Designing Chatbots for Task Completion Efficiency

Most people overcomplicating AI agents I’ve seen teams jump straight into frameworks and tooling before answering one basic question. What exactly should this agent do? Here’s a 10-step blueprint for building an AI agent that actually works. Whether you’re technical or non-technical, this applies. 1. Set the Objective Start with the problem, not the tech. Identify the core task, define what success looks like, and set clear boundaries. The best first agent? Automate the workflow you already do manually every single day. The boring, repetitive one. 2. Design the Core Instructions This is where most agents break. Give your agent a clear role, structured instructions, and guardrails. Think of it as writing a job description, not a prompt. If you gave these instructions to an employee, would they know exactly what to do? 3. Select the Right Model Not every task needs the most powerful model, think about context window limits, and always weigh cost against performance. Smart routing between models can cut costs by 60-70%. 4. Connect Tools & Systems An agent without tool access is just a chatbot. Integrate APIs, databases, CRMs, and automation workflows. Without tool integration, your agent stays informational instead of operational. The Model Context Protocol (MCP) is emerging as a key standard here. 5. Build Memory Capabilities Context is everything. Layer short-term conversation history, task-based working memory, and long-term storage using databases or vector stores. Agents without memory repeat mistakes 6. Add a Reasoning Layer This is what separates a basic chatbot from a real agent. This is where chain-of-thought and planning capabilities matter most. 7. Orchestrate the Workflow Define how everything connects. Managing how multiple agents communicate and maintain state is where the real complexity lives. 8. Design the User Experience A powerful agent with a bad interface is a wasted agent. 9. Test and Optimize Run functional and edge-case tests. Measure speed, accuracy, and reliability. Here’s the part most people skip: review your agent’s outputs the way you’d review a pull request. 10. Monitor and Scale This is where long-term success happens. Here’s why this matters right now: The agentic AI market is projected to hit roughly $10.8 billion in 2026, growing at over 40% annually. Gartner projects 40% of enterprise applications will include task-specific AI agents by the end of this year. And yet, only about a third of organizations have actually scaled their AI deployments beyond pilot programs. The gap between experimenting and executing is where the real opportunity. You don’t need to build the most sophisticated agent on day one. You need to build one that solves a real problem. What’s the first workflow you’d hand off to an AI agent? Follow Sufyan Maan, M.Eng. for more Join my newsletter: sufyannmaan.substack.com

The UK's Department for Business and Trade just released a 48-page evaluation of MS Copilot. Their conclusion? A generic, off-the-shelf AI chatbot isn't producing significant efficiency gains. Shocker… Here's what they found; 🔹 72% user satisfaction with basic writing and summarizing tasks 🔹 Modest time savings: ~1 hour saved on document drafting, negative time impact on scheduling and presentations 🔹 22% of users encountered hallucinations requiring fact-checking 🔹 Biggest benefits for neurodiverse users and non-native English speakers 🔹 No evidence of broader organizational productivity improvements Basically, it's a decent writing assistant. If we're expecting off-the-shelf LLMs to transform work, we're missing the point. LLMs aren't about optimizing existing workflows - they're about making work conversational. Imagine telling your procurement system: "Flag vendors with unusual pricing patterns from last 18 months" or "Generate an audit response comparing our data practices against our policy frameworks." That requires domain-specific training, system integration, and task-specific capabilities, none of which exist in off-the-shelf LLM driven copilot. Most companies are making the same mistake as the UK government. They're licensing generic AI tools and expecting productivity gains on individual tasks, when the real opportunity is building conversational interfaces to their actual business logic. To hit the nail on productivity gains with AI? 1️⃣ Start with the problem → Look for workflows where people navigate multiple systems, coordinate across functional areas, pass data back and forth, analyze it, and perform well-defined repetitive tasks. 2️⃣ Identify 1-2 specific processes and break them into testable components → Pick process you can decompose into individual tasks. Don't attempt to automate entire workflows until you've proven AI can reliably handle each component. 3️⃣ Invest in clean data, metadata, and integrations → Ensure you have the data infrastructure and system connections needed for AI to execute tasks rather than just generate text. 4️⃣ Measure each task against your hypothesis → Does it help? If all individual tasks were combined, would it provide enough gains to be worth the investment? 4️⃣ Be smart about expectations → This is emerging technology that will improve. Don't expect 100% accuracy out of the gate. The hard truth? Transforming your organization with AI requires an innovation mindset, not digital transformation. It's not about buying a tool, implementing it and seeing immediate ROI. Real transformation requires engineering investment and domain expertise. And that won't come from MS Copilot alone. The organizations that figure this out first won't be asking "Does AI save time on emails?" They'll be asking "What can we make possible when our systems can take orders in plain English?"

