How to Build AI Agents: A 10-Step Roadmap

A new MIT study found that 95% of businesses using AI aren't seeing a ROI. Yet the bravest thing a CEO can say about AI right now is, "Not yet”. I’ve been hands-on with AI since 2019 via what at that time was known as EBC Connect - an AI/ML arm to extend EBC’s core search. At that time AI was early. Now it's noisy. How do you separate signal from noise? I’ve spoken with dozens of CEOs, leaders and the like about AI and saw two patterns: The hype: "We'll adopt AI across all functions." (Otherwise known as ‘No specific use case’!) "It will bring massive cost savings overnight." (Ignores training and integration costs) "Our competitors are doing it, so must we." (FOMO isn't strategy…) "AI will replace 30% of staff this year." (Headline chasing) The real play: *Start with one friction-heavy workflow and measure uplift. *Build a small cross-functional squad: domain experts + data engineers + change-management lead. *Treat LLMs as copilots, keep a team of experts in the loop. *Set guardrails early: data privacy, model monitoring, ethical review. Define a 12-month ROI checkpoint before scaling. Takeaway? Vision isn't about bold AI "transformation." We’ve learned it’s about disciplined experiments that solve one real problem, prove value, then expand. This year at EBC, part of our AI roadmap is our focus on “20 to 2”: JDs, scheduling, interview transcription, advertising, ATS hygiene, proof reading … the non-high human touch elements each to be cut from roughly 20-minutes to 2-minutes. If your company is betting on AI, I’d love to hear what’s worked, and what hasn’t.

𝐀𝐈-𝐃𝐫𝐢𝐯𝐞𝐧 𝐏𝐫𝐨𝐦𝐩𝐭 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐈𝐭𝐬 𝐈𝐦𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐟𝐨𝐫 𝐏𝐫𝐨𝐟𝐞𝐬𝐬𝐢𝐨𝐧𝐚𝐥 𝐑𝐨𝐥𝐞𝐬 🔹 _Advances in AI and machine learning are revolutionizing how prompts are refined automatically._ AI systems analyze vast datasets of prompt-response pairs to continuously optimize for **accuracy, coherence, and personalization**. This ongoing self-improvement enables models like GPT-4 and Google’s Gemini to better grasp _user intent, sentiment_, and _domain-specific contexts_. 🔹 _For professionals in prompt-intensive roles,_ such as customer service agents, content creators, and decision support analysts, these technologies empower faster delivery of **nuanced and relevant outputs.** This pushes boundaries in automation quality, enabling more sophisticated interactions and insights. 🔹 📈 Case studies demonstrate that **structured prompt engineering can yield up to 340% higher ROI** compared to ad-hoc prompting efforts. This underscores the value of deliberate prompt design and refinement for maximizing AI’s impact in business workflows. 🔹 Practical AI-powered tools now support prompt management by enabling teams to store, organize, and optimize prompts collaboratively, improving consistency and scaling effectiveness across projects. 🔹 _As AI models evolve, so too must professional skillsets._ Adapting to AI-augmented workplaces involves mastering prompt design strategies, understanding model behaviors, and developing interdisciplinary collaboration skills to leverage AI responsibly and efficiently. --- Embracing AI-driven prompt optimization is not just about mastering technology—it's about transforming professional roles to unlock new levels of productivity and innovation in today's AI-powered economy.

Everyone wants AI to do their data analysis for them. But here’s the uncomfortable truth: If you don’t understand data analysis, AI will only make your mistakes faster. Last week, I joined a webinar on “AI for Data.” It was marketed as an analytics class for ANYBODY who uses data, is pivoting to analytics or wants to do the analysis themselves. Class started, and the coach jumped into prompts, the tools and automation, but skipped the foundation: - Cleaning messy data. - Knowing which questions matter. - Understanding the business context. Without that, AI becomes dangerous. It’s like giving a sports car to someone who’s never learned to drive. Here’s the TRUTH: If you do know the fundamentals, AI multiplies your speed and insight. So before you lean on AI, ask yourself: Do I understand the basics well enough to catch when it’s wrong? Because in data, the real risk isn’t AI replacing us.  It’s us replacing fundamentals with shortcuts. Do you agree? Or do you think AI will make fundamentals irrelevant?

Trust is the foundation of every relationship, even with AI. Here are the layers we can’t skip if we want AI safe, useful, and human-centered. Scaling models is easy. Scaling trust is not. Here’s how the stack really works ⬇️ 🔹 Foundation Models (LLMs, Vision, Multimodal) ↳ Powerful, but fragile on their own. ↳ Think of them as raw ingredients, useful, but not the final dish. 🔹 Memory & Context ↳ Short-term memory isn’t enough. ↳ Leaders need ways to help AI “remember” across conversations and decisions. 🔹 Tools & Plugins ↳ Where AI actually does things, calling APIs, searching data, running tasks. ↳ Too much access without control = chaos instead of value. 🔹 Planning & Orchestration ↳ A single prompt gives you a single task. ↳ Orchestration lets agents break big problems into steps and coordinate tools. 🔹 Governance & Guardrails ↳ Monitoring, approval gates, and risk checks keep systems honest. ↳ Guardrails don’t kill innovation, they build confidence to scale it. 🔹 Safety & Alignment ↳ AI must reflect your values, not just probabilities. ↳ Ignore this, and you lose both trust and reputation. 🔹 Human Oversight & Feedback Loops ↳ Humans bring judgment, ethics, and accountability. ↳ Feedback keeps the system learning and improving over time. Miss a layer and the stack breaks. Build them together, and you create AI that’s powerful, trusted, and scalable. Reflection: Which layer do you see organizations underinvesting in most today? 🔁 Repost to help more leaders see the full picture of AI trust. 👤 Follow Gabriel Millien for more AI transformation frameworks. Infographic credit: Brij kishore Pandey

