Agentic AI: The Next Frontier in Business Automation and Innovation

Agentic AI: How Autonomous Agents Are Revolutionizing Business

Artificial Intelligence (AI) has been sweeping industries for years, but a new wave of Agentic AI redefines what's possible. These autonomous, goal-driven agents go beyond traditional machine learning models by actively making decisions, executing tasks, and learning continuously with minimal human intervention. For businesses, this means automation with a brain, reshaping everything from customer service to product development. In this blog post, we'll explore Agentic AI, why it matters, and how companies across industries leverage it to streamline operations, boost innovation, and gain a competitive edge. At its core, Agentic AI refers to intelligent software agents that can act autonomously to achieve specific objectives. Unlike traditional AI models, which require predefined inputs and outputs, these agents operate independently. Like human employees, they can perceive their environment, make decisions, and take action to fulfill goals. These AI agents have multiple capabilities, such as reasoning, memory, planning, and natural language processing. These traits make them well-suited for complex tasks that require adaptation, contextual awareness, and multi-step execution. As the IEEE Computer Society noted, this intelligent automation is a key driver behind digital transformation in modern enterprises. We've come far from simple rule-based chatbots and robotic process automation (RPA). Agentic AI represents a shift from reactive assistance to proactive agents. Traditional AI tools respond to commands, while agentic AI plans and acts with foresight. For example, imagine a supply chain management agent that not only flags a low inventory issue but also forecasts demand, identifies the best vendors, negotiates pricing, and initiates the reorder process, all autonomously. That's not just automation. That's intelligent delegation. According to a Gartner report, by 2026, over 70% of enterprises are expected to deploy agent based systems to handle complex decision-making workflows. Customer service is among the earliest adopters of AI, but Agentic AI introduces a new level of sophistication. Instead of relying on pre-programmed responses, these agents can analyze customer history, interpret tone, and follow up with personalized offers or solutions. Companies like Cohere are developing language agents that deeply understand the context and adapt their responses accordingly, reducing the need for human intervention. Agentic AI can support R&D teams by searching research databases, analyzing market trends, and suggesting new product features. These agents can conduct simulations, test hypotheses, and recommend the next steps in real time. In software development, agent-based tools can autonomously write code, conduct QA testing, and suggest UI improvements. This frees up developers to focus on strategy and architecture. AI-powered sales agents can interact with leads across multiple channels, nurture them through the funnel, and personalize communication-based on behavior and preferences. In marketing, they can generate content, run A/B tests, and continuously optimize campaigns. The result? Smarter, faster, and more cost-effective customer acquisition and retention. Agentic AI can synthesize large datasets, identify patterns, and make informed decisions without fatigue or bias. This enhances accuracy and consistency across operations. These AI agents work 24/7, don't require breaks, and can manage thousands of tasks simultaneously. Businesses can scale their efforts without scaling costs. Unlike rule-based systems, Agentic AI adapts to new inputs and environments. It gets smarter over time, improving performance with every iteration. While the benefits are clear, there are still important considerations for businesses adopting Agentic AI. Autonomous decision-making can raise ethical concerns. Companies must ensure transparency and fairness in how AI agents act. These agents often interact with sensitive systems and data. Proper safeguards and monitoring are crucial. Deploying AI agents across legacy systems can be challenging. Businesses may need to modernize their infrastructure to leverage Agentic AI fully. Fortunately, the tech community is addressing these concerns. The IEEE Computer Society is actively involved in creating standards for ethical and trustworthy AI. Agentic AI isn't about replacing humans but augmenting human capability. These agents can take on repetitive, data-heavy, and decision-intensive tasks, allowing employees to focus on creativity, strategy, and relationship-building. The most successful companies will foster collaborative ecosystems where humans and AI agents work harmoniously. Think of Agentic AI as a new coworker: efficient, tireless, and always learning. If you're thinking about implementing Agentic AI in your business, here are a few steps to consider: Agentic AI is more than a trend -- it's a foundational shift in how work gets done. As businesses seek new ways to stay competitive in a fast-paced digital world, autonomous AI agents offer a path toward smarter operations, faster innovation, and deeper customer engagement. The age of Agentic AI has arrived. The question isn't whether your business will adopt it; it's how soon.

