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

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✉️ kieran@gilmurray.co.uk
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🦉 X / Twitter: https://twitter.com/KieranGilmurray
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📕 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.

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

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✉️ 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

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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

This World Economic Forum white paper explores how the intersection of artificial intelligence and workforce readiness will transform the global labor market by 2030.

It presents four distinct future scenarios ranging from "Supercharged Progress," where humans and machines thrive together, to "The Age of Displacement," where rapid automation overwhelms social systems. While business executives anticipate productivity gains and increased profit margins, there are significant concerns regarding job loss and stagnant wages.

To navigate these uncertainties, the report suggests "no-regret" strategies, such as prioritizing human-AI collaboration and integrating lifelong learning into corporate cultures.

Ultimately, the document serves as a strategic roadmap for leaders to align technological investment with human capital development to ensure long-term economic resilience.

TLDR / At A Glance:

• executive expectations of displacement and weak wage growth
• the two axes: AI speed and workforce readiness
• scenario one: supercharged progress with inequality risk
• scenario two: rapid automation and concentrated power
• scenario three: co‑pilot economy and hybrid roles
• scenario four: stalled progress and a bifurcated market
• no‑regret strategies for alignment, augmentation, foresight, culture, and multigenerational teams
• the policy question of distributing productivity gains

The ground under work is shifting, and not because algorithms woke up one morning smarter than us. The real pivot is whether people, teams, and institutions are ready to turn AI from a cost-cutter into a capability multiplier. We unpack a clear framework built on two volatile forces - the speed of AI progress and the readiness of the workforce - to show how four distinct futures could shape jobs, wages, and power by 2030.

We start by confronting a stark survey signal: most executives expect AI to boost profit margins while leaving wages flat, with more jobs displaced than created. From there we explore what happens when exponential breakthroughs meet a prepared workforce—supercharged growth with rising inequality risk and what unfolds when the same breakthroughs collide with skills gaps rapid automation, historic drops in confidence, and power concentrated in firms that control foundational models. Then we shift to slower, steadier paths: a co‑pilot economy where augmentation is normal, more than 40% of skills evolve, and hybrid roles thrive; and a stalled progress scenario where tools improve but readiness lags, displacement hits routine roles, and skilled trades gain value through scarcity.

Along the way, we share practical moves leaders can make now: align technology and talent strategies, prioritise human–AI collaboration over blunt automation, use predictive analytics to forecast skills, strengthen culture and ethical guardrails to build trust, and design multigenerational learning teams that pair domain veterans with AI‑native talent. The throughline is simple and urgent: the difference between abundance and fracture is human readiness, not model size.

If this conversation sharpened your thinking

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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 2025 World Economic Forum white paper argues that human-centric skills, such as creative thinking, emotional intelligence, and resilience, have become the primary "hard currency" of the modern workforce.

While artificial intelligence and automation continue to transform technical tasks, these unique human attributes drive the essential innovation and adaptability required for economic growth.

Despite their high value, the report notes a significant gap where these capabilities are rarely explicitly mentioned in job descriptions or systematically measured by educational systems.

To address this, the document proposes a global framework to improve how these "durable" yet fragile skills are developed, assessed, and credentialed.

The text concludes with real-world case studies demonstrating how organizations are successfully integrating human-focused training into their professional ecosystems. Together, these findings highlight that the ultimate competitive advantage in a digital age is the cultivation of human potential.

TLDR / At a Glance:

• WEF forecasts on skill disruption and role shifts
• Why human judgment frames problems and value
• The four core groups of human-centric skills
• Market signalling gaps in job ads and hiring
• Education shortfalls in teaching and assessing SEL
• Regional strengths and the global weakness in curiosity
• Post-pandemic fragility and timelines to rebuild skills
• Automation resilience of empathy, creativity, leadership
• The recognition paradox inside organisations
• A playbook for assessment, development, credentialing
• Case studies: AI simulations and digital badges
• Guardrails against cognitive offloading to AI

In a world where AI can draft code, write reports, and analyse oceans of data tempts us to believe the smartest career move is more tech, faster.

We take a different bet: the skills that compound over time and resist automation are the human ones we were taught to call soft. Creativity that reframes problems. Curiosity that hunts for better questions. Emotional intelligence that steadies teams through uncertainty. Communication and leadership that turn analysis into action.

Drawing on fresh evidence from the World Economic Forum and real workplace data, we map the new skill economy: 40% of job skills are set to change within five years, yet the capabilities that convert technology into business outcomes are under-signalled in job ads and under-taught in classrooms.

Google AI agents and the WEF break the human skill set into four clear groups -creativity and problem solving, emotional intelligence, collaboration and communication, and learning and growth and explain why each one acts as a force multiplier for AI. You will hear why empathy and leadership have low potential for AI transformation, why curiosity is the global weak point, and how post-pandemic atrophy proved these skills are fragile without deliberate practice.