My AI Journey, Chapter 1: From Ambitious Goals to Tangible Impact in IT VMO A couple of years ago, our CIO laid down a challenge that truly ignited my AI journey: "50% of all IT work is AI-powered" and "Reduce employee task friction by 50%." Bold goals, right? But as Leader of IT VMO, I saw an immediate opportunity to tackle a persistent pain point that many of us in operations face. Our IT VMO team was constantly fielding the same questions from stakeholders. While we had meticulously documented answers in SharePoint, training sessions, and various forums, the sheer volume of repetitive queries was a significant manual burden. This wasn't just friction; it was a drain on our capacity to focus on strategic VMO initiatives. That's when we decided to build our own solution. Inspired by tools like Cisco IT's BridgeIT (which leveraged GPT 3.5 at the time), we developed a specialized AI chatbot for our stakeholders - VIVA (VMO Integrated Virtual Assistant). The premise was simple: stakeholders could ask questions in natural language, and our Generative AI would respond with clear, concise, and easy-to-understand answers, pulling directly from our existing knowledge base. The impact? Revolutionary. This simple chatbot has given my team back invaluable time. We've shifted from being reactive answer-providers to proactive strategic partners, focusing our expertise only on those complex matters that truly require human guidance. The numbers speak for themselves: a remarkable 60% of stakeholder questions are now answered autonomously by our AI chatbot. The remaining 40% are handled by our always-on, always-available team, who can now dedicate their energy to higher-value tasks. This isn't just a story about a chatbot; it's a living testament to how I eliminated significant manual overhead, accelerated access to information, and freed our talent to innovate. For those who fear GenAI will take away jobs, or for those who hear industry leaders say AI will enable us to do more with limited time – this is what that reality looks like. It's about augmenting human potential, not replacing it. It's about empowering teams to achieve more impactful work. This is just the first chapter in my AI journey, and I'll be sharing more insights, challenges, and successes in upcoming posts about my usage of GenAI and Agentic AI in the VMO space. What repetitive tasks are currently burdening your teams? How are you leveraging AI to transform operations and truly empower your workforce? I'd love to hear your thoughts and experiences. Let's learn from each other how we can collectively drive this AI-powered future forward. #AI #GenerativeAI #AgenticAI #ITOperations #VMO #DigitalTransformation #Efficiency #Innovation #FutureOfWork #CiscoIT #AITransformation

