How to Build a Strong AI Infrastructure

I’ve had the chance to work across several #EnterpriseAI initiatives esp. those with human computer interfaces. Common failures can be attributed broadly to bad design/experience, disjointed workflows, not getting to quality answers quickly, and slow response time. All exacerbated by high compute costs because of an under-engineered backend. Here are 10 principles that I’ve come to appreciate in designing #AI applications. What are your core principles? 1. DON’T UNDERESTIMATE THE VALUE OF GOOD #UX AND INTUITIVE WORKFLOWS Design AI to fit how people already work. Don’t make users learn new patterns — embed AI in current business processes and gradually evolve the patterns as the workforce matures. This also builds institutional trust and lowers resistance to adoption. 2. START WITH EMBEDDING AI FEATURES IN EXISTING SYSTEMS/TOOLS Integrate directly into existing operational systems (CRM, EMR, ERP, etc.) and applications. This minimizes friction, speeds up time-to-value, and reduces training overhead. Avoid standalone apps that add context-switching or friction. Using AI should feel seamless and habit-forming. For example, surface AI-suggested next steps directly in Salesforce or Epic. Where possible push AI results into existing collaboration tools like Teams. 3. CONVERGE TO ACCEPTABLE RESPONSES FAST Most users have gotten used to publicly available AI like #ChatGPT where they can get to an acceptable answer quickly. Enterprise users expect parity or better — anything slower feels broken. Obsess over model quality, fine-tune system prompts for the specific use case, function, and organization. 4. THINK ENTIRE WORK INSTEAD OF USE CASES Don’t solve just a task - solve the entire function. For example, instead of resume screening, redesign the full talent acquisition journey with AI. 5. ENRICH CONTEXT AND DATA Use external signals in addition to enterprise data to create better context for the response. For example: append LinkedIn information for a candidate when presenting insights to the recruiter. 6. CREATE SECURITY CONFIDENCE Design for enterprise-grade data governance and security from the start. This means avoiding rogue AI applications and collaborating with IT. For example, offer centrally governed access to #LLMs through approved enterprise tools instead of letting teams go rogue with public endpoints. 7. IGNORE COSTS AT YOUR OWN PERIL Design for compute costs esp. if app has to scale. Start small but defend for future-cost. 8. INCLUDE EVALS Define what “good” looks like and run evals continuously so you can compare against different models and course-correct quickly. 9. DEFINE AND TRACK SUCCESS METRICS RIGOROUSLY Set and measure quantifiable indicators: hours saved, people not hired, process cycles reduced, adoption levels. 10. MARKET INTERNALLY Keep promoting the success and adoption of the application internally. Sometimes driving enterprise adoption requires FOMO. #DigitalTransformation #GenerativeAI #AIatScale #AIUX

I taught myself how to build AI agents from scratch Now I help companies deploy production-grade systems These are my favorite resources to set you up on the same path: (1) Pick the Right LLM Choose a model with strong reasoning, reliable step-by-step thinking, and consistent outputs → Claude Opus, Llama, and Mistral are great starting points, especially if you want open weights. (2) Design the Agent’s Logic Decide how your agent thinks: should it reflect before acting, or respond instantly?How does it recover when stuck? → Start with ReAct or Plan–then–Execute: simple, proven, and extensible. Start with ReAct or Plan–then–Execute (3) Write Operating Instructions Define how the agent should reason, when to invoke tools, and how to format its responses. → Use modular prompt templates: they give you precise control and scale effortlessly across tasks. (4) Add Memory Your agent needs continuity — not just intelligence. → Use structured memory (summaries, sliding windows, or tools like MemGPT/ZepAI) to retain what matters and avoid repeating itself. (5) Connect Tools & APIs An agent that can’t do anything is just fancy autocomplete. → Wire it up to real tools and APIs and give it clear instructions on when and why to use them. (6) Give It a Job Vague goals lead to vague results. → Define the task with precision. A well-scoped prompt beats general intelligence every time. (7) Scale to Multi-Agent Systems The smartest systems act an ensembles. → Break work into roles: researcher, analyst, formatter. Each agent should do one thing really well. The uncomfortable truth? Builders ship simple agents that work. Dreamers architect complex systems that don't. Start with step 1. Ship something ugly. Make it better tomorrow. What's stopping you from building your first agent today? Repost if you're done waiting for the "perfect" agent framework ♻️ Image Credits – AI Agents power combo: Andreas Horn & Rakesh Gohel

Your AI journey shouldn’t start with models. I’ve helped several enterprises avoid one of the biggest AI pitfalls: → Jumping straight to advanced models before building a strong foundation. Instead, we follow a proven "Crawl → Walk → Run" framework  to help you scale Enterprise AI Automation the right way. Here’s how it works👇🏻 𝗣𝗵𝗮𝘀𝗲 1: 𝗖𝗿𝗮𝘄𝗹 – 𝗕𝘂𝗶𝗹𝗱 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 Start with low-cost, low-risk projects.  Your goal? Learn fast and build core capabilities. ✅ Automate routine tasks using RPA (invoice processing, data entry) ✅ Organize and clean your data for downstream AI use 📌Key Insight: Don’t chase ROI yet. Chase readiness. Train teams. Prove small wins. 𝗣𝗵𝗮𝘀𝗲 2: 𝗪𝗮𝗹𝗸 – 𝗔𝗱𝗱 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲, 𝗦𝘁𝗮𝗿𝘁 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 Once your foundation is solid, step into mid-complexity AI with moderate investment. ✅ Predictive Maintenance: Reduce equipment failures with ML ✅ AI Chatbots: Improve CX while lowering support costs 📌Key Insight: Let technical and business teams work closely together. Use real learnings from the crawl phase to guide decisions. 𝗣𝗵𝗮𝘀𝗲 3: 𝗥𝘂𝗻– 𝗗𝗿𝗶𝘃𝗲 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲-𝗪𝗶𝗱𝗲 𝗜𝗺𝗽𝗮𝗰𝘁 Now you’re ready for high-stakes, high-reward transformation. ✅ Personalization Engines: Tailored experiences = loyal customers ✅ Executive Decision Support: Fast insights for strategic calls 📌Key Insight: Establish strong governance. Track ROI. Let AI shift from a support role to a strategic driver. Skipping these foundations breaks momentum. This approach is sustainable, and that’s how real AI transformation happens. Curious where your organization stands in this journey? Let’s connect… happy to share how we approach this at Pronix Inc #AIAutomation #AutomationStrategies #PronixInc