Agent Strategy, Made Practical For Leaders

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 strategyworking memory versus episodic memory with RAGstep-by-step RAG example using BYOD policytraditional RAG versus agentic RAG adaptabilityfine-tuning as semantic memory and trade-offsprompt engineering structure, guardrails and toolsrule of thumb for choosing methodsreasoning loops ReAct and perceive-think-act-learntask decomposition, planning and exception handlingAPI integration, orchestration and real-time adaptationleaders’ role in architecting capabilities to outcomesWe 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 GilmurrayWant to buy the complete book? Then go to Amazon or  Audible today.Support the showIf you are leading your businesses strategic transformation and need greater clarity, stronger execution and measurable results, let’s connect.  🌎 Website: www.KieranGilmurray.com📅 Book a call: https://calendly.com/kierangilmurray/catch-up📘 Kieran Gilmurray | LinkedIn🌐 Substack: https://kierangilmurray.substack.com📕 Amazon https://tinyurl.com/MyBooksOnAmazonUK AI Transparency Notice:  This podcast uses a hybrid format. When an episode features one of Kieran Gilmurray’s written articles, the narration is generated using a synthetic clone of his voice via ElevenLabs AI (the underlying article text is entirely human-authored). Episode descriptions and summaries are assisted by AI and should be considered unedited by a human unless specified.