January 28, 2026
Agentic AI in customer support is grabbing headlines in 2026, and for good reason. The conversation among Saa S leaders and digital businesses everywhere is shifting from basic automation to advanced agent-based AI systems that actually complete multi-step support tasks, learn from context, and work alongside human agents. Major launches like Moltbot and updates from Open AI and Anthropic only highlight how fast things are changing. If you’re in customer support or product management, there’s real urgency, this isn’t a future trend, it’s happening now. Understanding agentic AI in customer support is your ticket to staying competitive and making your customer experience smoother and smarter.
Agentic AI in customer support takes the concept of AI chatbots several steps further. Instead of just answering FAQs or providing canned responses, agentic agents are built to understand context, hold longer conversations, remember details, and execute multi-step tasks, like troubleshooting, processing returns, and even offering proactive help without being prompted. The term "agentic" refers to these systems acting much more like a real human agent, using reasoning, data-gathering, and even collaborating with other agents or people when required.
Not long ago, most customer support automation was limited to rule-based chatbots and simple helpdesk integrations. These tools had value, but their responses were rigid, their context memories short, and they failed when conversations became complex. The leap to agentic AI started around 2024, as foundation models improved and businesses demanded more from automation: accuracy, empathy, API integration, and real multi-channel reach.
| Old Support Bots | 2026 Agentic AI |
|---|---|
| Scripted FAQ and keyword triggers | Multi-step action, dynamic reasoning, and cross-app execution |
| Often disconnected from actual workflows | Directly updates orders, CRMs, and schedules in real time |
| Short memory, no learning from conversations | Remembers past customer issues and adapts over time |
| Reactive only, responds to direct queries | Proactively reaches out when it spots problems or opportunities |
According to Machine Learning Mastery, 72% of business leaders say agentic AI now outperforms human support for speed and consistency. The difference is felt immediately: agentic systems can resolve up to 90% of repetitive tickets and collect feedback automatically. And while they're always-on, humans are free to focus on edge cases and relationship-building.
2026 has seen an explosion of new agentic AI products in customer support. While tools like Moltbot are getting buzz for their end-to-end task automation, giants like Open AI are integrating proactive agent-like features into their platforms (think beyond mere chatbots). Anthropic and others are emphasizing agents that learn local processes and integrate tightly with internal business systems. Instead of viewing AI as an add-on, successful teams are making agentic platforms the hub for all customer communications, across live chat, email, social, and feedback portals.
The shift has been so significant that industry watchers, like Co Support and Buzz Clan, are calling 2026 the "Year of Agentic AI." Analysts cite multi-agent orchestration, where teams of specialized agents work together, as a major win. This is powering everything from IT support to e-commerce logistics without constant human intervention.
The biggest impacts of agentic AI in customer support fall into three buckets: faster resolution, proactive service, and new ways to gather feedback. Here’s how that looks in practice:
The adoption curve is steep. Gartner now estimates that 40% of enterprise customer service apps will have integrated agentic AI by the end of 2026, up from just 5% last year. Businesses are drawn by huge improvements in resolution rates, but also the fact that agentic AI reduces repetitive query volume by over 70%, as reported by Crescendo.ai.
| Traditional Automation ROI | Agentic AI ROI |
|---|---|
| Saves time on FAQs and simple tickets | Automates resolution of complex, multistep workflows and cross-channel feedback |
| Limited reduction in human staffing | Frees 40%+ of team hours for value-added work |
| Manual reporting and analysis | Automated insights, real-time analytics, and smarter feedback collection |
Of course, implementing agentic AI isn’t without challenges. Deloitte cautions that over 40% of projects may fail if legacy systems aren’t updated, and slow internal buy-in can stall rollout. But as major Saa S support providers (including Gleap) include agentic AI features, onboarding is getting easier and more accessible for mid-sized businesses, no in-house AI team required.
Forward-looking support teams are already using agentic AI to drive competitive advantage. The next 12 months will likely bring even smarter agent collaboration, as standards like MCP and A2A connect these systems together for end-to-end customer journey management. AI models will use more domain-specific data, further closing the gap between human and AI empathy.
If you lead a customer support or product team in Saa S, agentic AI in customer support is no longer a curiosity. It’s core to delivering faster, smarter, and more human-feeling service at scale. Ignore it, and you’ll likely fall behind; embrace it, and the efficiency, insight, and customer satisfaction returns can be transformative. If you’re looking for inspiration or a starting point, platforms like Gleap now build agentic capability into feedback collection, live chat, and multi-channel support, helping you get up to speed fast without needing to overhaul everything overnight.
Sources: MIT Technology Review, Machine Learning Mastery, Crescendo.ai, Buzz Clan, Co Support, Deloitte, Gartner, and various Substack and industry newsletters as of January 2026.
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