In a market saturated with “AI-powered” products, it's difficult to discern what’s truly transformative from what’s simply automated.

What is Agentic AI?

Agentic AI refers to AI that is not just generative, but goal-oriented and action-driven. It doesn’t just create content—it makes decisions, takes initiative, and completes tasks without requiring a prompt. It’s the difference between a system that responds to input and one that acts with context and expertise.

‍An AI Agent is a domain expert capable of doing real work, unprompted.

Agentic AI Has a Brain
What sets a genuine Agentic AI solution apart is a logic layer that allows it to operate autonomously within specific business domains. This means it understands the workflows, outcomes, and edge cases of a particular function—like technical sales—and can execute real, valuable work within that context. 

It doesn’t need to be told what to do. It knows what needs to be done.

‍And, according to Gartner®, because of this, “Agentic AI has the potential to perform as a highly competent teammate.”

While generative AI helps teams be more productive in isolated tasks, Agentic AI is about performance—owning and completing work that moves the business forward. For CROs navigating today’s high-pressure revenue environment, that distinction matters.

AI Agents Will Deliver on the Promises of Generative AI

“Agentic AI’s ability to take action autonomously or semiautonomously has the potential to help CIOs realize their vision for generative AI (GenAI) to increase productivity across the organization.”

Source

Gartner, Inc. Top Strategic Technology Trends for 2025: Agentic AI, Tom Coshow, Arnold Gao, et al. 21 October 2024.

How to Identify Whether AI is Agentic

With the explosion of interest in AI, the term "AI Agent" is being applied to everything from glorified chatbots to simple automation scripts. This has made it increasingly difficult for sales leaders to understand what’s truly transformative, vs. what Gartner® deems “agent-washing.” 

Many so-called agents are merely reactive tools dressed up in the language of autonomy—offering canned responses or waiting for specific prompts to act. But a real agent doesn’t wait to be asked. It operates with foresight, initiative, and domain awareness.

To make smart decisions about AI in sales, leaders must understand the spectrum of AI experiences dominating your inbox:
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TLDR: If it needs to be told what to do, it’s not an agent.

‍True agents do not just automate existing tasks—in the context of sales orgs, they are virtual team members who can partner with reps on important work that needs to be done at every stage of a deal, including: generating solution documentation; mapping stakeholder relationships; analyzing buyer needs; proactively supporting reps during and validation; and more

AI Agents for Sales

Unlike basic AI tools that rely on explicit prompts, Sales AI Agents operate with contextual awareness. They understand the dynamics of your sales process, the intricacies of your product, and the preferences of your buyers. That means they can identify gaps, respond to shifting priorities, and fill in the blanks—often before a human would even notice them.

For example, instead of a rep having to request a competitor comparison or a tailored technical brief, an agent recognizes the opportunity based on deal progression and delivers that collateral automatically. It becomes a kind of always-on teammate—not hovering in the background waiting for instructions, but actively steering the deal forward.

This level of support doesn’t just make the rep more effective. It unlocks capacity. Reps gain back time. Sales engineers focus on higher-order strategy. And leaders get more consistent execution across every team, every region, every vertical.

What Are High-Impact Intervention Points for Agentic AI?

Choosing the right underlying technology for your needs (i.e. AI agent vs. chatbot) is only half the battle. The other major question facing sales leaders is to identify the ideal strategic intervention point for these solutions that will maximize benefits without overwhelming reps. 

Where in your sales cycle are you missing insights?
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But in the middle? There’s a void.

This is the black hole of the sales cycle—where deals are stuck, stalled, or lost. It’s where sellers must respond to buyer objections, validate technical requirements, align stakeholders, and deliver custom materials that reflect the solution fit. Yet, most teams still manage this stage manually—patching together internal Slack threads and syncs with hours of follow-up just to move a deal forward. The result is an enormous drag on sales velocity and an unreliable forecast. Reps feel like they’re operating in the dark. Buyers feel like they’re left hanging.

The Impact of AI Agents on the Messy Middle

This is exactly where AI Sales Agents shine. They monitor buyer interactions, pull from CRM and call intelligence platforms, and generate relevant work products—solution documents, competitive responses, stakeholder maps—without the rep needing to ask. They give every AE the functional power of an always-on technical and deal expert, embedded directly into their workflow.

Agents reduce the cognitive load on sellers and eliminate the administrative tax that slows deals down. Rather than wasting 70% of their time on admin tasks or unqualified deals, sellers can rebalance their workload towards customer-facing interactions.

The result? Hours of reclaimed time.

Imagine your sales team is working 50 qualified opportunities per quarter. Vivun's AI Sales Agent, for example, can save 11.5-20 hours per deal out of the box. Across 50 deals, that's 500-850 hours saved per rep per quarter. That’s the equivalent of 12–21 full work weeks—time that can now be reinvested in building pipeline, advancing deals, and closing revenue. For more information, see the Financial Impact of an AISales Agent.
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Mid-funnel may be where deals are at their riskiest, but it’s also where Agentic AI has the greatest opportunity to drive revenue impact. By replacing manual follow-up with autonomous action, AI Agents ensure that no deal loses momentum simply because the team ran out of bandwidth. ‍‍

A New Sales Velocity Equation

In B2B sales, velocity has always been the aspiration—but it's often constrained by tradeoffs. Move too fast, and you risk cutting corners. Focus too much on quality, and you extend deal cycles. Attempt to scale, and you face ballooning costs. The traditional revenue equation forces you to choose: speed, quality, or efficiency—pick two. Agentic AI changes that.

AI Sales Agents introduce a new paradigm: one where you don’t have to sacrifice quality or incur heavy operational expense to move faster. These agents operate with deep domain expertise and autonomy. They don’t wait for prompts or assignments. They proactively deliver the outputs your team needs—stakeholder maps, solution briefs, technical summaries—precisely when the deal requires them.

Agentic AI increases both speed and quality, without proportionately increasing cost, creating exponential gains in revenue velocity.
Velocity = (Speed × Quality) ÷ Cost

With an AI Sales Agent embedded in the mid-funnel, speed becomes structural—not situational. Reps don’t have to request a document or chase down a Sales Engineer. The work is already done, continuously updated, and surfaced at the moment of need. It’s this kind of intelligent acceleration that compresses deal cycles, boosts rep confidence, and drives forecasting precision.

In a sales environment defined by pressure and unpredictability, Agentic AI is the new lever for velocity. It replaces reactive hustle with proactive momentum. It ensures you move fast—and smart. Because in today’s market, your edge isn’t just what you sell. It’s how fast you help your buyers reach certainty.

How to Evaluate AI Agents

Build vs. Buy?
To achieve these outcomes, you need to either build or buy an agent. Many organizations are attempting to build agentic solutions themselves, but Gartner® cautions that the necessary effort might well outweigh the potential benefits:

‍“It is possible to build AI agents from scratch…however, this requires a high level of expertise and is both challenging and time-consuming for most organizations.”  

Source: Gartner, Innovation Insight for the AI Agent Platform Landscape, 26 March 2025

So, if pre-built is the answer, how do you identify trustworth vendors selling useful agents?

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Step-By-Step Guide to Evaluating AI Sales Tools

1. Clarify the Use Case

Start with the problem you're trying to solve. Is it ramp time? Discovery depth? Technical validation? Be clear on where the friction is, and target that zone.

2. Categorize the Tool

Identify whether the solution is a foundational model, a productivity tool, or a true agent. Only agents bring the proactive, autonomous value required to move the needle on converting pipeline to revenue.

3. Test for Autonomy

Ask: Can this tool take action without being prompted? If it can’t generate work independently, it’s not an agent—it’s an assistant or a chatbot.

4. Assess Output Quality

Review outputs like solution docs, technical briefs, or buyer response drafts. Do they reflect the expertise of your top SEs or reps? If not, the "agent" doesn't have a brain and is likely an LLM-wrapper. Why does it matter? Domain expertise matters. You wouldn't trust a med school student to perform major surgery, even though they have access to all the same medical textbooks as a tenured surgeon.

5. Evaluate Integrations

Determine how well the AI tool connects to your existing stack—CRM, call intelligence, enablement platforms. Seamless integration is a must. Organizations with fully integrated tools and systems are 2.5x more likely to be confident in their ability to manage and capture revenue, according to Conga.

At the same time, the most productive AI sales tools will minimize the interfaces for reps to interact with all of these tools. So an integrated, multi-functional agent will be the easiest for reps to use and manage.

6. Conduct a Security Review

Security remains a primary concern when selecting AI tools. Security experts recommend sticking to fundamentals when evaluating vendors. Determine how your data is used, accessed, and protected.

Here's a basic checklist to avoid major red flags: Can the vendor clearly explain how your data is handled?Do they define what data is retained and for how long? Can they articulate where your data is being stored and shared with third parties?

7. Measure Time-to-Impact

Agents should deliver value immediately, not after weeks of configuration. Set a short evaluation period and watch for signs of time saved and deals accelerated.

8. Forecast ROI

Use metrics like cycle time reduction, AE capacity increase, and SE support load to estimate the return. The right agent pays for itself in velocity and conversion. For more information, see this guide.

Sales AI Evaluation Red Flags

Avoid Agent-Washing
According to Gartner, the market for AI agents is becoming increasingly cluttered—and not all tools labeled as “agents” deserve the title. Gartner warns of a growing trend known as “agent-washing”: the practice of rebranding legacy automation tools, chatbots, and RPA systems as AI agents in an attempt to ride the wave of market hype without delivering true agentic capabilities.

‍"Many vendors are contributing to the hype by engaging in ‘agent washing,’ rebranding existing products, such as AI assistants, RPA tools and chatbots, to capture buyers’ attention without substantial agentic capabilities." —Gartner, 2025

This phenomenon contributes to a fragmented, difficult-to-navigate market—one where sales leaders and AI buyers must tread carefully. While LLM-based agents have gained the most visibility, Gartner emphasizes that not all agent architectures are built the same, and only a small subset of them are currently delivering real business value in production environments.

To protect your team from agent-washing:

• Scrutinize vendor claims. Ask for proof-of-performance and be cautious about free tools, which can put your data at risk.
• Avoid feature bloat disguised as innovation. Look for agents that integrate seamlessly with your tech stack to enrich domain expertise with company-specific context. Look for agents that do multiple "jobs," so as to avoid managing a complex agent infrastructure. Otherwise you're replacing point solution sprawl with "AI-powered" point solution sprawl.
• Favor use-case-driven pilots. Don’t commit to expensive platform contracts until you’ve validated real impact on your team’s workflow and sales velocity.

As Gartner advises, “Begin your AI agent journey by experimenting with prebuilt agents and no-code builders” and “scrutinize vendor claims closely.” Taking this phased approach allows organizations to navigate the fragmented market while avoiding costly detours and shiny object syndrome.

Avoiding AI Tool Sprawl
Avoid the trap of adding tools that create new sources of admin work. If your sellers are spending more time prompting than selling, the ROI is backwards. Instead of driving efficiency, these tools simply transfer the burden from one team to another. That kind of complexity might look sophisticated on a tech stack slide, but it kills momentum in the field.

The true test is how seamlessly the AI tool integrates into the seller's flow of work. Does it anticipate what’s needed next? Does it cut steps, not add them? If it demands more time to operate than it saves, it’s not a solution—it’s a distraction.

Think of Agentic AI not as another app to manage, but as a digital teammate that shows up already briefed, already delivering. That’s the bar. If your tools can’t hit it, your reps won’t either.

To avoid tech stack overload, CROs need to depart from a point solution approach to incorporating AI. Instead, sales leaders should prioritize: proactive outputs over reactive assistance, domain expertise over general-purpose tools, and autonomy over prompt reliance.

Ask:

• Can the AI agent generate value on its own?
• Does it produce high-quality outputs that mirror human expertise?
• Is it integrated across systems or just floating standalone?

The goal is to reduce the cognitive and logistical load on your revenue teams—not redistribute it. 

‍Tools that require constant prompting or supervision may appear helpful at first, but ultimately become a drag on rep productivity. True agents remove the need for reps to stop and think, “What do I need to ask next?” Instead, they proactively deliver insights and content that align with deal progression, technical requirements, and stakeholder context.

It’s also important to evaluate how these tools contribute to organizational learning. Do they capture patterns from successful deals? Can they surface insights that inform coaching, forecasting, or broader go-to-market and lead-to-revenue strategy? 

If your AI investment doesn’t help you become a smarter revenue org, it’s not solving the problem—it’s just another app.

Architecting the RevTech Stack of the Future

So, how can revenue leaders integrate Agentic AI into their existing revenue technology stack? How do you know what tools to replace vs. which to keep? 

As a rule of thumb, the evolution of AI in sales should follow the same trajectory SaaS took over the past decade: platforms over point solutions. 


Just as best-in-class SaaS platforms replaced fragmented tools with unified systems, Agentic AI offers the greatest value when it operates across workflows—not within isolated use cases. The future isn’t more tools—it’s smarter, more connected ones that understand your business and work across the entire revenue engine.

Look for solutions that can work alongside sellers in a meaningful way. For example, valuable capabilities include the ability to:

• Access and interpret CRM, call, and market data
• Understand your product roadmap
• Operate across platforms/major systems of record

Conclusion: The Agentic Operating Model

Sales leaders are under immense pressure—to hit targets, scale teams efficiently, and deliver consistent results in increasingly complex deal environments. Traditional methods can’t keep up, and adding more tools or headcount isn’t a sustainable answer. This is where Agentic AI steps in—not as another point solution, but as a strategic shift in how work gets done across the revenue engine.

The mid-funnel is the ideal point of intervention. It’s where buyers demand answers and reps need support. It’s also where deals stall, forecasts break, and team morale slips. Agentic AI is uniquely suited to this stage because it doesn’t wait for a prompt—it understands what needs to happen, when, and why. It delivers the work products, insights, and execution that accelerate momentum, without sacrificing quality.

But not all AI is created equal. Real AI agents have a brain—they possess domain expertise, operate with contextual awareness, and produce outputs autonomously. If it needs constant prompting, it’s not an agent. If it can’t connect to your systems of record, it’s not equipped to drive outcomes. Sales leaders should evaluate AI not by the promise of automation, but by the ability to act with precision, proactively, and at scale.

This is more than a new toolset. It’s a new mindset. One that gives sellers the expertise they need, when they need it. One that clears the bottlenecks, lifts the burden, and restores momentum. 

This is More Power to Sales. And Agentic AI is how you deliver it.

AI Sales Agent FAQs

What is an AI Sales Agent?

An AI Sales Agent is a proactive, autonomous digital teammate built to do the work of a human in a specific business function—like a Sales Engineer or Sales Rep. Unlike traditional AI assistants that require constant prompting, a true agent uses embedded expertise, memory, and logic to understand context, make decisions, and take meaningful action on behalf of the team.

What's an example of an AI Sales Agent?

Ava by Vivun is the first and only AI Sales Agent with Sales Engineering expertise. She performs tasks like creating stakeholder maps, writing solution documents, and generating sales handoffs—automatically. Ava understands the workflows of technical sales, learns from your CRM and CI tools, and produces deliverables that would otherwise take your team 20+ hours per deal.

What's the difference between Agentic AI and Generative AI?

Generative AI creates content in response to prompts—like drafting emails or summarizing transcripts. Agentic AI takes it further: it acts autonomously, driven by goals rather than instructions. It perceives, reasons, and executes work without needing to be asked—just like your best team members.

What are the best use cases for Agentic AI in sales?

Agentic AI shines in the mid-funnel, where most deals stall. It accelerates technical discovery, automates follow-up materials, validates solutions, and creates consistent handoffs—closing the gaps that slow sales cycles. It’s ideal for companies with high AE:SE ratios or resource-constrained sales teams that still need technical coverage.

What’s the difference between an AI Agent, Assistant, and Avatar?

Agent: Autonomous, proactive, and specialized. Works independently to produce outcomes. Assistant: Helpful, often prompt-based. Enhances productivity, but rarely owns outcomes. Avatar: A digital persona or interface—like a chatbot or visual AI—but often lacks depth or decision-making ability. An Agent can have an Avatar, but not every Avatar is an interface to an Agent.

Is it better to build AI agents from scratch or invest in pre-built solutions?

According to Gartner, most organizations should begin their AI agent journey with prebuilt agents or no-code builders. Building agents from scratch can be time-consuming, technically complex, and resource-intensive—especially without deep domain expertise or infrastructure. While custom-built agents may offer flexibility for unique use cases, prebuilt platforms offer faster time to value, easier integration, and lower skill barriers. In a rapidly evolving market, where the cost of waiting is high, prebuilt agent platforms allow teams to pilot use cases and prove ROI without massive up-front investments. Gartner recommends identifying high-impact use cases, experimenting with prebuilt solutions, and scaling based on demonstrated outcomes.

How do I know if an AI solution is a true agent?

True agents have a “brain”—a logic layer trained on your domain—and the ability to deliver results without being told what to do. Ask: 
Does it require prompting, or act on its own?
Can it deliver completed work products—not just summaries?
Does it understand my sales domain and workflows?

Why should sales teams invest in AI Agents?

Investing in AI Agents means investing in velocity, coverage, and consistency across the sales cycle. Agents eliminate the manual busywork that bogs down AEs and SEs, generating technical assets, follow-up documentation, and deal intelligence in real time. 

This allows sales teams to focus on what they do best—building relationships and closing business. The cost of not investing? Slower cycles, higher operational costs, and lost opportunities that could have been accelerated with intelligent support. In a world where every deal counts, AI Agents don’t just improve productivity—they protect pipeline and prevent bottlenecks.

How should I evaluate vendors claiming to offer AI Agents?

Gartner recommends scrutiny. Look for signs of “agent-washing”—where vendors rebrand assistants, chatbots, or RPAs as agents without real autonomy or intelligence.
Ask vendors for: Examples of autonomous action, not just reactive features; Demonstrated use cases with tangible business impact; Integration across your tech stack; A clear logic layer (e.g., knowledge graphs) powering decisions--not just LLM-based information retrieval; Clear insights about where and how your data is used, stored, protected, and shared with third and fourth parties