Virtual Support Agent Design

The way we think about agents today is overly naive. We treat them like they're one thing—"agents"—when they're actually going to be as varied as software itself. A customer support agent needs to be careful, double-check everything, build trust. A commercial agent? Maybe you want it to be a bit pushy. Decision support agents can never be wrong about a number, never leak information, and must explain their reasoning clearly. Each type requires completely different design choices. Your customer support agent needs to understand your specific return policies, your brand voice. Your decision support agent needs to know your risk tolerance, your strategic priorities, how your board thinks. These aren't generic capabilities—they're deeply specific to how your organization operates. The future isn't one super-intelligent agent or one type of agent for all tasks. It's dozens of specialized agents, each designed for its specific role in your specific organization. Those who grasp this will deploy the right agent for each job. Those who don't will wonder why their one-size-fits-all approach keeps falling short. #AI #AIDilemma #AIAgents #EnterpriseAI

Metacognition is central to our ability to use AI well. The paper "Exploring the Potential of Metacognitive Support Agents for Human-AI Co-Creation" demonstrates how "metacognitive agents" can help human mechancial designers, also surfacing valuable lessons on effective agent design. The Carnegie Mellon University researchers created three agents, SocratAIs, HephAIstos and Expert FreeForm. Some of the key findings: 🧠 Metacognitive agents boost design feasibility. Designers supported by metacognitive agents produced significantly more feasible mechanical parts than those without support. The average design quality score was 3.5 out of 5 for supported users, compared to just 1.0 for unsupported users. 🗣️ Voice-based agents effectively prompt reflection. Using a voice interface, agents like SocratAIs and HephAIstus prompted designers to reflect on their design decisions and simulate real-world conditions. For instance, SocratAIs’ questions led users to reconsider incorrect force directions, improving load case setup and part feasibility. 🛠️ Sketching + planning enhances design reasoning. HephAIstus prompted users to sketch free-body diagrams and fill out planning sheets, leading to deeper engagement and improved problem setup. All users followed through with these activities, and in several cases, these tools anchored productive discussions that corrected prior design flaws. 📉 Over-questioning can backfire. While SocratAIs helped many, repeated questioning sometimes increased doubt and led users to override correct assumptions. In one session, this caused a participant to regress from a correct load setup to an incorrect one, illustrating how reflective support needs careful timing and calibration. 👥 Experts adaptively modulate support. Expert designers acting as support agents intuitively timed their interventions, sometimes delaying advice until users showed readiness. They blended reflective questioning with direct support, effectively guiding users without overstepping or causing dependency. 🧭 Metacognitive agents enhance self-regulation. Participants reported that agents helped them plan better and reflect more thoroughly. Some described feeling more organized and aware of their design logic, aligning with principles of self-regulated learning. One user noted the agent “walked me through my own thought process.” There is a lot more work to do in this vein, but this offers an important framing and valuable insights.

