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

Salesforce is all-in on AI Agents with Agentforce. Today at Dreamforce, Salesforce demonstrates Agentforce; a significant step toward leveraging AI agents to transform customer success, operational efficiency, and innovation across industries. Their tagline, "Humans with agents drive customer success," captures the idea of collaboration and synergies. Salesforce has a comprehensive approach to making AI Agents accessible and effective for all. ✅ A definition and framework for AI Agents. At its core, an Agentforce agent is defined by five key elements: - Role: The purpose of the agent on your team. - Knowledge: The data the agent needs to be successful. - Actions: The goals an agent can fulfill. - Guardrails: The guidelines an agent operates under. - Channel: The applications where the agent gets work done. ✅ Three agents available at launch: - Customer Service Agent: Enhances customer support with dynamic, conversational AI to handle inquiries 24/7. - Sales Development Agent: Engages prospects around the clock, acting as an intelligent sales agent that drives lead generation and customer acquisition. - Sales Coach Agent: Provides tailored coaching for sales representatives, including personalized feedback, pitch practice, and negotiation strategies, making every rep the best they can be. ✅ Additional agents coming soon: - Agentforce Assistant: Easily create and execute tasks for every employee. - Marketing Agents: Autonomously optimize and personalize marketing campaigns. - Commerce Agents: Instantly set up, manage, and optimize online storefronts. - Employee Service Agents: Automate onboarding and provisioning for new hires, enhancing internal operations. ✅ I see significant potential for Agentforce solutions in sectors like: - Banking: Automating credit risk assessments, fraud detection, and loan processing. - Retail: Managing inventory and order fulfillment, driving personalized marketing campaigns. - Healthcare: Supporting patient scheduling, providing medical assistants, and handling billing queries. - Manufacturing: Predicting equipment failures, optimizing supply chain management, and enhancing safety compliance. - Finance: Automating financial audits, reporting, and compliance monitoring. - IT: Modernizing legacy software systems, handling software requests, and performing security audits. 💡 While these first iterations of agents are powerful, they are still in the early stages, focusing on specific tasks without yet collaborating with other agents. However, this thoughtful and focused start lays the foundation for more sophisticated, interconnected AI systems in future versions—paving the way for a new era in how businesses leverage large language models and AI-driven solutions. I’m excited to see where Salesforce takes Agentforce next. Kudos to Salesforce for leading the charge in AI innovation and making these powerful tools even more accessible!

Email templates can help customer service reps improve efficiency. But what happens when just choosing the right one becomes overwhelming? It's a case where AI can unlock human super-skills. One company implemented an AI tool from Laivly to help agents select the right template. Laivly's "Smart Response" feature analyzes incoming emails to suggest the right template for agents to use. Agents can review the suggested template for accuracy, and add personalization before sending the final email. The Smart Response tool improved productivity by 49%. Even better, customer satisfaction increased 10% and first contact resolution rose by 17%. It's a great example of using AI to handle tedious, repetitive tasks so agents can be freed to concentrate on work where they can add more human value. I'm increasingly seeing stories like this. Rather than humans or AI, it's humans and AI.

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.