What Is Agentic AI? Everything to Know About Artificial Intelligence Agents

Barbara is a tech writer specializing in AI and emerging technologies. With a background as a systems librarian in software development, she brings a unique perspective to her reporting. Having lived in the USA and Ireland, Barbara now resides in Croatia. She covers the latest in artificial intelligence and tech innovations. Her work draws on years of experience in tech and other fields, blending technical know-how with a passion for how technology shapes our world. You've probably heard a lot about ChatGPT, Google's Gemini, image generators and AI writing tools. But there's a new term making the rounds: agentic AI. And while it might sound like another buzzword, it's not a new invention. Recent advances, however, have made it far easier to build, deploy and interact with these kinds of systems. Some of them you might have already seen at work, like customer service banking bots, self-driving cars and smart home assistants. If you're using Perplexity in the US as a Pro subscriber, a perfect example is its "Buy with Pro" feature. Rather than assisting with your shopping and handing you off to a retailer, it collects your preferences, processes the transaction (sometimes even selecting the best available retailer) and uses your stored payment and shipping information to complete the order. Experts say it's time to start paying attention to what these AI agents are capable of doing on their own, though widespread use across industries will take time before AI agents become mainstream. Unlike AI chatbots, which often require explicit instructions at each step, AI agents can break down complex objectives into smaller, manageable actions. So instead of simply responding to your questions or prompts, agentic AI is designed to take initiative. That means understanding its environment, making decisions and acting without human direction at every step. So what does that look like in practice, and how is it different from what artificial intelligence is already doing? I spoke to several experts and will break down everything you need to know about agentic AI -- including whether it can be trusted. Agentic AI systems aren't passive tools waiting for input. They operate in a cycle to sense the environment, decide what to do, and then act. That structure makes them more autonomous and lets them take on complex, goal-oriented tasks across multiple systems. "Agentic AI...is now making this sort of sense-decide-act loop available to everybody," Peter Stone, professor at the University of Texas and chief scientist at Sony AI America, told me. "Rather than waiting for the input-output behavior, you're able to task a program with sensing the world, deciding what to do and actually acting." Ankur Patel, CEO and founder of enterprise agentic AI company Multimodal, called it "a fundamental shift from reactive tools to proactive systems capable of complex decision-making." He gave an example of a loan underwriter who might otherwise spend hours cross-referencing pay stubs, tax returns and credit reports. "The AI agent automatically ingests and validates hundreds of data points from diverse sources. Think bank feeds, HR systems and government databases, while flagging inconsistencies like mismatched employment dates," Patel told me. In other words, it's not mere automation. "Agentic AI connects complex, multisource inputs with internal rules or manuals, and gives accurate, critical outputs in much shorter time frames," Patel explained. Generative AI creates content such as text, images, music and even videos, based on what it learned during training and your prompt. Agentic AI can use those same models, but adds a layer of autonomy with reasoning and planning to proactively achieve goals through a sequence of actions. A generative AI tool might write you a vacation itinerary. AI agents could plan the trip, book your flights, reserve the hotel and even rebook everything if your flight gets delayed or canceled. Large language models, like ChatGPT or Claude, can become agentic when connected to external tools, sensors or APIs. This ability to interact with the world (either physical or digital) is what makes the difference. While systems like ChatGPT and Siri are designed to answer questions, agentic AI is built to solve problems. "Chatbots answer questions. Agentic AI solves problems by turning insights into outcomes," Patel said. That means orchestrating tasks across platforms. "For example, it can verify documents, assess risks and even trigger real-world actions like loan approvals or insurance payouts." Like most new tech, agentic AI raises concerns about jobs. Will it replace workers, or help them do their jobs better? Stone said the answer isn't simple. "Usually, when people say automation, they're thinking of replacing jobs. When people say augmentation, they're thinking of changing jobs, making them more efficient," Stone said. He compared it to the transition from hand-washing dishes in a restaurant to using a dishwasher -- there's still a human in the loop, but they're doing less of the repetitive labor. Another relatable example is correspondence. While writing letters by hand and sending them via snail mail might trigger nostalgia in romantic folks like me, we now send messages and emails instantly from smartphones. Patel agreed that agentic systems free people up from the grunt work. "It's unfortunate that a lot of man hours even today are spent on drudgery," he said. "Good AI can take care of them without needing much supervision." For Patel, the bigger risk is falling behind. "The question isn't 'will AI take my job?' It's 'will I be working alongside AI or getting outpaced by those who do?'" While that might sound daunting to anyone hesitant about the shift, AI is advancing fast enough that it feels inevitable. Enterprise software vendors are already rolling out agentic systems in industries like: AI agents in these industries process documents, extract data, flag inconsistencies and route information with minimal human intervention. But, you don't have to work in any of these sectors to notice it. Opera's agentic web browser and Google's new agentic search, called AI Mode, aim to help you go from inspiration to purchase without clicking through pages of results. AI assistants that can book your travel, manage your inbox or compare online deals are all signs of what's coming in the consumer sector as well. Even Microsoft is adding an AI agent to Windows that can change system settings for you. Patel says everyday users should care for three reasons: "First, it gives people their most precious resource back -- time. Second, it vastly improves customer experience. Third, it prevents costly human errors." That said, there are still limitations. AI agents struggle in open-ended or unpredictable environments, especially when tasks lack clear structure or context. They also depend heavily on well-formed prompts or goals. Meaning, vague input can lead to irrelevant or faulty actions. Autonomy brings benefits, but also risks. When systems make decisions or take action without supervision, what happens if something goes wrong? And who is responsible? Is it the person using the AI, or the company/developers that built it? Legal dilemmas continuously expand with these AI advancements. Stone also warns that the risks aren't hypothetical. "The new type of risk... is not a person acting incorrectly or irresponsibly as a result of what the AI advises, but rather the AI system actually taking a dangerous action," he told me. Say you let an autonomous car drive itself, it can do more than just suggest a route and cause harm if it malfunctions or has you drive in circles in a parking lot like this unfortunate passenger. The stakes depend on what the AI is allowed to do. Booking a low-cost flight? Low risk. Accessing medical records or spending thousands of dollars? Much higher. "The risk is directly related to the space of actions and the agency or autonomy that you give to the agent," Stone emphasized. Patel pointed out that safeguards are essential, especially in regulated industries. "To truly trust AI, it needs to have detailed audit trails and decision logs, process documentation, confidence scoring, and the ability to route decisions to humans where absolutely necessary," he said. While the hype around agentic AI is rising fast, don't expect to hand over your entire life to AI agents anytime soon. It will take years for most agentic AI systems to be tailored to specific industries or problems, not one-size-fits-all assistants. "There's a real chance that by demanding perfection from autonomous agents, we're missing an opportunity to do a lot better than the status quo," Stone said. "I think we need to accept that there are going to be mistakes. But they're going to get better over time." And so, the direction is clear. We're moving from AI that chats with us to AI that does things for us. Add robotics to the mix, and it's a whole new ballgame. Agentic AI is artificial intelligence that can independently make decisions and take actions to achieve a goal. Instead of waiting for step-by-step commands, AI agents decide what needs to be done and take action across systems with minimal human involvement.

