December 18, 2025
Over the last 18 months, something subtle but profound has happened in customer support: AI has stopped being “the chatbot in the corner” and started to become the workflow layer that runs the whole operation.
Salesforce is rolling out Agentforce 360 with context-rich, deterministic agents built on trusted enterprise data. OpenAI’s GPT‑5.2 emphasizes multi-step project handling and tool use across long contexts. Google is wiring Anthropic’s Model Context Protocol (MCP) directly into Google Cloud so agents can safely talk to Maps, BigQuery, Kubernetes and internal APIs. At the same time, CX leaders report that agentic AI—systems that can autonomously plan and execute multi-step work—is moving from experiment to expectation.
For SaaS and CX teams, this isn’t just a model upgrade. It’s the beginning of a shift from AI copilots that suggest answers inside one channel to AI orchestrators that coordinate end-to-end customer journeys across every channel you support.
This article explores what that shift means for multichannel customer support, how agentic workflows will change the way you design your CX stack, and where a unified platform like Gleap can sit at the center of this new operating model.
Most teams’ first encounter with AI in support looked like this:
These were AI copilots: narrow assistants that could answer questions, but not move work forward.
In 2025, the frontier has shifted toward agentic AI—agents that can interpret context, decide on a plan, call tools or APIs, and update systems without explicit step-by-step instructions. Several developments from the sources you provided show this clearly:
The common thread: AI is evolving from an interface that sits in front of your stack to an orchestration layer inside it.
If you wanted to design the perfect stress test for agentic AI, you’d end up with something that looks a lot like modern SaaS support:
Userpilot’s 2025 CX trends highlight exactly this pressure: customers now expect seamless omnichannel experiences and proactive support. Crexendo’s analysis of AI in cloud communications reaches the same conclusion from the telco/UCaaS side: AI is becoming the default infrastructure for how we route, analyze and respond to conversations.
In that environment, a single-channel chatbot is a band-aid. What teams actually need is an AI orchestrator that can:
Most teams don’t need a full-blown “agent mesh” to see value. They need a practical architecture that fits into their existing CX stack and can mature over time.
Vendors love to market “unified inboxes,” but what agentic workflows really require is a unified context layer underneath that inbox:
Salesforce’s “context machine” approach and Google’s MCP-driven access to cloud services both underline the same thing: without trusted, structured context, agents are forced to guess. In CX, that translates to hallucinated answers, wrong refunds, or failed promises.
A platform like Gleap is well-suited as this context layer because it already sits where those threads converge: in-app bug reports with console logs, session replays, feature requests, public roadmaps, surveys and live chat, all tied to user identities and environments.
On top of this context layer, you define a set of agentic workflows. Think of them less as “smart bots” and more as distributed routers that:
Inspired by Salesforce’s Agent Script model, a pragmatic orchestration layer for support typically uses a hybrid design:
This structure is crucial for CX leaders worried about compliance, brand risk, or agents “going off-script.” It means you can expose powerful tools (refunds, plan changes, data deletes) but only under rules you define.
Google’s adoption of MCP—and its roadmap to wrap more services like Storage, databases, logging and security tools behind it—is a strong signal: standardized tool access is the future.
In a SaaS CX setting, your agentic tooling layer might include:
This is where Gleap’s workflow automation & integrations become pivotal. Instead of wiring each tool directly to your model, you let the agent talk to Gleap’s automation layer, which then fans out to:
Designing this middle layer well means you can swap models or LLM vendors later, without re-plumbing your entire CX stack.
All major sources—from Salesforce to Deloitte to Userpilot—stress one reality: agentic AI is not a human replacement. It’s a reconfiguration of work.
In support, that means:
Practically, you should design for three levels of autonomy:
Gleap can operationalize this by exposing AI-suggested actions right inside the thread view of its multichannel inbox, with clear labels (AI suggested, awaiting approval, auto-resolved) and escalation controls.
Rather than “turn on an AI agent for everything,” forward-looking teams are picking narrow, high-leverage workflows and expanding from there.
Problem: Agents burn time parsing long emails, fragmented chats, and vague bug reports; high-priority issues get buried behind low-value tickets.
Agentic workflow:
Outcome: Agents see a prioritized, enriched queue instead of raw noise. Mean time to first meaningful response drops without adding headcount.
Problem: Customers silently struggle across sessions and channels, then churn without ever opening a ticket. Traditional analytics can flag risk, but acting on it is manual and slow.
Agentic workflow:
Outcome: Support stops being purely reactive. You operationalize “proactive success” that Userpilot and Crexendo highlight, but with AI doing the heavy lifting instead of manual lists.
Problem: Support and engineering live in different systems; bug reports lack context; engineers waste time reproducing issues; customers don’t know when things are fixed.
Agentic workflow:
Outcome: You get the “context-rich agent” benefits Salesforce is building—without needing to be on Salesforce. Time-to-resolution shortens, and customers experience a tight, visible loop from report to fix.
Problem: Many of the highest-impact support automations (refunds, plan changes, data deletion) are also the riskiest. Leaders want the efficiency, but not rogue agents.
Agentic workflow:
Outcome: You get the speed of AI while keeping humans meaningfully in the loop where it matters—mirroring the hybrid reasoning and governance approach now championed by major platforms and analyst firms.
As Crexendo and Deloitte both note, the limiting factor for mainstream agentic AI isn’t just accuracy; it’s trust. Support leaders worry less about a wrong FAQ answer and more about actions that violate policy, leak data, or damage the brand.
To adopt AI orchestrators responsibly in multichannel support, you need a governance framework that covers:
Document what agents can and cannot do in your CX domain:
Then encode these rules in your orchestration engine and in Gleap’s automation workflows, not just in documents.
The more your agents act, the more you need observability:
Gleap’s existing logging (session replays, console and network logs) can extend naturally into this domain: you don’t just replay browser behavior; you replay agent behavior.
Following best practices emerging from early adopters, you can roll out autonomy in stages:
This mirrors how contact centers adopt new IVR flows—except now the flows can learn and adjust dynamically.
Gleap isn’t a foundation model vendor or a hyperscale cloud provider, and it doesn’t need to be. Its strategic role in an agentic CX world is to be the operating system where feedback, support, and product decisions converge.
Concretely, that looks like:
By combining this with an external orchestrator (or native AI capabilities) that leverages modern models like GPT‑5.2 and protocols like MCP, SaaS teams can get the benefits of Salesforce-like agentic architectures—without replatforming their entire business.
If you’re responsible for support, CX, or product, and you’re trying to turn these trends into a roadmap, here’s a pragmatic path:
The move from AI copilots to AI orchestrators won’t be a single launch; it will be a series of increasingly ambitious workflows that quietly reshape how support runs behind the scenes.
Teams that start now—by unifying context, tightening human-in-the-loop governance, and choosing platforms that connect support, feedback and product—will be the ones that turn agentic AI from a buzzword into measurable gains in resolution time, cost per ticket, activation, and retention.
And as the underlying technologies—Agentforce-style context engines, GPT‑5.2-class reasoning models, MCP-powered tool meshes—continue to mature, they’ll find their most tangible impact not in demos, but in the invisible choreography of every support interaction your customers experience.
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