May 2025
Remember the last time you planned a business trip? There were browser tabs everywhere: flights, hotels, calendar, currency converter, and client meeting locations. Each app worked in isolation and demanded your manual attention.
What if you could instead say, “Plan my Tokyo trip for next quarter. Direct flights, hotel near Marunouchi, under $5K, dinner with Tanaka-san Tuesday,“ and watch intelligent technology handle everything? No separate apps, no manual coordination—just AI working behind the scenes like an invisible operating system.
This is “agentic“ AI, and its future will reshape how businesses create new digital products.
The insights in this guide draw from key developments showcased at Google Cloud Next, Google I/O, and Microsoft Build 2024, where tech leaders unveiled their vision for the agentic future and the platforms making it possible.
Traditional AI is like a brilliant search engine. You ask, it answers. Generative AI, like ChatGPT in its base form, specializes in creating new content (text, images, code) based on prompts. But agentic AI? An agent is a thing that a human being is able to delegate tasks to, systems that take actions on your behalf and under your control.
This shift from “ask and answer“ to “delegate and done“ represents a fundamental change in how we interact with AI. While generative AI creates content, agentic AI accomplishes tasks. An agentic system might use generative AI as one of its many tools to draft an email or summarize a report as part of a larger objective. Still, its core purpose is to act and complete delegated goals, not just generate content.
Think about what this means for your business. Instead of sophisticated search results, you get capable assistants that book appointments, manage projects, accelerate research, or even help with medical diagnosis. For instance, Google’s Project Mariner already does this with Zillow. It navigates the site, finds suitable properties, and schedules tours while you focus on what matters most.
Pretty exciting. But here’s the catch: building this kind of AI partnership requires an entirely different approach to software architecture.
Imagine a human assistant who forgets everything you told them five minutes ago. Not very helpful, right? Agentic AI needs to be more like a seasoned colleague. That’s why the new architecture insists on statefulness, systems that remember past interactions and build context over time. This memory is what transforms agents from simple Q&A tools into evolving relationships that get more useful the longer you work together.
We’re talking about more than just text here. For this “invisible operating system“ to really click, agents need to see, hear, and speak your language, processing images, video, and audio; in other words, they are multimodal. Think about the richer interactions this unlocks, especially on your phone or the latest AR goggles and glasses.
To be truly useful, an agent often needs to draw on different AI models and tools, like using one model for understanding your request, another for analyzing data, and a third for generating a specific output, all within its capacity to get the job done. These skills are often powered by a suite of agentic tools.
Agentic tools are specialized AI models (many themselves supercharged by recent breakthroughs in large language models) for different tasks, APIs for accessing external services, and software libraries that enable interaction with digital environments.
While tech giants like Microsoft and Google are building these capabilities into their platforms, a vibrant open-source ecosystem is also emerging with frameworks like LangChain and AutoGen, making agentic development accessible to companies of all sizes and accelerating innovation across the industry.
An agent becomes genuinely proactive when its deep understanding of your personal and organizational context is combined with reliable data grounding. Its decisions and actions should be based on accurate information, not just patterns, enabling more trustworthy and tailored assistance.
Ensuring user privacy and data control remains essential as agents access this rich, grounded context. For example, an agent for a legal firm understands legal terminology and processes, while an agent for a consulting company reflects their analytical approach and industry expertise.
Computer use is the ability for AI agents to interact directly with browsers, software, and digital interfaces just like humans do. In other words, agentic AI works with your current setup, making it smarter.
Microsoft is building native support directly into Windows and GitHub Copilot, while Google is bringing these capabilities to developers through the Gemini API. Companies like Automation Anywhere and UiPath are already building on these capabilities.
When you tell an agent to “book a restaurant for Saturday night,“ this capability helps the AI figure out which reservation API to use, what parameters to include, and how to handle the response. Called function calling, this is the bridge between understanding your request and taking intelligent action.
Function calling helps make agents active participants in your workflow. They become systems that make smart decisions about which tools and services to use for any given task.
As tasks get more sophisticated, individual agents hit their limits. That’s where multi-agent orchestration becomes essential.
Microsoft Azure AI Foundry lets you create specialized agents (think Facilities agent, Finance agent, Legal agent) that collaborate on complex business processes spanning multiple domains.
Here’s where it gets really exciting: these orchestrated agents don’t exist in isolation. The ultimate vision is an open agentic web, a distributed ecosystem where agents discover services, access data, and collaborate across platforms.
Your application can become a vital part of a broader ecosystem where agents discover your services, as your users seamlessly access external capabilities through your platform. Microsoft’s NLWeb initiative exemplifies this direction, aiming to make any website or API an agentic application that agents can easily access.
What becomes possible when agents have these capabilities? The potential to reshape industries and address complex challenges is immense:
Imagine AI agents managing entire sophisticated processes, from initial data intake and analysis to multi-step execution and final reporting, requiring minimal human intervention once delegated. This goes far beyond simple task automation.
Stanford Medicine offers a compelling example: They use an agentic AI orchestrator that connects multiple specialized agents to synthesize patient history, radiology reports, and clinical trial data to inform cancer care decisions.
Agents can sift through vast amounts of information, identify patterns, and synthesize insights that might be beyond human capacity. Microsoft’s Discovery platform demonstrates how agents can reason over scientific knowledge to accelerate R&D, potentially identifying novel materials or drug candidates that human researchers might overlook.
The combination of reasoning, data access, and action-taking opens doors in specialized fields. Consider the impact of accessibility: In Japan, Aisin and Kato San used Microsoft’s Foundry platform to build an application that helps individuals with auditory processing disorders. This showcases how agentic AI’s ability to combine sophisticated reasoning with access to vast data and real-world action creates significant potential for positive impact in healthcare, scientific research, accessibility, and many other fields.