Making Agentic AI Work in the Real World

Two years ago, ChatGPT couldn't even tell you what day it was. These early models were frozen at their training cutoff -- brilliant conversationalists who could discuss Shakespeare but not yesterday's news. Then came web search. Language models could suddenly fact-check themselves and pull current information. But they remained observers, not participants. They could tell you about the world but couldn't touch it. Today's agentic AI represents a fundamental shift: we've given these systems tools. Take this scenario: you are planning a family vacation to Tokyo. A modern AI agent doesn't just suggest an itinerary. It watches travel vlogs, cross-references museum hours with your kids' nap schedules, books that hidden ramen shop, coordinates calendars, and handles deposits. It's not just thinking. It's doing. For enterprise organizations, the stakes multiply exponentially. Beyond personal data, we're talking about intellectual property, customer information, and company reputation. When you deploy an agent to negotiate vendor contracts, it shouldn't have access to your M&A plans. When it's analyzing competitor pricing, it shouldn't be able to share your internal roadmap. When processing employee benefits, it must protect health information. When analyzing customer behavior, it must safeguard personally identifiable information from being exposed in summaries or reports. The challenge compounds with emergent behaviors -- AI agents finding creative ways to complete tasks that we never anticipated. An agent told to "reduce customer support costs" might start auto-rejecting valid claims. One tasked with "improving meeting efficiency" could begin declining important stakeholder invites. So how do we leverage the unparalleled potential of Agentic AI, safely? This demands a new security paradigm. Authentication becomes: "Is this AI really acting on my behalf?" Authorization becomes: "What should my AI be allowed to do?" The principle of least privilege becomes critical when the actor is an AI operating at machine speed with its own problem-solving creativity. The stakes have fundamentally changed. The biggest hurdle to adoption will be how agents are given safe and secure access to enterprise resources. Enterprise adoption of AI agents requires solving a critical new challenge: how to grant agents access to corporate resources like Google Workspace or Slack APIs without over-privileging them beyond their intended scope. Traditional OAuth implementations provide only coarse-grained permissions -- typically read or read-write access at the application level -- creating an all-or-nothing security model that doesn't align with agent-specific use cases. We are building the ability for an enterprise to implement dynamic, context-aware permission management that evaluates agent requests against both explicit policy rules and semantic analysis of the agent's stated purpose. The system enables employees to delegate granular permissions -- say allowing an agent to read emails for summarization while preventing it from deleting emails -- through a consent-driven workflow that tracks and manages narrow permission lifecycles. By combining OAuth 2.1 compliance with semantic inspection, we can detect and block prohibited activities automatically, thereby keeping the user experience fluent. Critical actions would require a user's explicit authorization to avoid mishaps. We are doing this by extending the same principles of zero trust to Agentic AI. Whether agents are built in-house or outsourced, running on laptops, in the cloud, or in your own data centers, and whether they need access to SaaS, cloud, or on-prem applications, Cisco's Universal Zero Trust Network (UZTNA) architecture gives you the tools you need to adopt Agentic AI for your organization. At the heart of our UZTNA is one simple truth: we must take an identity-first approach to security. Identity transcends traditional technology boundaries, giving you the ability to establish policies at an individual level for humans, machines, services -- and now, Agentic AI. With this foundation, the system can continuously monitor behaviors to distinguish 'normal' from 'abnormal' in near real time, updating policies accordingly. Putting our UZTNA architecture in action, this means Duo Identity & Access Management (IAM) provides the authorization, Secure Access does semantic inspection so that the end user does not have to be prompted repeatedly for access permission, AI Defense is invoked to evaluate that agent actions align with its purpose, and Cisco Identity Intelligence monitors the actions and provides visibility. Together, they provide powerful protection without compromising Agentic AI adoption or experience. More and more, we are going to see Agentic AI become an everyday reality -- integrated into workstreams with the same autonomy as a human but with the speed and scale of a machine. While it represents boundless opportunities, the authorization and access challenges have to be solved. With Cisco's UZTNA architecture, no matter who builds these agents, where they run, or what they need to get the job done, we can ensure enterprise organizations have visibility and control across identity, authentication, authorization, access, and analytics. The future of AI is agentic -- and with the right safeguards in place, it can also be secure.

