Agent-Assist Tools for Customer Support Teams

𝗔𝗜 𝘁𝗵𝗮𝘁 𝗱𝗼𝗲𝘀𝗻'𝘁 𝗿𝗲𝗽𝗹𝗮𝗰𝗲 𝘆𝗼𝘂𝗿 𝘁𝗲𝗮𝗺. 𝗔𝗜 𝘁𝗵𝗮𝘁 𝗺𝗮𝗸𝗲𝘀 𝘁𝗵𝗲𝗺 𝘂𝗻𝘀𝘁𝗼𝗽𝗽𝗮𝗯𝗹𝗲. Here's what most companies get wrong about AI in customer support: They think it's all or nothing—either full automation or no AI at all. HubSpot just proved there's a better way. Their new Reply Recommendations feature is the perfect example of "AI assistance, not AI replacement." 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: → Support reps stay in control (review, edit, send) → Customers get faster, more accurate responses → Teams build confidence in AI gradually → No risk of rogue AI responses damaging your brand 𝗧𝗵𝗲 𝗴𝗲𝗻𝗶𝘂𝘀 𝗺𝗼𝘃𝗲? They merged Reply Recommendations into Customer Agent, making it a zero-risk entry point. Teams can experience AI's value without deploying a fully autonomous agent. 𝗪𝗵𝗮𝘁 𝗜 𝗹𝗼𝘃𝗲 𝗺𝗼𝘀𝘁:  • Reps can dismiss, edit, or use recommendations  • AI learns from your actual content sources  • No credits used (yes, really)  • Human expertise + AI speed = magic 𝗧𝗵𝗶𝘀 𝗶𝘀 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝘄𝗼𝗿𝗸 𝗹𝗼𝗼𝗸𝘀 𝗹𝗶𝗸𝗲:  • Not humans OR machines.  • Humans AND machines.  • The best teams won't be the ones who resist AI.  • They'll be the ones who learn to dance with it. P.S. If you're still debating "should we use AI?"—you're asking the wrong question. The right question is: "How do we use AI to make our people more effective?"

If chatbots talk, AI agents execute. What’s an AI agent? An AI agent is autonomous software that understands your goal, plans the steps, uses tools/APIs, and learns from feedback to finish the job with minimal supervision. Think proactive operator, not just a chatbot. 🧠🛠️ Why it’s a game-changer 🚀 - From replies to results: Books meetings, files tickets, reconciles data, triggers deployments, and verifies outcomes. - From tasks to outcomes: Orchestrates multi-step workflows and collaborates with other agents to hit KPIs. - From scripts to learning: Adapts to edge cases, remembers context, and improves every run. Real wins you can copy today ✅ - Customer Support: Auto‑triage tickets, search KBs, summarize history, propose fixes, and escalate only when needed. - Sales Ops: Prospect → qualify → personalize → schedule → update CRM without nudges. - Content Engine: Research → outline → draft → fact-check → repurpose for LinkedIn/IG/X → analyze and iterate. - IT/DevOps: Watch logs, detect anomalies, run playbooks, verify recovery, and post‑mortems—fewer 3 a.m. alerts. - Finance Ops: Reconcile transactions, flag anomalies, prep monthly close, draft stakeholder updates. How it works (simple loop) 🔁 Perceive → Reason → Act → Learn. Inputs in, plans made, tools called, results improved—on repeat. Start this week (no fluff) 🗂️ - Pick one repeatable workflow with clear success criteria. - List required tools/APIs (docs, CRM, ticketing, calendar, storage). - Set guardrails for autonomy vs. human approval. - Log everything; review weekly to tighten prompts, memory, and policies. Scroll-stopping openers 🎯 - “Chatbots answer. Agents deliver.” - “Outcomes > outputs. Meet AI agents.” - “One agent > five manual workflows.” 💬 Comment “AGENT” for a plug‑and‑play blueprint to automate your most annoying workflow this week. #AIAgents #AgenticAI #Automation #GenAI #LLM #ToolUse #Workflows #Productivity #CustomerSupport #SalesOps #DevOps #MLOps #AIinBusiness #Growth #Startups #APIs #Operations #Engineering #TechLeadershipa

Support teams face constant pressure to resolve cases faster without overloading engineering. For one Glean customer, valuable resources were tied up in avoidable tickets, MTTR (mean time to resolution) hovered at nearly two days, and agents spent hours manually triaging cases. Their goal: boost self-solves, improve MTTR, and reduce R&D reliance – without adding more tools. So they embedded Glean in Zendesk, giving agents prompts to quickly gather knowledge across all company data. In triage, agents use Glean to find similar tickets, summarize runbooks and past Jira investigations, and compile clear updates for customers or well-packaged escalations. That streamlined process now drives faster resolutions, smoother knowledge transfer, and consistent workflows—leading to: • 34% increase in self-solves with more future automation planned - this is incredible progress • 24% faster MTTR (1.9 → 1.5 days) • 2–4 hours saved per week for 85% of users (13–26 business days/year) • Reduced R&D involvement in lower-tier tickets By streamlining resolutions, knowledge transfer, and process consistency, the team achieved remarkable results – proof of what’s possible when AI is embedded into everyday workflows. Stories like this are energizing – showing how teams are using Glean to reimagine what they can accomplish.

