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

A lot of people think the toughest part about deploying AI agents in enterprise environments is to figure out the best model to use - OpenAI vs Claude vs DeepSeek. Completely wrong. We have worked with top enterprises and multiple public companies to deploy AI support agents, and here’s what we’ve learned: the real question isn’t whether AI can automate support, it’s how to make AI work effectively in the complex, human-centric world of enterprise operations. Yesterday, I was on a call with the Senior VP of Operations for a company handling 4 million annual support issues, and the top questions were: 1. How do we test and monitor the AI at scale? What will effective QA from humans look like? 2. What are the guardrails in the model? Will the AI self-QA before the humans have to QA? 3. What's the workflow to manage the knowledge - can the AI go and update our knowledgebase when it learns new topics? 4. How do we design a hybrid support model so that AI<>Humans can collaborate depending on who is best equipped to respond 6. Most importantly, how do you integrate AI agents into complex enterprise systems without disrupting workflows? - Zendesk + Confluence + Notion + Slack These aren’t just technical challenges, they’re operational and strategic challenges that require deep expertise in both AI and customer experience. The future of AI in customer support isn’t just about the models themselves. While foundational AI infrastructure will inevitably become commoditized (Welcome DeepSeek AI), the real value lies in application layer - the tools and systems that bring AI agents to life and deliver real value in the messy, hybrid environments of large enterprises, with minimal changes. At Fini, we’re building the future of AI-driven support by tackling these questions head-on and delivering real value for our enterprise customers. Out platform makes it dead easy for enterprises to self-deploy, and let their CX teams manage AI<>Human collaboration. The future of customer support is here, and it’s hybrid. Let’s build it together.

Building an AI Agent with Memory A longstanding challenge in #AI research has been how to enable machines to “remember” context and knowledge over extended interactions. Today’s Large Language Models (#LLMs) offer impressive language capabilities, but they still need robust strategies for short-term and long-term memory to evolve into truly adaptive, context-aware systems. Here’s a straightforward approach: 1️⃣ Clarify the Agent’s Purpose Anchor your design on the agent’s end goal—whether it’s a scheduling assistant or a support bot—and detail the nature of user queries, knowledge domains, and data flows. 2️⃣ Select the Right LLM Evaluate models such as OpenAI #GPT, Google #Gemini, Anthropic #Claude, or Meta #LLaMA based on your application requirements. An LLM with strong “reasoning” in your domain will reduce the need for extensive fine-tuning. 3️⃣ Plan Memory Requirements Short-term memory handles the immediate conversation context, crucial for continuity. 🔺 Long-term memory accumulates persistent knowledge about users or tasks. 🔺 Both are essential for coherent, personalized responses. 4️⃣ Leverage a Memory Framework Memory management can be nontrivial. Open-source or managed solution streamlines how your agent stores and retrieves conversation logs, domain facts, or user profiles. 5️⃣ Model User Profiles and Sessions Distinguish different users via unique IDs and maintain session continuity. This eliminates the need for repetitive reintroduction of context in back-and-forth interactions. 6️⃣ Design a Workflow for Memory Updates Define systematic protocols for storing new facts, referencing past data, and deciding what is relevant to each user query. Automation here can significantly reduce retrieval errors. 7️⃣ Enrich with a Knowledge Graph Beyond plain text memory, a knowledge graph encodes structured relationships among entities. This approach often leads to more reliable and interpretative reasoning. 8️⃣ Develop Dynamic Prompts Integrate memory content into prompts so that the LLM receives relevant details at the right time. Always consider security and privacy constraints in your retrieval logic. 9️⃣ Implement Data Security Whether you store data locally or in the cloud, adopt robust encryption and role-based access to ensure confidential information remains protected. 🔟 Iterate, Monitor, and Scale Continuously measure performance via logs and user feedback. As your system expands, refine memory mechanisms to accommodate more data and more complex interactions. Building AI agents with genuine long-term knowledge and situational awareness requires layering robust memory systems on top of powerful LLMs. By explicitly planning memory architectures, leveraging open-source tools, and adopting a structured data approach, you can create more capable, user-centric AI systems that learn and adapt over time. #agent #agenticai #genai #graph #machinelearning REF: Level Up Coding

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

Cursor’s AI support bot has influencers freaking out. Users couldn’t log into their accounts on multiple devices. Cursor’s LLM-powered support said it was company policy to allow only one device per license, but that policy doesn’t exist. Hallucinations are common with LLMs, and there’s a simple solution. LLM answers must be grounded in source documentation, knowledge graphs, or tabular data. A fundamental guardrail design pattern for agents fixes this, so there’s no reason to freak out. Once the LLM provides an answer, a round of checks must run to verify it. In this case, a similarity score would have revealed that the support bot’s answer wasn’t a close match to any passage in a company policy document. Salesforce and many other companies use similarity scoring to prevent hallucinations from seeing the light of day. Deterministic guardrails are critical design elements for all agents and agentic platforms. Another best practice is using small language models (SLMs) that are post-trained on domain or workflow-specific data (customer support questions and answers in this case). LLMs are more prone to hallucinations than SLMs. AI product managers and system architects work together during the agent design phase to scenario plan failure cases and specify the guardrails that will mitigate the most significant risks. It’s agentic design 101 and has been part of my instructor-led AI product management course for almost a year. Cursor’s AI customer support agent is poorly designed, but the influencer freak-out and media attention it attracted are just more proof that most of these people aren’t actively working in the field. #AI #ProductManagement

This is how Adyen built an LLM-based ticket routing + support agent copilot to increase the speed of their support team. - Adyen used LangChain as the primary framework. The entire setup runs on Kubernetes for flexibility and scalability. - First, the ticket routing system uses an LLM to automatically direct support tickets to the right agents based on content analysis. This improved the accuracy in ticket allocation compared to their human operators. - For the support agent copilot, Adyen built a document management and retrieval system. It uses vector search to retrieves relevant docs from their internal support documentation and suggests answers to support agents, which cuts down the response time significantly. - The architecture is modular so their existing microservices are integrated easily too. Link to article: https://lnkd.in/gqUZZ6nd #AI #RAG #LLMs

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