AI agents can tip the cybersecurity scales to the defenders

Agentic AI has the potential to establish a new era of cyber resilience, but only if we seize this moment and shape the future of cybersecurity together. AI is fast becoming one of the linchpins of modern business - and with it, modern IT and cybersecurity. In a few short years, our use of AI has shifted from experimental to essential, transforming the way we work and think about work. The overlap here is significant. The emergence of AI-powered systems is reshaping the nature of cyber defence and the rise of Agentic AI introduces both unprecedented opportunities and complex new risks. As AI becomes a powerful cyber shield and a potential attack vector, security leaders must evolve their thinking and tooling to match. Historically, cyber defence has always played catch-up. Threat actors have been able to innovate faster, coordinate better and exploit gaps before organizations can patch them. In the cat and mouse game of cybersecurity, the advantage has been on the attackers' side; after all, they only need to be successful once, while defenders must successfully block threats every time to avoid a breach. AI presents a unique opportunity to flip the script. Imagine a future where vulnerabilities are flagged and resolved before code is ever deployed, where systems can autonomously correct security flaws as they arise and where every endpoint and agent participates in a global, self-healing defence network. If attackers are still leading the innovation curve a few years from now, we'll have missed the moment. Agentic AI promises to play a leading role in this shift. Agentic AI represents a breakthrough and a burden. On one hand, these autonomous agents can respond to threats faster than any human, collaborate across environments and proactively defend against emerging risks by learning from a single intrusion attempt. It's cyber defence at machine speed. On the other hand, these same capabilities can be weaponized. Adversarial AI may soon launch highly targeted attacks that evolve in real time. Using agents, it could execute without human input and bypass traditional defences entirely. When both attackers and defenders operate at microsecond intervals, the nature of cyber conflict transforms. The line between shield and sword has never been thinner. With AI workloads, traditional cybersecurity risks still apply, but they're now compounded by entirely new threats. Prompt injection, LLM jailbreaking, model integrity manipulation and unpredictable agent behaviours are fundamentally shifting how we prepare for, monitor and detect and respond to attacks. Securing AI agents is fundamentally harder than securing traditional systems, because they don't operate on static logic. They learn, evolve and act based on dynamic inputs. This learning scale is only accelerating. Unlike human users, AI agents will perform millions of operations continuously and autonomously. That means managing and protecting vast new populations of non-human identities and transactions. The volume, velocity and variety of this activity demands new security models built for real-time orchestration and adaptability. Before we can secure this new AI-powered environment, we must first see it clearly. The rise of Shadow AI, which are unauthorized or unmanaged AI deployments, makes visibility our first priority. Discovery must become continuous, dynamic and comprehensive, spanning endpoints, networks, cloud workloads and every enforcement point. Once we have visibility, the next step is intelligent control - understanding which models are in use, what data they interact with and whether sensitive information is adequately protected. Data loss prevention, encryption and contextual access controls must evolve to match the fluidity and autonomy of AI. What we truly need is an AI operating system for cybersecurity. In essence, this is an intelligent platform with real-time situational awareness of users, assets, applications and threats across the entire enterprise. It should not just detect change, but anticipate it, acting with context and precision. Think of it as a virtual administrator that understands every employee's intent, behavioural history and risk profile and can make instant decisions to protect the environment. But internal context isn't enough. This AI operating system must also plug into the broader world, ingesting global threat intelligence, adapting to emerging risks and reconfiguring defences based on external events. