G2’s Best Software Awards were released this month, and its new Best Agentic AI Software Products for 2026 list suggests buyers now expect more than deflection, they want resolution.
In G2’s top 10 Agentic AI products, six are CX or conversation-first tools: Fin by Intercom at number two, Zendesk for Customer Service at number three, Retell AI at number four, Qualified at number seven, Genesys Cloud CX at number eight, and JustCall at number nine.
The same pattern runs further down the list, with Tidio at number 14, Talkdesk at number 20, Freshdesk at number 34, LivePerson at number 39, Gladly at number 41, Ada at number 46, and Forethought at number 50.
What The G2 Rankings Do, And Do Not, Prove
G2 ranks products using normalized Satisfaction and Market Presence scores, built from verified user reviews and publicly available market presence data. Eligibility depends on review volume, and G2 notes that only reviews within its evaluation window count toward ranking.
That makes the list useful as a signal of buyer sentiment and momentum. It does not, by itself, prove market share, revenue leadership, or absolute technical performance across vendors.
Deflection Was The Chatbot Era, Resolution Is The Agent Era
Chatbots trained teams to ask one question: how many contacts did we avoid. That was a sensible goal, and it still matters when volume spikes.
Agentic support changes the question to: how many issues did we finish. That shift demands better knowledge, tighter workflows, and stronger governance, because the system can take actions, not only respond.
A Staged Model Of Adoption: Assist To End-To-End Resolution
A practical way to read the current momentum is as a maturity curve. It starts where the workflow is repeatable and the outcomes are measurable, which is why CX is seeing early confidence.
Stage One: Assist (Agent As Copilot)
At this stage, the agent supports humans. It drafts replies, summarizes context, and suggests next steps, while a person remains responsible for the final response.
A common early signal of value is operational relief. On G2’s agentic AI list, one Fin by Intercom reviewer describes it as:
“A Reliable AI Teammate That Takes the Pressure Off Customer Support.”
Stage Two: Triage (Agent As Router)
Triage is where the agent starts making decisions about classification, priority, and routing. That can reduce misroutes and shorten queues, but it also exposes gaps in taxonomy and reporting.
Review language often gravitates toward automation plus measurement here. A Zendesk for Customer Service snippet on the same list frames the value as:
“Streamlines Support with Flexible Automation and Clear Reporting.”
Stage Three: Partial Fulfillment (Agent As Task Runner)
Partial fulfillment is where autonomy becomes tangible. The agent completes bounded tasks, like updating account details, initiating a return, or scheduling an appointment.
This stage raises the stakes on integration, permissions, and reliability. A Genesys Cloud CX reviewer snippet highlights that foundation as:
“Flexible, Robust Cloud-Native Platform with Powerful APIs and Analytics.”
Stage Four: End-To-End Resolution (Agent As Owner)
End-to-end resolution means the agent can carry a case from intent to outcome, and it also knows when to escalate. It is the most compelling promise, and also the stage where teams can over-claim impact.
Here, continuity matters, and context loss becomes expensive. A Gladly reviewer snippet points to that expectation:
“Intuitive, Conversation-Based Support That Keeps Full Context Across Channels.”
Why CX Is The First Place Buyers Trust Agents
The buyer logic is straightforward. Support and contact centers already run on metrics like containment, resolution time, CSAT, and cost per contact, so teams can measure change quickly.
CX also has repeatable workflows. That makes it easier to set boundaries for autonomy, test safely, and spot failures early.
What To Be Careful Not To Over-Claim
A high rank reflects satisfaction and market presence signals. It does not mean a vendor’s agent will deliver the same results in every environment.
We should also avoid implying that agents remove the need for people. In practice, autonomy usually changes work distribution. It automates the repeatable middle, and it pushes humans toward exceptions and high-stakes conversations.
The Takeaway For Enterprise Teams
G2’s new Agentic AI list does not settle the category. It does, however, hint at where durable wins are emerging first.
If you are planning an agent program, start where resolution is easiest to define and govern. Then build the knowledge, automation, and oversight muscle that makes autonomy safe at scale. Over time, the teams that do that will not only deflect more contacts, they will resolve more customer moments, and that is where loyalty tends to compound.
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