Breaking News! Google Specialist releases guide to Agentic AI systems

Antonio Gulli’s comprehensive book covers 21 design patterns for building autonomous AI agents, featuring practical frameworks and technical implementation guides for developers. Antonio Gulli, a Google distinguished engineer, has published a comprehensive 400-page technical guide to building autonomous AI systems, offering detailed blueprints for creating sophisticated artificial intelligence agents. Antonio Gulli, Senior Director and Distinguished Engineer in Google’s CTO Office, announced Agentic Design Patterns: A Hands-On Guide to Building Intelligent Systems with a scheduled release date of December 3, 2025. The publication addresses a critical gap in AI development methodology. According to Gulli, building effective agentic systems requires more than just a powerful language model—it demands structured architectural blueprints. “It’s about moving from raw capability to robust, real-world applications,” Gulli stated in the book’s introduction. The guide presents 21 distinct agentic patterns that serve as fundamental building blocks for autonomous AI systems. These patterns range from foundational concepts such as Prompt Chaining and Tool Use to advanced implementations including Multi-Agent Collaboration and Self-Correction frameworks. Each pattern represents a reusable solution to common challenges encountered when building intelligent, goal-oriented systems

Technical specifications detailed in the book cover multiple implementation frameworks. The guide utilizes three prominent development platforms: LangChain and its extension LangGraph for building complex operational sequences, CrewAI for orchestrating multiple agents, and the Google Agent Developer Kit for evaluation and deployment processes. This multi-framework approach ensures broad applicability across different technical environments. The publication structure follows a practical methodology. Each chapter focuses on a single agentic pattern, providing pattern overviews, use cases, hands-on code examples, and key takeaways. According to the table of contents, Part One covers 103 pages of core execution patterns including Prompt Chaining, Routing, Parallelization, Reflection, Tool Use, Planning, and Multi-Agent systems.

Part Two addresses 61 pages of memory management and learning capabilities. This section explores Memory Management, Learning and Adaptation, Model Context Protocol (MCP), and Goal Setting frameworks. The technical depth continues through Parts Three and Four, covering 114 pages of advanced topics including Exception Handling, Human-in-the-Loop patterns, Knowledge Retrieval, and Safety implementations. 

The book’s technical approach emphasizes practical implementation over theoretical discussion. According to the publication details, the guide includes executable code examples, architectural diagrams, and step-by-step implementation instructions. This hands-on methodology addresses the growing demand for actionable AI development resources in enterprise environments. Industry validation for the guide emerged through social media discussions among AI practitioners. Multiple technology leaders shared positive assessments of the publication’s practical value. The book received recognition as a “#1 New Release in Probability & Statistics” on Amazon with a December 3, 2025 release date.

Gulli brings extensive technical credentials to the publication. His background includes over 30 years of relevant experience in AI, Search, and Cloud technologies. He holds a Ph.D. in Computer Science from the University of Pisa and has previously authored technical publications including “Deep Learning for Keras” across multiple editions and languages. The economic context for agentic AI development shows significant market potential. Recent research published on PPC Land indicates Google Cloud projects the agentic AI market could reach $1 trillion by 2040, with 90% enterprise adoption expected. This projection reflects growing demand for autonomous AI systems capable of executing complex workflows with minimal human intervention.

The timing of Gulli’s publication coincides with increased industry focus on AI agent development. Major technology companies have recently released comprehensive AI agent guides, marking a shift toward more autonomous systems. Companies including Anthropic, OpenAI, and McKinsey have published complementary resources, though Gulli’s guide stands out for its comprehensive technical depth and practical implementation focus. The book addresses critical challenges in AI agent reliability and safety. Traditional single-prompt interactions often prove insufficient for complex, multi-step tasks. Agentic patterns provide structured approaches to decomposing complex objectives into manageable components while maintaining coherence across extended workflows. Pattern composition represents a key advancement outlined in the guide. The publication demonstrates how individual patterns combine to create sophisticated systems. For example, an autonomous research assistant might integrate Planning patterns for task decomposition, Tool Use for information gathering, Multi-Agent Collaboration for specialized analysis, and Reflection for quality assurance. 

Agentic AI is an advanced form of artificial intelligence focused on autonomous decision-making and action. Unlike traditional AI, which primarily responds to commands or analyzes data, agentic AI can set goals, plan, and execute tasks with minimal human intervention. This emerging technology has the potential to revolutionize various industries by automating complex processes and optimizing workflows. Agentic AI systems are designed to operate with a higher degree of autonomy. It works by using AI agents, which are essentially autonomous entities designed to perform specific tasks. At its core, this technology is built on several key components:

1. Perception: Agentic AI starts by gathering information from its surroundings and different sources, such as sensors, databases, and user interfaces. This could involve analyzing text, images, or other forms of data to understand the situation.
2. Reasoning: Using a large language model (LLM), agentic AI analyzes the gathered data to understand the context, identify relevant information, and formulate potential solutions. For example, if the goal is to schedule a meeting, the LLM can parse the text of emails to identify attendees, available times, and the meeting’s purpose.
3. Planning: The AI then uses the information it gathered to develop a plan. This involves setting goals, breaking them down into smaller steps, and figuring out the best way to achieve them.
4. Action: Based on its plan, the AI takes action. This could involve performing tasks, making decisions, or interacting with other systems.
5. Reflection: After taking action, the AI learns from the results. It evaluates whether its actions were successful and uses this feedback to adjust its plans and actions in the future. This continuous cycle of perception, planning, action, and reflection allows agentic AI to learn and improve over time.

