Visions of a World with Widespread AI Agents in Production & Your Developer Toolkit for the New Era | by Shir Meir Lador | Google Cloud – Community | May, 2025

It feels like every week there’s a new frontier in AI, doesn’t it? We’ve all been amazed by what Large Language Models (LLMs) can do — their ability to generate text, translate languages, and answer questions is truly remarkable. But the evolution doesn’t stop there. I’ve been getting a lot of questions lately — and asking myself too — what’s the real difference between an LLM and an “AI agent”? And what does it really mean when we talk about these agents moving into production?

I believe we’re on the cusp of something truly transformative. When we talk about an AI agent, we’re moving beyond models that primarily respond to our prompts. An AI agent is an application that uses AI models (often including LLMs as their “brain”) to make decisions and act independently to achieve a user-specified goal. It’s a shift from reactive tools to proactive partners that can manage, execute, and achieve complex tasks.

Getting to this point has been quite a journey in AI:

• We started with an LLM and a prompt — powerful, but largely reactive.
• Then, Retrieval Augmented Generation (RAG) came along, allowing LLMs to pull in real-time facts from external data sources, making their responses richer and more current.
• A real game-changer was function calling, where LLMs could intelligently figure out which external tools or APIs to call, with the right arguments, and actually execute them to interact with the world.
• We then layered on a reasoning loop, enabling these systems to better understand intent, formulate plans, choose the appropriate tools, execute actions, and review the results before responding or taking further steps.
• And that brings us to where we are now, exploring multi-agent systems: teams of specialized agents, each an expert with certain tools or in particular domains, all communicating and collaborating to get complex jobs done.

This article offers a glimpse over that horizon, where these multi-agent systems are widespread. We will explore compelling visions of what our world might look like with the widespread usage of AI agents in production, moving from concept to impactful, real-world systems. Crucially, we will then pivot to you, the developer, outlining the essential toolkit and mindset required to not just adapt to this new era, but to become a key architect in building it.

Part 1: Glimmers of an Agent-Driven World — What If…?

It’s exciting to imagine the possibilities when these agents are deeply integrated into our daily lives and industries. Let me paint you a few pictures of what this could look like:

Vision 1: Your Personal Conductor of AI Agents — Meet Alex & Ruth
Let me paint you a picture. Imagine it’s, say, 2028. Meet Alex, a founder juggling a fast-growing startup, a complex research project, and trying to keep some semblance of a personal life — family, health, maybe even squeezing in time to learn a new skill. The mental load is, frankly, immense. Alex’s calendar is a Tetris game gone wrong, the inbox is a constant battle, and those important little details? They’re constantly slipping through the cracks. Sound familiar to anyone? 😉

Now, what if Alex had something more than just another app? What if Alex had ‘Ruth’? Ruth isn’t your typical assistant. She’s a personalized network of AI agents, a primary Orchestrator Agent (you can even call her Ruth, the “Root” agent 🙂), dedicated to navigating the beautiful chaos of Alex’s life.

So, Alex might say to Ruth, “Okay, next month is critical. We’ve got the ‘Nova’ product launch, my research paper on quantum entanglement is due by the 15th, my daughter’s school play is on the 10th, I absolutely need to book flights for that Tokyo conference, and, oh yeah, I’m determined to hit my 30-minute daily workout and eat healthy.”

Within moments, Ruth’s network of specialist agents quietly gets to work:

• The Project Manager Agent immediately dives into Alex’s team’s task management system (think Jira, via its API). It identifies the critical path for the “Nova” launch, flags potential roadblocks, and even drafts status updates in Google Docs.
• Meanwhile, the Research Paper Assistant Agent is already scheduling focused writing blocks in Alex’s calendar. It’s pulling up relevant prior notes and cited articles from places like arXiv (maybe tapping into Zotero or Mendeley APIs) and will even help format the bibliography and give it a proofread.
• The Personal & Family Logistics Agent has blocked out time for the school play (no missing that!). It’s also researching flight options for Tokyo — comparing prices, layovers, and Alex’s airline preferences using tools like airline APIs and Google Flights. It might even pop up a few thoughtful gift ideas for Alex’s daughter.
• And the Health & Wellness Agent? It’s synced with Alex’s fitness tracker (via Fitbit API or Garmin Connect IQ SDK), scheduling workouts when Alex is likely to have the most energy (cleverly predicted from calendar density!). It’s also suggesting recipes based on dietary preferences and what’s in the smart fridge (hello, recipe APIs!), and adding ingredients to the grocery list through a grocery delivery API.

This kind of seamless, proactive, and deeply personalized coordination by a dedicated multi-agent system — that’s the magic I envision for individual empowerment!

Vision 2: The Truly Responsive Smart City
Now, picture our cities. What if Traffic Flow Agents, Energy Grid Agents, and Public Safety Agents were all working in perfect harmony? Using data from IoT sensors and city service APIs, they could dynamically adjust traffic signals to ease congestion, optimize energy distribution based on real-time demand, and provide first responders with critical, consolidated information during emergencies. This isn’t just about efficiency; it’s about creating urban environments that are more livable, sustainable, and responsive to citizens’ needs.