LangChain recently published a helpful step-by-step guide on building AI agents. 🔗 How to Build an Agent –https://lnkd.in/dKKjw6Ju It covers key phases: 1. Defining realistic tasks 2. Documenting a standard operating procedure 3. Building an MVP with prompt engineering 4. Connect & Orchestrate 5. Test & Iterate 6. Deploy, Scale, and Refine While the structure is solid, one important dimension that’s often overlooked in agent design is: efficiency at scale. This is where Lean Agentic AI becomes critical—focusing on managing cost, carbon, and complexity from the very beginning. Let’s take a few examples from the blog and view them through a lean lens: 🔍 Task Definition ➡️ If the goal is to extract structured data from invoices, a lightweight OCR + regex or deterministic parser may outperform a full LLM agent in both speed and emissions. Lean principle: Use agents only when dynamic reasoning is truly required—avoid using LLMs for tasks better handled by existing rule-based or heuristic methods 📋 Operating Procedures ➡️ For a customer support agent, identify which inquiries require LLM reasoning (e.g., nuanced refund requests) and which can be resolved using static knowledge bases or templates. Lean principle: Separate deterministic steps from open-ended reasoning early to reduce unnecessary model calls. 🤖 Prompt MVP ➡️ For a lead qualification agent, use a smaller model to classify lead intent before escalating to a larger model for personalized messaging. Lean principle: Choose the best-fit model for each subtask. Optimize prompt structure and token length to reduce waste. 🔗 Tool & Data Integration ➡️ If your agent fetches the same documentation repeatedly, cache results or embed references instead of hitting APIs each time. Lean principle: Reduce external tool calls through caching, and design retry logic with strict limits and fallbacks to avoid silent loops. 🧪 Testing & Iteration ➡️ A multi-step agent performing web search, summarization, and response generation can silently grow in cost. Lean principle: Measure more than output accuracy—track retry count, token usage, latency, and API calls to uncover hidden inefficiencies. 🚀 Deployment ➡️ In a production agent, passing the entire conversation history or full documents into the model for every turn increases token usage and latency—often with diminishing returns. Lean principle: Use summarization, context distillation, or selective memory to trim inputs. Only pass what’s essential for the model to reason, respond, or act.. Lean Agentic AI is a design philosophy that brings sustainability, efficiency, and control to agent development—by treating cost, carbon, and complexity as first-class concerns. For more details, visit 👉 https://leanagenticai.com/ #AgenticAI #LeanAI #LangChain #SustainableAI #LLMOps #FinOpsAI #AIEngineering #ModelEfficiency #ToolCaching #CarbonAwareAI LangChain

I set up a support AI agent in 20 minutes on your competitor’s system. So why are you estimating 90 days?   True story: I lost a support AI deal I had been working on for six months despite all the effort.   I had personally guided the customer through everything:   🔹 How LLMs work 🔹 What RAG is 🔹 How to configure agents 🔹 Why fact checkers and guardrails matter 🔹 How to organize and activate access control 🔹 Brainstorming the ideal experience and integrations   After 10+ Starbucks trips, countless discovery calls, and whiteboard sessions, the customer got an agent studio license and set everything up by himself.   I took this challenge to my team:   Can we reduce 90 days of setup to 15 minutes?   Their response:  "Setting up a Q&A system? Sure, that takes 15 minutes. But we build problem-solving Support AI agents that resolve complex support issues."   Our goal is not just speed. It is accuracy, contextual precision, and consistency.   90 days ensures the AI is calibrated, deflects 70% of tickets, and maintains 90%+ accuracy.   My tone-deaf response:   "Can we do all this in 15 minutes?"   Four months later, my team made it happen. A high accuracy, self-serve, problem-solving Support AI agent configurable in 15 minutes.   It includes:   ✅ Agentic RAG for smarter retrieval ✅ A personalization engine ✅ A clarification engine ✅ A Q&A engine ✅ An escalation engine ✅ A classification engine ✅ A team assignment engine   Four months later, I checked a support issue on my competitor’s website. The answer was inaccurate.   My takeaway for support leaders:   Let's focus on solving tickets autonomously, not just exposing a basic low accuracy Q&A system that hurts NPS.   I am happy to spend time sharing details on how to achieve this on any solution of your choice for free.   A cup of coffee would be nice, though. 🙂   You can also find info on what are the capabilities your support agent should have here. https://lnkd.in/dSHEVE3N