The challenge faced today is how risk mitigation governance and controls can be applied at each Agentic layer, depending on the industry and use-case.

As you ingrain AI in your environment have you taken into account all Agentic AI layers? It may feel like the wild wild west at times but as the responsible executive or company owner remember to bring structure to the AI you are building and don't skip any of these critical layers. #AI #AgenticAI #innovation #LLM #RAG #Knowledgebase #ethicsandsafety #interationinterface #externalinteractions #operationalindependence #governanceandauditability

Right now, most organizations are using artificial intelligence to shave time off admin. Large language models are drafting emails, summarizing documents, automating customer support. These tools are helpful, but they barely touch the surface of what AI is capable of. The real opportunity for businesses integrating AI lies not only in automation, but also in simulation - using it not just to do things faster, but to make smarter decisions before they are made. AI can now model real-world complexity, test strategy, and forecast the likely impact of decisions - before they’re made. That changes the nature of leadership: from acting quickly to acting wisely. Too often, major decisions in both business and government are based on historic data, small-scale polling or executive instinct. But in a fast-changing world, that is no longer enough. I saw this during the Covid-19 pandemic where even in Downing Street, critical decisions were made with patchy data and too much uncertainty. Business leaders face the same risks today when making high-stakes calls - made with tools not designed for speed or complexity. Benefits of simulation Simulation offers a different, more exciting path. AI can now function in effect as a strategic rehearsal room; a digital environment where pricing decisions, product changes, or policy proposals can be tested against realistic, data-rich models of how people are likely to respond. This means fewer surprises, better timing, and more confident delivery.u Uhfvbd v.

Last year, we explored how AI and ML were reshaping business intelligence — turning static reports into predictive, action-driven insights. The conversation centered on automation, anomaly detection, and smarter forecasting, with AI-powered BI driving measurable revenue gains and cost reductions.  Now, in 2025, the stakes are even higher. AI isn’t just about crunching numbers — it’s about guiding strategy, predicting market shifts, and even shaping business models. But here’s the question: are we harnessing AI to truly see around corners, or just generating prettier dashboards faster?  Because in today’s data-driven world, speed without strategy is just noise. How is your organization using AI in BI differently this year? https://lnkd.in/d5S8mDHr

Because of brilliant insights from Russell Parrott I'm totally focused on AI failure cases and not just AI use cases. We should be focusing on failure attribution for our agentic AI products. The more we integrate multiple agents with external databases, tools, and memory modules, 𝐭𝐡𝐞 𝐦𝐨𝐫𝐞 𝐰𝐞 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞 𝐬𝐲𝐬𝐭𝐞𝐦 𝐟𝐫𝐚𝐠𝐢𝐥𝐢𝐭𝐲. 💥 I'm going to create a new AI role - Failure Analyst/Auditor. How about Chief Failure Officer to implement "Failure Mode and Effects Analysis" so organizations can identify potential failure points in products or processes 𝐛𝐞𝐟𝐨𝐫𝐞 𝐭𝐡𝐞𝐲 𝐨𝐜𝐜𝐮𝐫? What the PDF teaches us: 💠 Accurate failure attribution plays a critical role for system debugging, data efficiency, and grounded self-improvement. ✅ System debugging - by enabling agentic systems to perform effective self-debugging iteratively to enhance performance. ✅ Data efficiency - it uses "failed agent trajectories" to build more informative training data. ✅ Grounded self-improvement - to create self-correction through agent credit assignments. This cannot be left to humans who have to continuously analyze verbose audit logs. For my non-technical friends, the simple suggestion is to build AI agents that automatically learn how to attribute failure. Let AI identify the decisive error step responsible for system failure. Which begs the question - is this something we want to delegate responsibility for? Russell Parrott posted that "when tested, AI systems fail." What do we attribute this failure to? 💡 Check out Russell's post https://lnkd.in/e_W-Zg3j for a great discussion on Untrustworthy AI. My Six Sigma friends will understand how I feel about AI failures. I want to start a whole new practice on Failure Auditing and help teams compile failure lists and measure failure impacts for every step in the AI lifecycle. Please also follow Prof. Hernan Huwyler, MBA CPA, Logical AI Governance and Aishwarya Naresh Reganti because they also support similar types of analysis through Continuous Monitoring, Continuous Calibration and Continuous Correction efforts. They are truly amazing AI resources!