Learning from Google’s Guide to Agentic Design Patterns
What Google’s new playbook reveals about the future of digital strategy
Our second Deep Tech Briefing takes inspiration from Google’s newly released Agentic Design Patterns playbook. While the 406-page report is aimed at developers, its lessons reach far beyond engineering. For executives and strategists, it offers a timely reminder that adopting AI is not simply about acquiring tools. Just as organisations once misunderstood the true nature of digital platforms, many now risk approaching AI without appreciating the architectures and principles that underpin it. This briefing unpacks those ideas and highlights what they mean for digital strategies today.
The New Wave of Hype
Not so long ago, ‘digital transformation’ was the corporate buzzword of choice. Leaders rushed to purchase platforms, applications and cloud services, often without understanding what a platform actually was, or how digital architectures created value. Many projects under-delivered, not because the technologies were flawed, but because organisations failed to grasp the underlying concepts.
We now find ourselves in a similar moment with AI. Once again, executives are being urged to act quickly — adopting new tools, plugging in applications, experimenting with agents — without a clear sense of what these systems really do, how they operate, and what their true value proposition is.
The risk is repeating the mistakes of the last cycle: investing heavily, only to discover later that the strategy lacked depth.
A Source of Insight
This briefing was inspired by a major release from Google’s AI leadership. Antonio Gulli, Google’s CTO of AI, has shared a 406-page playbook on building AI agents — and made it freely available.
While the text is written for developers, its implications for executives are profound. The central idea is that large language models (LLMs) are the engine, but agentic design patterns are the blueprint. Without the blueprint, organisations risk familiar AI pitfalls: hallucinations, context loss, and unreliable performance.
For technical teams, the playbook provides code examples and detailed treatments of advanced prompting, memory management and RAG (retrieval-augmented generation), inter-agent communication, and tool use. For leaders, the real lesson is clear: it is not enough to acquire powerful AI systems. To unlock real value, leaders must understand the architectural logic that sits beneath them.
Core Concepts Every Leader Should Understand
- Reflection and Adaptation
AI agents are designed with the capacity to reflect — evaluating their own outputs, learning from mistakes, and improving iteratively. For leaders, the parallel is clear: organisations too must build reflexive capabilities, learning from the market in real time, and adjusting course as circumstances shift. - Planning and Goal Setting
Unlike simple automation, agentic AI can plan: it identifies goals, breaks them down into steps, and executes dynamically. In business, this translates to moving away from static roadmaps. Leaders must recognise that strategies now need to be living systems — constantly reviewed, tested, and reconfigured. - Multi-Agent Collaboration
AI systems are beginning to operate in swarms, coordinating across multiple agents to achieve complex objectives. This mirrors the challenge of modern enterprises, which must orchestrate across ecosystems of partners, suppliers, regulators and customers. Leaders must become adept at guiding collaboration in environments where no single actor has full control. - Human-in-the-Loop
One of the most crucial concepts is human oversight. AI can automate many tasks, but the role of people remains essential — for judgement, ethics, creativity and trust. Leaders must decide not just where to apply AI, but where not to. - Guardrails and Governance
Agentic AI is only as safe as the structures placed around it. Patterns of control, monitoring, and evaluation ensure systems remain aligned with organisational values. In the same way, businesses need governance frameworks that integrate risk management and responsibility into the heart of their digital strategies. - Exploration and Discovery
AI systems are capable of venturing into the unknown, testing new approaches and surfacing unexpected solutions. This is a reminder that leaders must create organisational cultures where experimentation is not only permitted but encouraged — tempered by clear criteria for learning and evaluation.
Implications for Digital Strategies
The key lesson is that AI should not be approached as another IT expenditure. It is a new cognitive infrastructure. Leaders who view it merely as a cost-saving tool will fall behind.
To succeed, businesses need to:
- Build strategic literacy — a deep understanding of the logic of AI systems, not just their features.
- Develop elevated value propositions — articulating AI’s contribution in terms of human outcomes, customer experience, and ecosystem value.
- Embed governance as strategy — making safety, oversight and ethics a source of competitive strength, not an afterthought.
Strategic Priorities for the AI Era
AI represents a discontinuity: a break with previous digital shifts. It demands new ways of thinking, fresh strategic approaches, and a willingness to experiment responsibly.
At Holonomics, we work with leaders to move beyond the hype — helping them to grasp the concepts, frameworks and architectures that matter most. We help organisations articulate AI’s value through the lens of customer-centricity and human values, ensuring that technology adoption is both purposeful and sustainable.
If you would like to explore how we can support your organisation in building the literacy, governance and strategic capacity required for the AI era, please contact us to start the conversation.
Reference
Antonio Gulli, Agentic Design Patterns: A Hands-On Guide to Building Intelligent Systems (Google AI, 2024).
