Want a clear path from AI buzzwords to business results? We walk through a practical executive framework for building and deploying agents that actually move the needle. Instead of drowning in technical detail, we focus on what matters: memory that persists, reasoning loops that plan and adapt, and tool integrations that touch the systems where value is created.

TLDR / At a Glance:

• executive mental model for agent strategy
• working memory versus episodic memory with RAG
• step-by-step RAG example using BYOD policy
• traditional RAG versus agentic RAG adaptability
• fine-tuning as semantic memory and trade-offs
• prompt engineering structure, guardrails and tools
• rule of thumb for choosing methods
• reasoning loops ReAct and perceive-think-act-learn
• task decomposition, planning and exception handling
• API integration, orchestration and real-time adaptation
• leaders’ role in architecting capabilities to outcomes

We start by demystifying memory. Short-term working memory keeps conversations coherent, while episodic memory via retrieval augmented generation anchors responses in live, organisation-specific data. Using a concrete BYOD policy example, we show how semantic search, vector embeddings, and augmented prompts reduce hallucinations and boost accuracy. Then we contrast traditional RAG with agentic RAG, where autonomous agents iterate questions, switch data sources, and ask for clarification to get the right context before acting.

From there, we unpack fine-tuning as semantic memory that embeds domain expertise, including the trade-offs around cost, maintenance, and catastrophic forgetting. We pair that with prompt engineering you can use today: define persona, objectives, tools, constraints, and output format to shape reliable behaviour without new infrastructure. Our rule of thumb keeps choices simple—start with prompts, add RAG or function calling for freshness and depth, and fine-tune when specialisation is essential.

Finally, we get practical about execution. ReAct loops and the broader perceive-think-act-learn model enable agents to decompose tasks, plan across constraints, handle exceptions, and learn from outcomes. The payoff arrives when agents connect to your stack through APIs, orchestrate across CRM, ERP, payments, and messaging, and adapt to real-time data. Leaders don’t need to code chips; they need to architect systems that combine memory, planning, and tools into a consistent methodology. Subscribe, share with a colleague who leads transformation, and leave a review telling us which workflow you’ll automate first.

Want some free book chapters? Then go here How to build an agent - Kieran Gilmurray

Want to buy the complete book? Then go to Amazon or Audible today.

Support the show

𝗖𝗼𝗻𝘁𝗮𝗰𝘁 my team and I to get business results, not excuses.

☎️ https://calendly.com/kierangilmurray/results-not-excuses
✉️ kieran@gilmurray.co.uk
🌍 www.KieranGilmurray.com
📘 Kieran Gilmurray | LinkedIn
🦉 X / Twitter: https://twitter.com/KieranGilmurray
📽 YouTube: https://www.youtube.com/@KieranGilmurray

📕 Want to learn more about agentic AI then read my new book on Agentic AI and the Future of Work https://tinyurl.com/MyBooksOnAmazonUK

The hype is loud, but the results are quiet—and that’s the paradox we set out to crack. We make the case that real ROI shows up when AI stops answering and starts acting. By drawing a sharp line between chatbots and autonomous agents, we walk through the four capabilities that matter—memory, reasoning, deep integration, and execution—and show how they compound into speed, accuracy, and measurable outcomes across the business.

TLDR / At A Glance

• the platform shift from chatbots to agents
• the four capabilities of agents: memory, reasoning, integration, execution
• the agent loop: perceive, think, act, learn
• the move from tool trials to agent hives and redesigned work
• measured impact on satisfaction, cost, and cycle time
• new models: Agent-as-a-Service, autonomous processes, embedded intelligence
• first-mover advantages in data, CX, efficiency, and talent
• a practical path to start with one outcome and iterate

We break down the agent loop in plain language: how Perceive goes beyond prompts to a live map of your data, how Think plans steps and tools, how Act connects to your CRM, ERP, and comms stack, and how Learn closes the loop so performance improves over time. Then we zoom out to the bigger arc: today’s iPhone moment sets the stage for an App Store-style wave where agent hives coordinate entire workflows—from trend scanning to content creation to orchestration—while humans step up to review and risk-manage.

You’ll hear concrete proof points and emerging playbooks. We highlight customer service where autonomous resolution now handles the bulk of routine issues, the 60–90% cycle-time cuts when handoffs disappear, and the cost savings that fund faster innovation. We explore new business models—Agent-as-a-Service, autonomous processes in logistics and real estate, and embedded intelligence in industrial gear and software—alongside the competitive dynamics that reward early movers with better data, stronger partnerships, and top talent.