We go practical with a three-part playbook to make human skills visible and valuable: assessment that measures thinking in co

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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

Think code that ships faster, reviews itself, and arrives with tests baked in and humans who write fewer lines while making more decisions.

That’s the shift we unpack with CTO and software architect Stephen Houston, CTO TeamFeePay, who moved from healthy sceptic to AI-native practitioner and now runs an end-to-end workflow where models generate features, independent AIs review against the ticket, and a third engine composes tests to probe real behaviour.

TLDR:

• AI as leverage across the software lifecycle
• Enduring value of architecture, design patterns, and clear requirements
• Why pure coding roles shrink and product skills grow
• Levelling of junior and senior lines through supervised AI
• Practical AI workflow with Jira, codegen, reviews, and testing
• Common pitfalls, overconfidence, and guardrails for quality
• Leadership actions for CTOs in small and medium teams
• Mindset shift from fear to measured adoption

We dig into what actually changes and what never should. The timeless pillars still stand: clear requirements, sound architecture, and deliberate design. The reframe is where engineers spend their time. Instead of inferring missing specs and grinding boilerplate, top performers write machine-readable acceptance criteria, think through edge cases, and supervise multiple AI agents in parallel.

Juniors with strong fundamentals can now punch above their weight, while seniors extend their reach by orchestrating, validating, and aligning work with product goals. The real skill is judgement: knowing when an answer is plausible but wrong, catching shortcuts like hard-coded outputs, and encoding lessons into prompts so the whole team compounds.

We also get honest about risk and reward. Some developers will be replaced—mainly those who only type and never think in systems or business value. The rest can treat AI like a tireless junior: fast, eager, sometimes wrong, always improving with guidance.

Stephen outlines practical steps for leaders: integrate LLMs with your issue tracker and repo, define prompting standards, remove policy bottlenecks, and measure throughput and defects before and after. Start asking “why can’t an AI do this step?” and automate ruthlessly so humans focus on design, decisions, and outcomes.

Ready to move up the stack and future-proof your craft?

Listen, subscribe, and leave a review with the one habit you’ll change this week. Then share this with a teammate who needs a nudge from fear to practice.

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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

This podcast explores how the core principles of coding and software design have remained consistent over the years, even with evolving technologies like IoT and blockchain.

Stephen Houston of TeamFeePay and I discuss the importance of good **software architecture and adhering to sound design principles for building scalable systems.

We talk to software engineering best practices that ensure long-term success in software development now the world of engineering has changed with AI.

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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

Imagine accessing board-level talent only when you truly need it no bloated overhead, no inventing tasks to fill a calendar.

That’s the promise of fractional leadership, and it’s reshaping how ambitious companies scale. We sit down with Kevin Steele, founder of Clarendon Financial and Strategy, to unpack how a fractional CFO links finance to strategy and builds the financial engine that powers growth.

TLDR / At A Glance:

• What a fractional executive is and is not
• Why growing firms choose fractional over full-time
• Cost, flexibility, and scaling engagement up or down
• CFO focus on linking finance to strategy
• Clean data as the non-negotiable foundation
• The 30-60-90 plan and engagement stages
• When and how to hand over to a full-time hire
• How to vet credibility, sector fit, and references
• Managing fractionals for outcomes, not hours
• The future of fractional work and the credibility gap

Kevin lays out a clear operating model: start with discovery, fix the data, then install budgets, forecasts, and non-financial KPIs that tie your commercial story to cash. He explains why clean data is non-negotiable garbage in, garbage out and how a focused 30-60-90 plan drives measurable results fast.

We dig into the moments when fractional support creates the most leverage, from due diligence and acquisitions to board reporting and scenario planning, and how engagements flex up or down based on need.

The conversation also gets practical about selection and trust. Not all “fractionals” are equal. We outline a simple due diligence checklist: proven C-suite experience, sector specialism aligned to your model, and live client references.

Kevin shares why he specialises in B2B services and why, at scale, a fractional CFO should help you graduate to a part-time or full-time hire with clean systems that a new leader can run with.

Along the way, we talk about the credibility gap in a world where anyone can claim expertise, and why the real value often looks like “another adult at the table” a steady, independent voice that challenges assumptions and keeps strategy honest.

If you’re weighing full-time versus fractional, or wondering how to get real ROI from rented expertise, this conversation gives you the playbook to decide with confidence.

Subscribe for more deep dives on leadership, finance, AI, and the operating systems that help founders grow. If this helped clarify your next move, share it with a colleague and leave a review what role would you rent first?

Need a fractional executive in your business? Then let's chat - https://calendly.com/kierangilmurray/next-steps-fractional

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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