At Chatbase, we’re only 15 people but we have 20+ AI agents running the show. Let me walk you through how we actually use them and why it works. 1. Social media is our first signal what’s trending, what’s changing, what’s clicking It’s not just about posting. We treat social like a listening tool. We’ve got agents that scrape LinkedIn, Meta, and Google ads from our competitors every day. They track what features they’re pushing, what pain points they highlight, and what language they use. That helps us spot early shifts in the market like when everyone suddenly starts talking about integrations or onboarding. So instead of guessing, I know what to post, write, or test next. We use: – Agent for scraping ads – Agent that summarizes tone, CTAs, strategy – Agent that drafts post hooks based on what’s working Tools: Lovable + Make..com + Supabase + OpenAI (We even have agent that helps me write my personal posts, more on that in a future post.) 2. Content is where the long-term game is played and we don’t play blind We mix two inputs: 1. What competitors are writing and ranking for 2. What our customers are asking and struggling with We have agents tracking competitor blogs and keywords so we know where we’re behind. But we also pull from our own changelog and support chats to turn real conversations into blog posts, carousels, newsletters, and help docs. We use: – Agent tracking competitors and keywords – Agent that surfaces support themes and insights from customer conversations (this is a really cool feature in Chatbase, you can summarize convos into groups. It’s great for support, but I love using it for marketing) Tools: Lovable + Make + Chatbase + OpenAI 3. Support isn’t a cost center, it’s our strongest growth engine Support is where we hear the truth. What’s broken? What’s unclear? What feature isn’t delivering? Our AI agents handle conversations directly on the site and inside the product but they also give us full visibility into what’s happening. And that’s gold for content, sales, and product. We use: – Agent that gives real-time support (with Stripe, Zendesk, Calendly, etc.) – Agent that turns FAQs into help docs – Agent that flags high-intent convos and routes to sales Tool: Chatbase 4. Sales isn’t just a funnel, it’s a feedback loop When someone reaches out for enterprise pricing, we don’t just want to “close” them. We want to know: – What pushed them to upgrade? – What blockers did they hit? – What feature made them reach out? This feeds back into our product messaging, pricing tiers, and content. We use: – Agent for qualifying leads – Agent for booking sales calls – Agent that logs which features are mentioned most during chats Tool: Chatbase That’s how we scale Chatbase. Not with a huge team. But with a smart setup. In the video, I wanted to show that every agent is connected and that delivering the best possible customer experience means marketing, sales, and support must work together.

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.

Don’t look at AI agents from a cost-saving lens. Look at them as a long-term investment in scalability, consistency, and reduced dependency on human cycles. Every company hits a point where its support operations start to creak - either because the query volume grows faster than hiring can keep up, or because training and retaining agents becomes a cycle of diminishing returns. But not every company needs AI agents today. So… how do you know if your company is ready? Here’s a simple framework we use when advising teams: ✅ AI agents might be a great fit if: - You get 500+ repetitive queries/month that follow predictable patterns (L1/L2) - Your team is spending over 30% of time on “known” questions - You’re expanding support across multiple channels - chat, email, WhatsApp, Voice - You're launching in new regions or languages, and want to scale without 5x-ing headcount - You’ve already documented SOPs, macros, or have a structured knowledge base ⚠️ They might not be worth it (yet) if: - The query volume is very low, or mostly one-off edge cases - Your internal processes are still evolving and SOPs keep changing weekly - You don’t yet know what “good support” looks like for your customers - You’re looking for a quick win just to cut costs, not improve experience The most common misconception is thinking AI agents are here to replace support teams. They’re not. They’re here to free up your team from the repeat work - so they can focus on the nuanced, human conversations that really matter. We’ve seen this play out well in industries like: - Consumer brands (Order status, returns, refund flows) - Fintech & lending (KYC follow-ups, user activation, re-engagement) - Mobility & devices (Troubleshooting, Part replacements, Warranty claims) So the next time someone asks, “Are AI agents right for us?” Don’t start with cost. Start with: “Where is our team spending time they shouldn’t have to?” #CustomerExperience #AIagents #SupportAutomation #OperationalExcellence #Robylon