UX Designers could be the next "AI Agent Designers" AI agents are already shaping human decisions—but for AI to truly work for humans, it needs more— human understanding.   That’s where UX designers come in. Engineers may build AI’s brain, but designers give it a heart and soul—ensuring it thinks, reacts, and feels in a way that aligns with human needs. Here’s why UX designers—are uniquely equipped to design AI agents that people can trust and rely on. 1. Designers Lead with Empathy AI without empathy is just automation. Designers are trained to map human emotions, build personas, and design for real-life complexities. Without this skill, AI agents risk being robotic in the worst way possible. 🔹 A mental health AI must detect distress and respond with sensitivity, not scripted advice. 🔹 A customer service AI should sense frustration and adjust its tone—just like a skilled human agent. 2. Designers Understand Mental Models People approach AI with preconceived expectations—Designers align AI interactions with user expectations, ensuring that AI behaves in a way that makes sense to the people using it. 🔹 When designing an AI assistant for a hospital, should patients feel like they’re talking to a doctor or a supportive companion? 🔹 When using a legal AI, should it sound authoritative or collaborative? 3. Designers Optimize Workflows The best AI agents don’t just respond—they anticipate and assist. This requires a deep understanding of workflows and context—something UX designers are already skilled at. 🔹 A finance AI should automate repetitive decisions but flag high-risk ones for human review. 🔹 A customer support AI needs to know when to escalate a case to a human agent before the user gets frustrated. 4. Designers Think About Cognition, Not Just Code Human cognition is messy—people forget, hesitate, and change their minds. Designers study cognitive load, decision-making patterns, and how people trust (or mistrust) technology. Without this, AI risks making decisions that feel alien to humans. 🔹 If a shopping AI makes a recommendation, should it explain why it chose that option? 🔹 If a personal finance AI detects risky spending, how should it phrase the alert to motivate behavior change? 5. Designers Have a Research & Problem-Solving Mindset Great AI isn’t just built—it’s tested, refined, and iterated. Without this mindset, AI agents may function technically, but they won’t connect emotionally with users. 🔹 Designers run user research to understand pain points. 🔹 They apply human-centered thinking. 🔹 They focus on solving real problems—but making it useful, reliable, and humane. AI Needs More Than Intelligence. It Needs Design. The future of AI isn’t just about writing better algorithms. It’s about designing AI that feels natural, intuitive, and trustworthy. 💡 What’s your take? How can Agent designers shape the AI-driven future? #AgentDesigners #AIUX #HumanCenteredAI #UXDesign #AIExperience

🚀 How I Built My Federal Agency’s First ServiceNow Virtual Agent When I became a Senior AI Product Manager, I was tasked with a bold challenge: Build the agency’s first AI-powered ServiceNow Virtual Agent for the IT Service Desk. This wasn’t just about launching a chatbot it was about transforming how employees experienced IT support. 🔑 The Problem Thousands of repetitive tickets (password resets, account unlocks, status checks). Long wait times for basic issues. Knowledge base underused because it wasn’t easy to navigate. 💡 Our Approach Discovery: Interviewed agents & end-users → 60–70% of tickets were repetitive. Build: Used ServiceNow Virtual Agent + Flow Designer + Service Catalog + AI Search. Train: Built NLU models to understand real employee language (“I can’t log in” vs. “reset LDAP”). Test & Iterate: Measured deflection rate, success rate, and satisfaction. Launch: Rolled out in phases with strong change management and clear comms. ✅ The Impact Ticket deflection up, fewer routine calls. Agents freed up for complex work. Employees gained 24/7 support for common issues. 📌 Lessons Learned Start small expand use cases gradually. Strong data = strong chatbot. Human + AI creates the best experience. Change management is as critical as tech. This project proved that government product managers can drive digital transformation AI isn’t just for the private sector. It’s reshaping how public servants get work done. This is just Part 1. I’ll be sharing more in my Substack series: success metrics, cross-department expansion, and what I’d do differently if I built it again today. 👉 Read the full playbook here: https://lnkd.in/euSfb96V ServiceNow ResearchServiceNow Community ServiceNow Partners #ITSM #AIAgent #Chatbot #ServiceNow #VirtualAgent #AI