Vision 3: Healthcare That’s Genuinely Proactive and Personalized
Healthcare is another area ripe for transformation. Imagine “Patient Advocate Agents” streamlining appointments across different EMR/EHR systems and helping manage complex medication schedules. “Health Monitoring Agents,” connected to wearables and home diagnostic tools (perhaps using standards like FHIR by HL7), could offer continuous oversight, flagging potential issues long before they become critical. And “Care Team Collaboration Agents” could ensure all of a patient’s providers are truly on the same page. This shift could lead to more preventative care, better outcomes, and a much less stressful journey for patients and their families.

Vision 4: Education That Adapts to Every Single Learner
We all know that a one-size-fits-all approach to education doesn’t work. What if “Personalized Learning Agents” could tailor lessons for each student, drawing on vast content repositories and adaptive tools via Learning Management System (LMS) APIs to match their unique pace and style? “Teacher Augmentation Agents” could handle routine administrative tasks, freeing up educators to focus on what they do best: inspiring and mentoring. This could truly empower every student to reach their full potential.

Vision 5: The “Agent Economy” — A Marketplace of Interoperable Skills, Seamlessly Integrated
This gets me thinking about how these specialized agents will interact on a larger scale, beyond just our personal networks. I envision an “Agent Economy” — a vibrant marketplace, perhaps like an “Agent Mall,” where businesses and individuals can discover, evaluate, and integrate specialized AI agents on demand.
Imagine searching for a “Logistics Optimization Agent” for your supply chain, or a “Personalized Financial Planning Agent.” These agents would advertise their capabilities, security protocols, and communication endpoints via standardized “Agent Cards.”
The real magic here would be the easy integration into your existing workflows, powered by Agent-to-Agent (A2A) communication protocols. If Alex, from our first vision, needed a highly specialized “Research Cross-Referencer Agent” for a short-term project, Ruth (Alex’s orchestrator agent) could, using A2A, discover such an agent in the marketplace, securely connect to its A2A endpoint, and seamlessly integrate its services into Alex’s ongoing research paper workflow. There’d be no fussing with complex SDKs for each new agent; A2A would provide that standard “handshake.” This creates a truly dynamic and interoperable ecosystem, allowing us to compose sophisticated solutions by plugging in specialized agent skills as needed, fostering rapid innovation.

For enterprises looking to participate securely in this economy and manage their own portfolio of agents, platforms like AgentSpace by Google Cloud could provide a crucial layer. An AgentSpace could act as an enterprise’s curated environment to build, govern, and deploy their own agents, as well as a secure gateway to discover and integrate trusted third-party agents from the wider Agent Economy.

Part 2: The Developer Blueprint — Mindset, Skills, and Tools for the Agent Era

These visions are exciting, but they won’t build themselves! This is where we, as developers, come in. To make this future a reality, perhaps the most significant shift is moving from a Feature Builder to a System Orchestrator. Our role expands beyond coding individual application features to designing and orchestrating complex systems of collaborating intelligent agents. We become architects of intelligent ecosystems, thinking about how individual agents, each with goal-driven autonomy, interact to produce emergent behaviors. This requires a deep commitment to understanding the entire system, not just its parts, and always keeping an eye on the bigger picture and the real-world impact.

With this “System Orchestrator” mindset as our foundation, let’s look at the essential skills and tools we’ll need:

Essential Skills to Cultivate:

• Multi-Agent System Design: This is about more than just distributing tasks. It’s the ability to decompose complex problems into distinct roles for specialized agents, define their individual goals, and, crucially, design how they’ll collaborate, communicate, and resolve conflicts. It involves understanding the “why” behind an agent’s actions, not just the “how” of a single task.
• Tool Integration & API Mastery: Agents need to perceive and act upon the world. This means becoming proficient in connecting them to a vast array of external data sources, services, and APIs (e.g., accessing a task management system like Google Tasks API, a knowledge base like Google Scholar, or interacting with IoT devices) leverging protocols like MCP.
• State Management & Long-Term Memory: For agents to be truly effective partners, like Ruth assisting Alex, they need to maintain context, learn from past interactions, and recall relevant information over extended periods. Designing robust state management is key.
• Designing for Human-in-the-Loop (HITL) & Trust: This is absolutely critical. As agents take on more significant and potentially sensitive tasks, we must design clear and effective mechanisms for human oversight, intervention, and final approval. Especially in high-stakes scenarios (like in healthcare or finance), ensuring a human can review, override, or guide an agent’s decision is non-negotiable for building trust and ensuring Responsible AI. This includes transparency in how agents make decisions and providing users with control.

Orchestration Frameworks (e.g., Google Cloud’s Agent Development Kit — ADK):

To build sophisticated multi-agent systems like the one helping Alex, you need a conductor’s baton. An Agent Development Kit (ADK) provides exactly that. It’s the structure to define individual agents, their specialized tools, and their “brains” (often powered by models like Gemini). What makes an ADK so good for this?

