Evaluating AI Agents in 2026: What Buyers Must Know

The Agentic AI category didn’t exist on G2 a year ago.

In 2026, it has its own Best Software Awards list.

The Agentic AI market is projected to surpass $47 billion by 2030. That velocity is telling. And the data suggests it’s grounded in real deployment, not just hype.

According to G2’s 2025 AI Agents Insights Report, based on a survey of over 1,000 B2B decision-makers, 57% of companies already have AI agents in production, and that number is likely higher by now. Additionally, more than half said they were highly likely to expand scope or budgets over the next 12 months. It’s clear that agents are moving — and in some cases, have already moved — from experiment to operating infrastructure.

G2’s introduction of a dedicated Best Agentic AI Software list this year is itself a signal. It reflects a category that has moved from conference-stage demos to production-grade deployments fast enough to generate the review volume needed for an awards ranking. The winners on this inaugural list — led by Salesforce Agentforce — signal where the complexity is being resolved and where buyers must pay closer attention.

Agents are becoming infrastructure, not standalone apps

The first wave of agentic AI was about capability. Buyers on G2 used to ask, “Can an agent complete a task?”

The current wave is about implementation and scaling, where they ask, “Can it complete a task within a real workflow, using real data, and under real governance?”

This shift is visible in how leading products are created.

Salesforce, the company whose product ranked #1 on the list, has built Agentforce not as an isolated tool but as what the company calls a “system of agency.” It operates across marketing, sales, service, and field operations, connected to a unified data layer.

Leandro Perez, Chief Marketing Officer for Australia and New Zealand at Salesforce, describes this through a live deployment with the PepsiCo demo. “We’re presenting them as an agentic enterprise,” he explains.

A prospect lands on PepsiCo’s website and engages a marketing agent. When they drop off, a sales agent re-engages them via email. If an equipment issue arises after the deal has closed, a customer service agent handles the request and dispatches a field technician, who arrives with an AI-generated inspection briefing.

This wall-to-wall deployment pattern is the direction the category is moving. Buyers evaluating agentic AI in 2026 are looking for solutions that operate across roles, not point solutions that automate a single task.

From production to scale: Where deployments stall

Scaling is a different challenge from deploying.

G2’s report found that 83% of buyers are satisfied with agent performance and that the median time to a first meaningful outcome is six months or less.

Yet moving from a successful deployment to company-wide use requires you to tackle secure data readiness, governance, and organizational change.

Leandro has observed this pattern across Salesforce’s nearly 30,000 live Agentforce customers.

He identified four traps that stall the adoption of AI agents:

Taking a do-it-yourself approach.

“The current narrative in the media is that you can build everything yourself, and you don’t need software anymore. And that couldn’t be further from the truth,” he says.

Trying to automate everything at once.

“It’s like assuming you never had the internet and then now having the internet and saying everything has to be digital.”

Neglecting data readiness.

“The agent is only as good as the data. If you don’t have that paired with the data, then the agent is kind of just really smart, but doesn’t actually have the context to be personalized.”

Deploying an agent without ongoing management.

“Think of agents much like you would treat an employee. You don’t just hire someone and leave them in the corner.”

Leandro Perez
CMO for Australia and New Zealand, Salesforce

This framing of agents as digital labor, where they are onboarded, monitored, and managed like employees, is emerging as a core operating principle for successful deployments.

From raw capability to trust and simplicity

Bijou Barry, Research Principal at G2, confirms that the category’s center of gravity is shifting from capability to reliability. “What started as a race to build capable agents is becoming something more interesting: Agents are becoming a capability layer within the stack, not a product category unto themselves,” she observes.

“The question is no longer ‘can AI Agents act?’ — it’s ‘how well, how fast, and with whom?”

Bijou Barry
Research Principal, G2

She highlights that multi-agent coordination at scale remains brittle, that the trust and security architecture for autonomous action is immature, and that evaluation infrastructure is still being built. “The vendors who survive the next phase won’t just be the ones who built the most capable agents,” Bijou notes. “They’ll be the ones who made agents fast, trustworthy, and composable enough to work together.”

This maps directly to what buyers on G2 are validating with their reviews. The products earning top marks are not necessarily the most feature-rich. But they deliver automation accuracy, integration with existing systems, and real-time recommendations without adding complexity.

Dharamveer Prasad, an Application Security Engineer at Cybersmithsecure and Agentforce user, captures this from a practitioner’s perspective. “An agentic AI solution should not only generate insights but also help teams take action faster without adding complexity to their workflow,” says Dharamveer, part of the G2 Icon community. Icons are experienced professionals passionate about providing software feedback.

AI agents are transforming industries and work. Learn how companies are using the agents in our in-depth article.

How should buyers choose Agentic AI software?

Generative AI is non-deterministic. This means agents built on it will produce different outputs for similar inputs.

Leandro explains how the Agentic AI category addresses this: Agentforce now allows users to blend deterministic steps, where a process must follow a defined sequence with generative flexibility. “If the customer is taking a step, you can tune the agent to ask a certain question,” he explains. “You give it some guidance, so it’s not so freeform.”

This deterministic-nondeterministic blend is becoming a differentiator across the Agentic AI category. Buyers evaluating agentic AI should probe for it.

Leandro also points to two capability shifts that will reshape evaluation criteria in the near term.

The first is voice. He describes a deployment with jewellery brand Pandora where customers can call, interrupt the agent naturally, and complete tasks like booking appointments or requesting exchanges, all by voice over the phone.

The second is agent orchestration, which is stitching multiple agents together to execute multi-step workflows.

The next frontier involves agents handing off to each other, explains Leandro. “You might need those agents to talk to each other, obviously with a human supervising that.”

For buyers, this means evaluating not just what an agent can do today but whether the platform’s architecture supports coordinated multi-agent workflows tomorrow.

Bijou frames this from a market perspective. “As agent capabilities become table stakes, the differentiation will move to performance, speed, and multi-agent coordination,” she says. “A single agent completing a task is impressive. A mesh of specialized agents completing a complex workflow reliably, quickly, and with a human on the loop rather than in the loop is a different value proposition entirely.”

Based on the patterns across this year’s winners and the category trajectory, here is what should be on every buyer’s evaluation checklist:

Data integration depth: Does the agent connect to your existing CRM, ERP, and operational systems? Agents without context are just expensive chatbots.

Governance and auditability: Can you monitor what the agent is doing, review its conversations, and set guardrails on what it can and cannot act on? G2’s research found that agent programs with a human in the loop were twice as likely to deliver cost savings of 75% or more. Governance isn’t a constraint on speed; it’s a multiplier of outcomes.

Orchestration readiness: Can the agent work with other agents? G2’s report found that 50% of companies already have agents handing off work across different vendors and platforms.

Human-in-the-loop design: Buyers must pick agents that don’t replace human judgment but augment it. As Leandro puts it: “We believe in human and AI working together.”

Speed-to-value over customization depth: Look for plug-and-play solutions over bespoke implementations.

The market has shifted from “build” to “buy”. G2’s report found in-house builds ranked last in satisfaction, time-to-value, and ease of use.

The category is young. The opportunity is now.

Agentic AI is among the fastest-growing new categories on G2, and its first Best Software Awards list reflects a market where real use has outpaced early skepticism. With 57% of companies already in production, 83% reporting satisfaction, and the median time to meaningful outcomes at six months or less, the technology is proving itself.

The winners signal a clear direction. Agents are embedded in workflows, connected to trusted data, governed transparently, and designed to work alongside humans. Buyers who evaluate with these principles will be best positioned to extract real operational value from this pivotal category shift in enterprise software this decade.

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Explore the full list of G2’s 2026 Best Agentic AI Software winners.

FAQs

What is agentic AI software?

Agentic AI software consists of AI systems that autonomously run tasks, coordinate across business systems, and make decisions within defined guardrails, unlike traditional chatbots, which only respond to queries. These agents operate across functions like customer service, sales, marketing, and field operations, often connected to a company’s CRM and data infrastructure.

How should companies evaluate Agentic AI tools in 2026?

Companies should evaluate agentic AI across five key criteria: data integration depth, governance and auditability, orchestration readiness, human-in-the-loop design, and speed to value. G2’s research found that agent programs maintaining human oversight were twice as likely to achieve cost savings of 75% or more compared to fully autonomous setups.

What are the biggest mistakes to avoid when deploying AI agents?

The four most common deployment mistakes are: building in-house instead of using proven platforms, automating too many processes simultaneously, neglecting data readiness, and failing to manage agents after deployment.