Ever wonder how AI agents solve problems one step at a time? 🤔 🔧 𝗧𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: Traditional AI assistants often stumble on complex, multi-step issues – they might give a partial answer, hallucinate facts that don't exist, deliver less accurate results, or miss a crucial step. 🧠 𝗧𝗵𝗲 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: Agentic AI systems with 𝘀𝗲𝗾𝘂𝗲𝗻𝘁𝗶𝗮𝗹 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 to handle complexity by dividing the problem into ordered steps, assigning each to the most relevant expert agent. This structured handoff improves accuracy, minimizes hallucination, and ensures each step logically builds on the last. 📐𝗖𝗼𝗿𝗲 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲: By focusing on one task at a time, each agent produces a reliable result that feeds into the next—reducing surprises and increasing traceability. ⚙️ 𝗞𝗲𝘆 𝗖𝗵𝗮𝗿𝗮𝗰𝘁𝗲𝗿𝗶𝘀𝘁𝗶𝗰𝘀 • Breaks complex problems into sub-tasks • Solves step-by-step, no skipped logic • Adapts tools or APIs at each stage 🚦𝗔𝗻𝗮𝗹𝗼𝗴𝘆: - Think of a detective solving a case: they gather clues, then interview witnesses, then piece together the story, step by step. No jumping to the conclusion without doing the groundwork. 💬 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗘𝘅𝗮𝗺𝗽𝗹𝗲 - 𝘊𝘶𝘴𝘵𝘰𝘮𝘦𝘳 𝘚𝘶𝘱𝘱𝘰𝘳𝘵 𝘚𝘤𝘦𝘯𝘢𝘳𝘪𝘰: A user contacts an AI-driven support agent saying, “My internet is down.” A one-shot chatbot might give a generic reply or an irrelevant help article. In contrast, a sequential-processing support AI will tackle this systematically: it asks if other devices are connected → then pings the router → then checks the service outage API → then walks the user through resetting the modem. Each step rules out causes until the issue is pinpointed (say, an outage in the area). This real-world approach mirrors how a human support technician thinks, resulting in far higher resolution rates and user satisfaction. 🏭 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲 - 𝘐𝘛 𝘛𝘳𝘰𝘶𝘣𝘭𝘦𝘴𝘩𝘰𝘰𝘵𝘪𝘯𝘨: Tech companies are embedding sequential agents in IT helpdesk systems. For instance, to resolve a cybersecurity alert, an AI agent might sequentially: verify the alert details → isolate affected systems → scan for known malware signatures → quarantine suspicious files → document the incident. 📋 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗖𝗵𝗲𝗰𝗸𝗹𝗶𝘀𝘁 ✅ Great for complex problems that can be broken into smaller steps. ✅ Useful when you need an explanation or audit trail of how a decision was made. ✅ When workflows involve multiple dependencies that must be followed in a defined order. ❌ Inefficient for tasks that could be done concurrently to save time. ❌ Overkill for simple tasks where a direct one-shot solution works fine. #AI #SRE #AgenticLearningSeries

Two drastically different ways enterprises are handling AI Agents for customer support — and only one actually works. THEIR WAY: - Train AI on product info and conversation history—no real-time data - Focus on routine support tasks: password resets, basic returns, store hours - Go fully autonomous, even when issues get complicated - Push self-service, often leading to dead-ends and hallucinations - Requires heavy technical expertise to customize OUR WAY: - Pull real-time customer context from CRMs, EHRs, EMRs, and more - Tackle complex use cases: returns, billing disputes, insurance claims - Offer flexibility: AI-based, logic-based, or hybrid automation, depending on risk - Cover the entire lifecycle—from self-service to agent-assist - Allow seamless human handoff—no forced autonomy where it doesn’t belong - Let business users design and modify AI Agents directly TAKEAWAY: AI Agent vendors tell you they can deflect your entire support volume. Sure—until you watch CSAT drop and revenue slip. Because they don’t capture and understand the customer context required to handle high-stakes issues. Your AI Agent can’t provide medical advice without understanding patient symptoms and medical history. It can’t approve or deny an insurance claim without policy details. If you implement AI Agents, make sure they have the context they need to make the right call. Context = Accurate automation #AI #CustomerSupport #Automation