• Flexible Orchestration: It can handle both predefined workflows (like a daily briefing sequence for Alex’s ‘Ruth’ system) and LLM-driven dynamic routing for when things need to adapt on the fly (like Ruth re-prioritizing when an urgent meeting pops up).
• Multi-Agent Architecture: This is where it really shines. It’s built from the ground up for creating systems of multiple agents. You define your specialized agents (Project Manager, Travel Booker, etc.), each with its own purpose and tools, and an Orchestrator agent (like Ruth) to delegate and synthesize information. Crucially, ‘Each agent can be separately built, maintained, and deprecated without immediate impacts on another.’ That modularity is gold for complex systems!
• Rich Tool Ecosystem: Alex’s agents need lots of tools! An ADK supports built-in Google Cloud products, third-party tools (like Jira), and importantly, lets you create custom tools — perhaps to talk to a proprietary company database. It even allows agents to use other agents as tools.
• Deployment Ready & Built-in Evaluation: ADK is for real-world applications. It typically supports containerization for deployment anywhere and includes tools to systematically check how well your agents are performing their tasks.
• State Management: For a system like Ruth that needs to remember things, an ADK handles memory and state (we often call these “Sessions”). This lets agents recall past conversations, preferences, and the status of ongoing tasks over multiple interactions, or even days.

Communication Protocols (e.g., A2A — Agent-to-Agent):

Now, how do Ruth’s different specialist agents — the Project Manager, the Travel Booker, the Health Planner — actually talk to each other and to Ruth, the main Orchestrator Agent, effectively and securely? This is where Agent-to-Agent (A2A) communication is vital. Think of it as the universal language for agents. Just like APIs changed how software components talk, A2A is set to do the same for AI agents. Some core A2A capabilities really stand out:

• Capability Discovery through “Agent Cards”: How does one agent find another that can help it? Through ‘Agent Cards.’ An agent publishes this machine-readable JSON document — kind of like its LinkedIn profile mixed with a detailed API spec. It describes who it is, its unique skills (what it can do), how to reach it (communication endpoints), and the security protocols it supports.
• User Experience Negotiation: This is pretty cool. If an agent needs to show something to a human user but is doing so through another agent (maybe a master orchestrator that owns the UI), A2A lets them negotiate the user experience — can it show an iframe, a complex web form, or just simple text and buttons? This ensures information is presented well, no matter the chain of agents.
• Task and State Management: A2A interactions are fundamentally task-oriented. When Agent A gives a task to Agent B, A2A helps manage that task’s lifecycle, keeping track of its state (pending, in-progress, done, failed?) and ensuring necessary context is passed along.
• Secure Collaboration and Artifact Exchange: Agents need to share info, instructions, and results. A2A provides a standardized and secure way to exchange these ‘artifacts’ (which could be a JSON report, a generated document, an image, or even a request for human input). Security here is paramount.

Standardized Tool Access (e.g., MCP — Model Context Protocol):

Think about all the external tools and data sources Alex’s agents need to connect to — the company PM system, airline booking sites, the smart fridge’s inventory API. How do they do this reliably and securely without writing custom code for every single one? Enter the Model Context Protocol (MCP). people like to call MCP the “USB-C for AI” — it’s a universal adapter.

• Standardized Tool Use: MCP lets tool providers (your airline, your PM software vendor) expose their services in a standard way. Agents can then find and use these tools without needing a custom integration for each.
• Secure and Controlled Data Access: MCP puts a big emphasis on security and user consent. Alex would only let Ruth’s agents access the tools and data explicitly authorized, and all interactions are structured and auditable. This is super important when dealing with personal and sensitive company info.
• Client-Server Architecture: Agents (as MCP clients) can connect to various MCP servers offering specific capabilities, like a ‘Google Calendar MCP Server’ or a ‘Jira MCP Server.’
• AI-Assisted Development (e.g., Gemini Code Assist):
  And let’s not forget that AI is also here to help us build these sophisticated agent systems more efficiently! Tools like Gemini Code Assist from Google Cloud can significantly boost our productivity by assisting with code generation, debugging, understanding complex codebases, and even helping to write documentation. Embracing these tools allows us to focus more on the high-level architectural design and the unique logic of our agent systems.

Part 3: Our Invitation — Let’s Build This Future

This shift towards an agent-powered world is, I believe, one of the most exciting opportunities for us developers in a long time. We’re moving from building software that people use to creating intelligent systems that collaborate with people. It’s a big leap!

My advice? Embrace it! Let’s cultivate that agent architect mindset, hone these new skills, and get familiar with these powerful toolkits.

The future will be orchestrated, and we, as developers, have a unique chance to be the conductors. So, let’s go forth, learn, innovate, and build these incredible intelligent systems that will define tomorrow. What are you most excited to build with agents? I’d love to hear your thoughts!