Author: IRPA AI Senior Analyst, Kieran Gilmurray
Chapter 2: The Executive’s Framework for Agent Strategy
The most successful technology transformations in business history have been led by executives who focused on core business principles. Steve Jobs didn’t need to be a chip designer to envision the iPhone. Jeff Bezos didn’t need to be a database engineer to architect Amazon’s marketplace. Similarly, executives don’t need to understand the intricacies of neural networks to make strategic decisions about AI agents. That said, they do need a clear mental model as to what agents can do and how they create value.
You’d never hire someone without knowing what they’re good at. So don’t deploy AI agents without understanding their architecture.
In this chapter, we’ll look at how to think strategically about agents and what makes them different from regular software.
Memory in Modern AI Systems
What is the single greatest limitation of early AI models, such as GPT-1? It forgot everything – true digital amnesia. They gave brilliant answers—then instantly forgot the conversation. For business, that was unacceptable. You cannot help a customer if you lose their name and problem in seconds. Newer models fixed this flaw.
Short-term or working memory is what enables modern AI to maintain conversational continuity. Rather than treating each interaction as a blank slate, the system uses a dynamic context window to capture recent exchanges, user instructions, and reasoning steps. This enables the AI to track conversations, resolve ambiguities, and respond in a coherent manner. Working memory acts like a scratchpad, holding just enough information for the current task but fading once the task ends unless it is stored in a longer-term system.
Episodic Memory and Retrieval Augmentation Generation (RAG)
A further challenge arises when a question requires information that was either missing from the training dataset or generated after the model’s knowledge cut-off. In such cases, an LLM may hallucinate – producing factually incorrect outputs – or default to a generic response such as ‘this is outside my capabilities’.”
Retrieval-Augmented Generation (RAG) is a powerful technique that enhances an LLM’s response by grounding it in external, up-to-date information. This type of memory is often referred to as episodic memory. When a user submits a query, the system does not rely only on its pre-trained data. Instead, it first searches a defined corpus, such as internal documents, spreadsheets, or wikis, before generating an answer. This ensures outputs are both contextually relevant and factually supported.
Step-by-Step RAG Example
Let’s imagine you are working at a company, and you want to use an internal chatbot to ask a question about the company’s policy regarding the use of a personal laptop for work purposes.
Step 0: The Query
This abridged excerpt sets the foundation. When the book releases on the 19th, the full chapter goes further. It examines what the agentic economy looks like between 2026 and 2030, which organisations are already seeing material gains from autonomous agents, how new business models are forming around agent-led work, and the strategic risks facing leaders who delay. This shift is already underway. The only open question is who moves early enough to benefit.
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About the Author: Kieran Gilmurray
Kieran is a globally recognized authority on AI, automation, and digital transformation, having authored multiple influential books and hundreds of articles that have earned him prestigious accolades, including being named a Top 50 Global Thought Leader and Influencer on Generative AI in 2024, a Best LinkedIn Influencer for AI and Marketing, Top 50 Global Thought Leaders and Influencers on Manufacturing 2024, Top 14 people to follow in data and one of the World’s Top 200 Business and Technology Innovators.
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Originally posted in the IRPA AI Network — Announcements & Updates