The 3 types of AI tools every CS leader needs to understand (and how to use them) AI tools are everywhere, but as a CS leader, you need to cut through the noise and understand what actually matters for your operation. Here’s my simplified breakdown for customer success applications: 1/ Large Language Models (LLMs) What they are: The “brains” behind ChatGPT, Claude, Gemini - sophisticated tools that read and write like humans. How CS leaders use them: • Analyzing customer call transcripts to identify risk signals • Generating personalized QBR content based on usage data • Creating customer-specific success plans from templates • Summarizing months of customer interactions before renewal calls Key limitation: They don’t know your customer data unless you feed it to them. 2/ Workflow Automation Platforms What they are: Tools like Zapier, Workato, and Microsoft Power Automate that connect your existing systems and automate step-by-step processes. How CS leaders use them: • Automatically updating health scores when usage patterns change • Triggering alerts when customers miss onboarding milestones • Creating customer pulse reports by pulling data from multiple systems • Routing high-risk accounts to senior CSMs based on specific criteria CS-specific example: When a customer’s usage drops 30% week-over-week, automatically create a task for their CSM, pull recent support tickets, and generate a summary of their recent interactions. 3/ AI Agents *lWhat they are: Digital helpers that can complete specific tasks within larger processes, combining LLM intelligence with system integrations. How CS leaders use them: • Research agents that compile customer background before executive meetings • Health score agents that analyze multiple data sources to predict churn risk • Content agents that create personalized customer communications • Analysis agents that identify expansion opportunities based on usage patterns CS-specific example: An agent that monitors customer communications, identifies mentions of business challenges, researches relevant case studies, and drafts personalized recommendations for the CSM to review. —- I keep thinking about the ways to get started, it all seems like so much. Change management, getting IT or security involved… but you need to just start. Start with your biggest operational pain points: 1. Identify repetitive tasks your team does manually 2. Map which type of AI could address each task 3. Test with simple workflows before building complex agents 4. Measure impact in terms of CSM time saved and customer outcomes The technology exists today. The real work is understanding your CS processes well enough to determine where AI can replace tasks currently requiring human intervention. Remember: Agents handle individual smart tasks. Workflows organize how those tasks connect. LLMs provide the intelligence that makes it all possible. What CS process would benefit most from AI automation in your organization?

We built a Zendesk email assist AI agent and it's handling a full quarter’s work for one human support rep. Here's the step-by-step flow: 1. User sends a complex or nuanced product question to support@voiceflow.com 2. Tico (our AI agent) reviews the question and passes the content and intent. 3. The most fitting knowledge base is tapped via confidence level. 4. A personalized, accurate & highly-specific response is drafted. 5. The draft is slotted into Zendesk as a private comment. 6. Our team reviews, tweaks if necessary, and sends it to the user. This has slashed the onboarding and training time for support staff that's typically slowed down by the complexity of the product. The impact? ✅ Our support team is no longer just keeping up; they’re ahead, delivering faster, sharper responses. ✅ Customers feel understood, their issues addressed with pinpoint accuracy, boosting our CSAT scores. ✅ Tico’s continuous learning means every interaction makes it smarter, ready for even the most nuanced queries. So far, Tico Assist is tackling over 2000 tickets - a full quarter’s work for one human support rep, for less than the price of lunch. If you’re navigating high support volumes with a lean team, this type of Zendesk AI Assist Agent can help blend automation with quality for your customers. P.S. Tico doesn’t just fetch any answer. It pulls from the most relevant knowledge base (e.g. a technical code response for a developer question). From my post last week, this multi-knowledge base strategy is something that I think we will see much more of in CX this year.