The Microsoft Agent Store already provides a marketplace where developers can publish agents to reach hundreds of millions of users. Over 70,000 organizations are using Foundry to build AI applications. The emergence of this powerful agentic web means we need a new framework for thinking beyond traditional app development.
The companies that understand the shift from building tools that users operate to building systems that users delegate to will shape the next decade of technology. How can a company implement agentic AI? Here are critical decisions for leaders navigating this transformation:
Your competitive edge won’t come from traditional features but from intelligent systems that accomplish meaningful tasks for users. The question shifts from “what features should we build?“ to “what complex processes could an AI agent handle for our users?“
Instead of creating a better dashboard for sales analytics (feature), you’d make an agent that proactively identifies at-risk deals, suggests outreach strategies, and even drafts initial communication (complex process).
Don’t just look for tasks to automate; identify complex, multi-step processes where your users currently spend significant effort coordinating actions across disparate systems. These are prime candidates for delegation to AI agents that can operate with greater autonomy, like our “Tokyo trip“ example above. Ask: “What sophisticated goals can we empower our users to delegate entirely to an AI agent?“
Agentic systems thrive on rich, real-time context to make decisions and take effective action. Evaluate your data infrastructure not just for quality, but for dynamic accessibility to AI agents. Can it provide the necessary grounding for complex reasoning? Can it empower agents to interact with the required tools and APIs? This is about enabling agents to do, not just to know.
Your initial agentic projects should prove out an entire delegated task, however, scoped down. This lets you test the full cycle: intent understanding, planning, multi-step execution, and outcome assessment. Focus on building a simple but complete “digital teammate“ for a specific purpose rather than just proof-of-concepting a standalone AI model.
Skilled AI teams can help you with everything from building integrations to recreating processes. Beware of “vibe coding“ here, where AI outputs look impressive but don’t actually align with solid business needs. You need people who ensure strategic outcomes, not just cool demos.
When platforms promise tools that build sophisticated apps “in minutes, not weeks,“ early adopters who master this approach will deploy superior solutions at unprecedented speed.
Which business processes and customer journeys should be delegated to this AI layer first? Not every workflow benefits equally from agentic automation; strategic selection determines success.
Complementing cloud-powered capabilities, the agentic web increasingly extends to users’ local devices via on-device AI processing. As a product leader, you must determine the optimal mix of cloud, edge, and on-device deployment to balance power, privacy, and performance for your agentic features.
This trend indicates a move towards a hybrid approach, where the optimal mix of cloud, edge, and on-device deployment is chosen to balance power, privacy, and performance.
Building trust is paramount. This involves not just technical reliability but fostering a genuine human-AI partnership where interactions are intuitive and ethically sound. As AI agents become more sophisticated, the nature of human-AI interaction evolves into a new creative collaboration, where curating intelligent experiences becomes as important as creating them.
As a result, you’ll need to proactively address the ethical, privacy, reliability, and governance challenges from the outset. To build and maintain user trust, make these challenges core to your agentic strategy, not afterthoughts.
This includes ensuring transparency in how your agents make decisions. Unlike traditional software with predictable if-then logic, agentic systems can feel like “black boxes,“ especially when multiple agents collaborate on complex tasks. Users need to understand not just what their agent accomplished but also why it chose that particular approach. Building explainability into your agentic systems from day one creates the transparency that enables proper human oversight of increasingly autonomous systems.
Adapting to this agentic web will require more than just adopting new tools; it demands a strategic reimagining of product development, user interaction, and your business’s value proposition.
The future of business technology isn’t about better apps—it’s about smarter systems that get things done. Is your company ready?
Agentic AI refers to artificial intelligence systems that can proactively take actions and perform tasks on your behalf once delegated. Unlike traditional AI, which primarily provides answers to queries, agentic AI shifts to a “delegate and done“ model. For businesses, this means the potential to automate complex end-to-end workflows, accelerate research through advanced reasoning and information synthesis, manage projects, and even assist in specialized fields like medical diagnosis, ultimately reshaping how digital products are created and how businesses operate.
An agentic AI system is designed to understand goals, make decisions, and execute tasks in digital (and sometimes physical) environments. It needs key capabilities to function effectively: a good memory (statefulness) to recall past interactions, the ability to understand the world through various inputs (text, image, audio, video), a range of skills (often drawing on multiple AI models and tools), and actionable context grounded in reliable data to make informed decisions and take appropriate actions.
Standard ChatGPT is primarily a generative AI. However, when integrated with tools, plugins, or capabilities like function calling (allowing it to interact with external APIs and software), it can exhibit agentic behaviors. For instance, if ChatGPT can book a reservation or access live information via a tool, it’s acting agentically in that specific context. So, while the core model is generative, its capabilities can be extended to become more agentic.
Agentic AI can be applied to:
The future points towards an “open agentic web,“ a distributed ecosystem where AI agents discover services, access data, and collaborate across platforms. Businesses will likely shift from developing standalone apps to creating intelligent systems that accomplish meaningful tasks. This requires a strategic reimagining of product development, user interaction, and the company’s overall value proposition, moving from “better apps“ to “more intelligent systems that get things done.“
This is a crucial point highlighted by industry discussions.
This is where “Brand AI“ becomes crucial. AI agents interacting with customers must authentically represent your brand through:
Understanding the following terms can be helpful when exploring agentic AI:
Our article on Decoding Software Development Buzzwords can help you gain a broader understanding of general software development buzzwords that might be used in conjunction with AI development.
On-device processing for agentic AI offers several distinct advantages:
If you have aspirations and goals that you’re passionate about, we would love nothing more than to have a conversation with you. Drop us a line today, and let’s embark on an extraordinary journey together.
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