In a world of instant gratification, your customer support builds loyalty faster than your products ever will. 𝐄𝐯𝐞𝐫𝐲 𝐝𝐞𝐥𝐚𝐲𝐞𝐝 𝐬𝐮𝐩𝐩𝐨𝐫𝐭 𝐚𝐠𝐞𝐧𝐭 𝐫𝐞𝐩𝐥𝐲 𝐨𝐫 𝐜𝐚𝐧𝐧𝐞𝐝 𝐜𝐡𝐚𝐭𝐛𝐨𝐭 𝐫𝐞𝐬𝐩𝐨𝐧𝐬𝐞 𝐩𝐮𝐬𝐡𝐞𝐬 𝐲𝐨𝐮𝐫 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫𝐬 𝐭𝐨𝐰𝐚𝐫𝐝𝐬 𝐬𝐰𝐢𝐭𝐜𝐡𝐢𝐧𝐠 𝐭𝐚𝐛𝐬, 𝐟𝐢𝐧𝐝𝐢𝐧𝐠 𝐚𝐧𝐨𝐭𝐡𝐞𝐫 𝐬𝐭𝐨𝐫𝐞, 𝐚𝐧𝐝 𝐧𝐞𝐯𝐞𝐫 𝐥𝐨𝐨𝐤𝐢𝐧𝐠 𝐛𝐚𝐜𝐤. Let’s be real, no small/mid-sized business or e-com startup can afford an army of support reps sitting on standby for every customer ping. And those out-of-touch chatbots that struggle to understand specific situational context or make decisions using customer's historical data? ? They don’t cut it anymore either. 𝐓𝐡𝐚𝐭’𝐬 𝐰𝐡𝐲 𝐰𝐞 𝐛𝐮𝐢𝐥𝐭 𝐚𝐧 𝐀𝐈-𝐩𝐨𝐰𝐞𝐫𝐞𝐝 𝐦𝐮𝐥𝐭𝐢-𝐚𝐠𝐞𝐧𝐭 𝐚𝐬𝐬𝐢𝐬𝐭𝐚𝐧𝐭 𝐭𝐡𝐚𝐭 𝐟𝐢𝐥𝐥𝐬 𝐢𝐧 𝐭𝐡𝐞 𝐠𝐚𝐩 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐬𝐥𝐨𝐰 𝐡𝐮𝐦𝐚𝐧 𝐫𝐞𝐩𝐥𝐢𝐞𝐬 𝐚𝐧𝐝 𝐦𝐞𝐜𝐡𝐚𝐧𝐢𝐜𝐚𝐥 𝐛𝐨𝐭𝐬. It responds instantly, 𝐠𝐞𝐭𝐬 𝐰𝐡𝐚𝐭 𝐲𝐨𝐮𝐫 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫𝐬 𝐫𝐞𝐚𝐥𝐥𝐲 𝐦𝐞𝐚𝐧, answers based on the customer’s specific problem rather than a preset rulebook, applies your business rules on the fly while generating responses, detects emotions and knows exactly when to loop in a human. Behind the scenes, multiple specialized agents work together, like the 𝐎𝐫𝐝𝐞𝐫 𝐋𝐨𝐨𝐤𝐮𝐩 𝐀𝐠𝐞𝐧𝐭, 𝐑𝐞𝐭𝐮𝐫𝐧 & 𝐏𝐨𝐥𝐢𝐜𝐲 𝐀𝐠𝐞𝐧𝐭, 𝐅𝐫𝐚𝐮𝐝 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 𝐀𝐠𝐞𝐧𝐭, 𝐒𝐞𝐧𝐭𝐢𝐦𝐞𝐧𝐭 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐀𝐠𝐞𝐧𝐭, etc. each handling its domain to deliver precise, contextual support in seconds. The result? Less chaos for your team, lower costs for your business, and customers who actually get answers when they need them. 🎥 𝐖𝐚𝐭𝐜𝐡 𝐭𝐡𝐞 𝐝𝐞𝐦𝐨 𝐭𝐨 𝐬𝐞𝐞 𝐭𝐡𝐞𝐬𝐞 𝐬𝐩𝐞𝐜𝐢𝐚𝐥𝐢𝐳𝐞𝐝 𝐚𝐠𝐞𝐧𝐭𝐬 𝐢𝐧 𝐚𝐜𝐭𝐢𝐨𝐧, 𝐬𝐨𝐥𝐯𝐢𝐧𝐠 𝐫𝐞𝐚𝐥 𝐞-𝐜𝐨𝐦𝐦𝐞𝐫𝐜𝐞 𝐬𝐮𝐩𝐩𝐨𝐫𝐭 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬 𝐢𝐧 𝐫𝐞𝐚𝐥 𝐭𝐢𝐦𝐞.