If you’re choosing where to begin, we share a simple path: pick one high-volume, rules-heavy workflow, expose the right data, set guardrails for autonomy, and instrument the learn step from day one. The window for first-mover advantage is shrinking by the month. Subscribe, share this with a leader who needs it, and leave a review with the one workflow you’d hand to an agent tomorrow.

Want some free book chapters? Then go here How to build an agent - Kieran Gilmurray

Want to buy the complete book? Then go to Amazon or Audible today.

Support the show

𝗖𝗼𝗻𝘁𝗮𝗰𝘁 my team and I to get business results, not excuses.

☎️ https://calendly.com/kierangilmurray/results-not-excuses
✉️ kieran@gilmurray.co.uk
🌍 www.KieranGilmurray.com
📘 Kieran Gilmurray | LinkedIn
🦉 X / Twitter: https://twitter.com/KieranGilmurray
📽 YouTube: https://www.youtube.com/@KieranGilmurray

📕 Want to learn more about agentic AI then read my new book on Agentic AI and the Future of Work https://tinyurl.com/MyBooksOnAmazonUK

Forget the neat headline that blames ChatGPT for a white-collar collapse. We stack three heavyweight datasets - occupation-level unemployment risk, 10.5 million LinkedIn profiles, and three million university syllabi - to test the timeline and the tale unravels.

The spike in risk for AI-exposed roles began in early 2022, months before the public touched the tool. Around launch, risk stabilised. The more convincing culprits are old-school: rising interest rates and a pandemic hiring binge that needed a hard correction.

My Google Notebook LM bots pull apart the clean “AI killed white-collar work” story and test it against unemployment risk, 10.5 million LinkedIn profiles, and three million university syllabi. The timeline breaks the myth, and the data points to macroeconomics and the power of complementarity.

At a Glance / TLDR:

• Unemployment risk for AI-exposed jobs rising in early 2022, not after ChatGPT
• Why risk stabilised around launch and the Connecticut outlier
• Monetary tightening and post-pandemic overhiring as key drivers
• Graduate outcomes from 2021–2022 cohorts across tech and other high-paying fields
• Syllabi analysis showing AI-exposed skills correlating with higher pay post-launch
• Complementarity over replacement and the shift from generation to judgment
• Practical guidance on learning core skills and using AI to amplify them

We follow the canaries next: recent grads. If AI erased entry-level tasks, the classes of 2021 and 2022 should be uniquely punished in tech. Instead, we see a broader white-collar chill hitting finance, consulting, and other high-paying tracks at the same time. This isn’t an AI-specific rejection; it’s a tight, risk-averse market trimming junior headcount across the board. That context matters for anyone trying to read their prospects or redesign a hiring plan.

The real twist comes from the classroom. By matching course learning objectives—coding, synthesis, argument evaluation—to outcomes, we see that students with higher exposure to AI-performable tasks fared better after late 2022. Not worse. Why? Complementarity. AI doesn’t replace good writers and engineers; it multiplies them. Give Copilot to someone who understands architecture and they ship faster and cleaner. Give it to a novice and you get confident chaos. The value has shifted from generation to judgment: specifying, verifying, and integrating outputs with real-world constraints.

We end with clear takeaways. Stop misdiagnosing a macro downturn as a machine takeover. Double down on foundations—code structure, data modelling, rhetoric, editorial standards—and pair them with modern tools to raise your personal ceiling. If you’re a leader, design roles and training for verification and integration, not just production. If you’re a learner, build projects that prove leverage, not just fluency. Subscribe for more data-driven deep dives, share this with a friend who’s rethinking their career, and leave a review to tell us which skill you plan to sharpen next.

Link to research: AI-exposed jobs deteriorated before ChatGPT

Support the show

𝗖𝗼𝗻𝘁𝗮𝗰𝘁 my team and I to get business results, not excuses.

☎️ https://calendly.com/kierangilmurray/results-not-excuses
✉️ kieran@gilmurray.co.uk
🌍 www.KieranGilmurray.com
📘 Kieran Gilmurray | LinkedIn
🦉 X / Twitter: https://twitter.com/KieranGilmurray
📽 YouTube: https://www.youtube.com/@KieranGilmurray

📕 Want to learn more about agentic AI then read my new book on Agentic AI and the Future of Work https://tinyurl.com/MyBooksOnAmazonUK