Google Cloud’s Vertex AI provides a comprehensive suite of tools for training, building, and deploying AI models, including pre-trained APIs for common tasks and custom training options for advanced use cases. Vertex AI also offers MLOps tools to manage the entire machine learning life cycle, from data preparation to model monitoring, which is crucial for the ongoing development and improvement of agentic AI systems.

Agentic AI versus generative AI: While both agentic AI and generative AI are forms of artificial intelligence and can be used together, they have distinct functionalities. 

Generative AI, as its name suggests, is focused on the creation of new content, such as text, images, code, or music, based on input prompts. The LLM is at the heart of generative AI, and the value is generated by what the model can do and simple extensions of the LLM’s capabilities. For example, you can generate or edit content, and even perform simple function calling and chain together various options. 

Agentic AI is a subset of generative AI that is centered around the orchestration and execution of agents that use LLMs as a “brain” to perform actions through tools. Agentic AI goes beyond content creation and function calling by executing actions in underlying systems to achieve higher-level goals. For example, generative AI could be used to create marketing materials, while agentic AI could then be used to deploy these materials, track their performance, and automatically adjust the marketing strategy based on the results. In this way, agentic AI can use generative AI as a tool to achieve its goals.

Agentic AI versus AI agents

While the terms “agentic AI” and “AI agents” are often used together, there is a subtle difference. AI agents are the building blocks of agentic AI. Think of AI agents as individual tools in a toolbox, while agentic AI is the coordinated use of those tools to build an entire house. While an AI agent might focus on a specific task, agentic AI employs multiple agents to handle complex workflows. Agentic AI acts as an overarching system that coordinates and manages these agents to achieve broader objectives.

AI is envisioned to make Google Search radically more helpful, so you can ask any question on your mind and get things done. Starting today, we’re bringing more advanced agentic and personalized capabilities to AI Mode so you can make progress on your tasks and get more tailored information based on your interests. We’re also bringing AI Mode to even more people around the world. Read on for more. New agentic capabilities in AI Mode can help you get things done more easily. We’re starting to roll out today with finding restaurant reservations, and expanding soon to local service appointments and event tickets. For example, you can now ask about getting a dinner reservation with friends that includes multiple constraints and preferences — like party size, date, time, location and preferred cuisine — and AI Mode will streamline this process. Searching across multiple reservation platforms and websites, it will find real-time availability for restaurants that meet your specific needs — and then present you with a curated list of restaurants with available reservation slots to choose from. AI Mode does the legwork and links you directly to the booking page, so you can easily take the last step and finalize your reservation. Under the hood, AI Mode uses the live web browsing capabilities of Project Mariner, direct partner integrations on Search, and the power of our Knowledge Graph and Google Maps to help users take action on the web. We’re working with partners like OpenTable, Resy, Tock, Ticketmaster, StubHub, SeatGeek, Booksy and many more to make this experience possible. 

Assume you are hired in ABC Consulting, for your digital marketing skills. Your role involved running campaigns in Google Adwords for your clients which are large MNCs. Limit your answers to 400 words per question. 

1. Suppose you are using Google Adwords for Advertising, how do you envision Agentic capabilities in this platform would be useful for your tasks. Critically evaluate how uplift in campaign performance (e.g., CTR, conversion, LTV) can be attributed specifically to agentic orchestration, rather than to improved creatives or larger budgets. 

2. The report illustrates how agentic AI can auto-generate and deploy content while routing only select cases for approval.  Where should the line be drawn between agent autonomy and human approval in digital marketing workflows (e.g., bids, creative swaps, send-time, pricing)? What rules, thresholds, or metrics would you set to determine when escalation to a human is required?

3. Agentic AI relies heavily on real-time signals from CRM, ERP, POS, and customer interaction data to adapt decisions.

Which data gaps or latencies most limit the effectiveness of agentic AI in digital marketing (e.g., identity resolution, product catalogue freshness, event streaming)? Based on your experience, what workarounds or solutions can mitigate these issues? 

4. The report shows that agentic AI systems execute autonomously and escalate only exceptions. Describe a possible instance where an agent operates “over-optimized” (e.g., chasing short-term CTR at the expense of LTV, or brand-unsafe placements). What governance mechanisms (e.g., thresholds, re-weighting objectives, new evaluation pipelines) could be introduced to correct or prevent such risks?

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