Contact centers may not be the most exciting application for AI, but as our team has been digging into the category, I’ve been impressed by how far things have come — even since we last looked at it a few months ago. One area in particular is AI agent assistants. These copilot solutions are advancing rapidly, with capabilities such as: • Call summarization, classification, and structured data collection (i.e. filling out CRM fields) • Agent response and next-best-action support (for both chat and phone conversations) • Real-time caller sentiment analysis • Real-time QA and agent feedback • Automatic surfacing of relevant information (e.g. SOPs, help content, and customer info) Unlike many of the other areas we cover, the AI agent assistant category is primarily composed of vendors who are not specific to the healthcare industry. These products frequently show up as part of more comprehensive omnichannel Contact Center as a Service (CCaaS) platforms, such as: • Bright Pattern • Dialpad • Five9 • Genesys • NICE • Talkdesk • ujet.cx Additionally, there are a handful of industry-agnostic vendors who offer agent assistants as a standalone product or paired with broader intelligence features, like QA insights and performance analytics. These include: • Abstrakt • Balto • Convin • JustCall • Level AI Where the vendors above offer solutions that will work across all contact center use cases, there are situations where solutions for specific healthcare workflows — such as instances where clinical care and digital communication overlap — are needed. While these solutions may not work for your entire contact center, they can drive meaningful value for specific aspects of your operation. Examples include: • Birch.ai - healthcare-specific AI-powered agent assistants and call center intelligence • Laguna Health - AI-enabled conversational AI care management platform • Rotera Alyks - digital assistant for revenue cycle call center operations • Verbal - AI-enabled assistance and QA platform for virtual care clinicians We're interested to see whether organizations will be willing to implement multiple specialized solutions or will sacrifice specificity for efficiency with one-size-fits-all options. Like everything else in AI these days, this space is evolving rapidly.

Last month, I met with four customers using HubSpot’s Customer Agent. Here’s what we learned about the state of agents in customer experience. 1. Agents solve capacity challenges for small and medium businesses. One customer is managing 120 brands with a team of one. Another is competing with big box stores as a family business. Customer Agent is resolving 65%+ of tickets automatically. There’s always more work than SMBs can do – agents provide the leverage. 2. Instructions are the unlock. One customer spent a full day mapping 20 categories and 200 questions into custom instructions. They taught the agent what to say AND what not to say. Now it asks the right follow-up questions, gathers context, and routes customers exactly where they need to go. 3. 24/7 multilingual support levels the playing field. Three time zones, multiple languages, 24/7 is no longer a feature list. It’s how small businesses compete with global corporations. For a family business, having an agent work Saturday afternoons when they’re closed helps them compete on a larger scale. 4. The bar for business AI is consumer AI. Customers use Perplexity and ChatGPT every day. They’ll only adopt business tools that match or beat that experience. Agents need to understand custom properties, ask intelligent follow-ups, and work across the entire customer journey, not just support tickets. What’s working (or not working) with AI agents in your customer experience? Let me know in the comments.

Today, every company needs a website. Tomorrow, every company will need an AI agent that can answer any customer question. Not just support tickets—but pricing, webinars, demos, product details, and more. That’s my big takeaway after seeing how thousands of companies are using Breeze Customer Agent. Here are 3 reflections: 1. Customer Support is a phenomenal use case for AI. Customer Agent resolves over 50% of tickets on average – with some customers seeing resolution rates up to 80%. Nutribees are using it to handle 77% of their tickets – in the words of their CMO, “It’s a key tool for generating revenue and increasing customer satisfaction." AI isn’t just helping Support teams resolve tickets, it’s expanding their impact. 2. Better data = better results. The customers with the best resolution rates all had one thing in common: a well-documented knowledge base. That insight led us to launch Knowledge Base Agent – it fills knowledge gaps by drafting new articles, giving Customer Agent the data it needs. When it comes to agents, data is the difference-maker. 3. Agents need full context across the customer journey. Customers don’t only ask questions about customer support. They ask about marketing webinars, sales demos, and product pricing. Point agents that can only answer one category of questions will run into problems once a customer needs help with something else. Without context, point agents will cause the same problems as point apps. Now, back to my main takeaway: In the future, every company will want an AI agent to answer the full range of customer questions. That’s why in June, we’re expanding Customer Agent to all Pro and Enterprise customers across every Hub. We want every go-to-market team to be able to deliver a powerful AI-first experience for customers and scale their results – whether that’s resolving more tickets, generating more leads, or closing more deals. We’ve learned a lot over the last few months, and we’re just getting started. I can’t wait to see what our customers achieve!

Breeze Customer Agent isn’t just about efficiency; it’s about elevating customer interactions to a new standard of intelligence and responsiveness. Where it has been useful for us: 1. Seamless AI-to-Human Handoff – Breeze detects when a query requires human attention and intelligently assigns it to the right team, along with the conversation history, reducing repetitive questions. 2. AI-Powered Lead Qualification & Conversion – The AI uses smart forms, CRM integrations (e.g., HubSpot), and real-time conversation scoring to identify potential leads before routing them to sales. Have you implemented AI in your support strategy? Let’s discuss how AI-driven solutions like Breeze Customer Agent are shaping the next era of customer success.