The secret to better AI isn't more agents - it's how they're connected. Are you familiar with these 3 powerful architectures? 𝗦𝗲𝗾𝘂𝗲𝗻𝘁𝗶𝗮𝗹 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 Instead of a master agent delegating tasks downward, sequential architectures create a pipeline where specialized agents work in sequence: • First agent performs vector search on your knowledge base • Second agent uses those results to formulate targeted web searches • Third agent synthesizes all gathered information into a comprehensive response This approach creates a 𝗻𝗼𝗻-𝗵𝗶𝗲𝗿𝗮𝗿𝗰𝗵𝗶𝗰𝗮𝗹 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 where information flows horizontally between specialized agents. Each agent builds upon the previous agent's output, similar to the ReAct framework's thought→action→observation cycle. When to use it: Perfect for complex queries requiring multiple data sources and when different tools need to be used in a specific order. 𝗦𝗵𝗮𝗿𝗲𝗱 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝘄𝗶𝘁𝗵 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗧𝗼𝗼𝗹𝘀 Rather than just retrieving information, these systems actively transform and enrich your data: • AI agents access the same database but with 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝘁𝗼𝗼𝗹𝘀 instead of search tools • Agents add attributes, generate summaries, or create metadata • Previously unsearchable information becomes discoverable This architecture leverages function calling capabilities to connect LLMs with external tools that can modify and enhance your data. When to use it: Ideal for automatically enriching large document collections or creating searchable attributes from unstructured content. 𝗠𝗲𝗺𝗼𝗿𝘆 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 This approach focuses on the 𝗺𝗲𝗺𝗼𝗿𝘆 𝗰𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁 of AI agents to create more contextually aware systems: - Agents can transform and analyze conversation history stored in vector databases • Generate summaries of past interactions (e.g., "last five conversations about project X") • Create structured analyses of user behavior patterns over time When to use it: Excellent for customer service applications, ongoing project management, or any system that benefits from understanding interaction history. Each of these architectures represents a different way to combine the core components of AI agents: LLMs for reasoning, tools for task completion, and memory for learning from past experiences. Want to learn more? Get your free ebook: https://lnkd.in/e_Exuwh5 𝘗.𝘚. 𝘍𝘪𝘯𝘥 𝘱𝘢𝘳𝘵 1 𝘰𝘧 𝘵𝘩𝘪𝘴 𝘱𝘰𝘴𝘵 𝘩𝘦𝘳𝘦: https://lnkd.in/eaYYEckG

Just finished reading an amazing book: AI Engineering by Chip Huyen. Here’s the quickest (and most agile) way to build LLM products: 1. Define your product goals Pick a small, very clear problem to solve (unless you're building a general chatbot). Identify use case and business objectives. Clarify user needs and domain requirements. 2. Select the foundation model Don’t waste time training your own at the start. Evaluate models for domain relevance, task capability, cost, and privacy. Decide on open source vs. proprietary options. 3. Gather and filter data Collect high-quality, relevant data. Remove bias, toxic content, and irrelevant domains. 4. Evaluate baseline model performance Use key metrics: cross-entropy, perplexity, accuracy, semantic similarity. Set up evaluation benchmarks and rubrics. 5. Adapt the model for your task Start with prompt engineering (quick, cost-effective, doesn’t change model weights): craft detailed instructions, provide examples, and specify output formats. Use RAG if your application needs strong grounding and frequently updated factual data: integrate external data sources for richer context. Prompt-tuning isn’t a bad idea either. Still getting hallucinations? Try “abstention”—having the model say “I don’t know” instead of guessing. 6. Fine-tune (only if you have a strong case for it) Train on domain/task-specific data for better performance. Use model distillation for cost-efficient deployment. 7. Implement safety and robustness Protect against prompt injection, jailbreaks, and extraction attacks. Add safety guardrails and monitor for security risks. 8. Build memory and context systems Design short-term and long-term memory (context windows, external databases). Enable continuity across user sessions. 9. Monitor and maintain Continuously track model performance, drift, evaluation metrics, business impact, token usage, etc. Update the model, prompts, and data based on user feedback and changing requirements. Observability is key! 10. Test, Test, Test! Use LLM judges, human-in-the-loop strategies; iterate in small cycles. A/B test in small iterations: see what breaks, patch, and move on. A simple GUI or CLI wrapper is just fine for your MVP. Keep scope under control—LLM products can be tempting to expand, but restraint is crucial! Fastest way: Build an LLM optimized for a single use case first. Once that works, adding new use cases becomes much easier. https://lnkd.in/ghuHNP7t Summary video here -> https://lnkd.in/g6fPsqUR Chip Huyen, #AiEngineering #LLM #GenAI #Oreilly #ContinuousLEarning #ProductManagersinAI