What if building AI agents felt like writing instructions for a colleague? Creating AI agents required two scarce resources: 1. Engineering teams to write code 2. Technical oversight to debug workflows At Quantalogic, we asked: What if domain experts—not just developers—could build agents for their specific needs? 👉 Our Answer  A dual-path platform where users: - Design visually with YAML based workflows (e.g., “Customer Support Agent: Check ticket → Escalate → Log resolution”) - Guide semi-autonomous agents (ReactAgent) that “think” through tasks like a human (“Order delayed? Check inventory → Notify customer → Update CRM”) This week’s MetaChain research from Hong Kong University paper validates our approach. Their innovation—natural language-to-agent automation—mirrors our core philosophy: “The best agents aren’t coded—they’re described.” 👉 Why This Matters for Enterprises Most AI frameworks force teams to:  ❌ Hire developers to translate business logic into Python   ❌ Manually stitch APIs and data pipelines   ❌ Sacrifice transparency for automation Quantalogic flips this:  ✅ YAML based visual Workflows: Product managers design agents like flowcharts (e.g., “If inventory < threshold, trigger restock”).  ✅ ReactAgent: Balances autonomy with control—agents propose actions, users approve/reject.  ✅ MetaChain Synergy: We’re adopting their language-driven principles to let users refine agents via chat (“Make this report more visual”). 👉 Join the Shift  We’re bridging MetaChain’s academic rigor with enterprise pragmatism: - Open Beta within 3 weeks: Try our YAML + ReactAgent Studio (PM if interested). - Case Studies: How we use Agent to reverse engineer full project. Ready to turn your team’s expertise into AI—without coding? Let’s discuss.

𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬 𝐝𝐨𝐧’𝐭 𝐧𝐞𝐞𝐝 𝐦𝐨𝐫𝐞 𝐝𝐚𝐭𝐚 𝐭𝐡𝐞𝐲 𝐧𝐞𝐞𝐝 𝐛𝐞𝐭𝐭𝐞𝐫 𝐦𝐞𝐦𝐨𝐫𝐲 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞. Most agents fail not from ignorance, but from memory blindness. Design memory first, and agents become informed, consistent, and trustworthy. Five memories turn static models into adaptive, accountable digital coworkers. ↳ 𝐖𝐨𝐫𝐤𝐢𝐧𝐠 𝐦𝐞𝐦𝐨𝐫𝐲 holds current goals, constraints, and dialogue turns in play. ↳ 𝐒𝐞𝐦𝐚𝐧𝐭𝐢𝐜 𝐦𝐞𝐦𝐨𝐫𝐲 stores facts, schemas, and domain knowledge beyond single tasks. ↳ 𝐏𝐫𝐨𝐜𝐞𝐝𝐮𝐫𝐚𝐥 𝐦𝐞𝐦𝐨𝐫𝐲 captures tools, steps, and policies for repeatable execution. ↳ 𝐄𝐩𝐢𝐬𝐨𝐝𝐢𝐜 𝐦𝐞𝐦𝐨𝐫𝐲 logs situations, outcomes, and lessons from past work. ↳ 𝐏𝐫𝐞𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐦𝐞𝐦𝐨𝐫𝐲 tracks users, roles, thresholds, and exceptions that personalize actions. Insight: Separation prevents overwrites and hallucinations when contexts suddenly shift. Insight: Retrieval gates control which memories are relevant, reducing noise. Insight: Freshness scores prioritize recent episodes without erasing durable knowledge. Insight: Audit trails from episodic memory create governance and regulatory defensibility. A Manufacturing support agent forgot entitlements and unnecessarily escalated routine tickets. Adding procedural, episodic, and preference memories with retrieval gates. Resolution accuracy rose, first contact resolutions jumped, and escalations dropped dramatically. Leaders finally trusted agents because decisions referenced verifiable, auditable memories. If you deploy agents, design memory before prompts, models, or dashboards. ♻️ Repost to your LinkedIn empower your network & follow Timothy Goebel for expert insights #AIAgents #Manufacturing #Construction #Healthcare #SmallBusiness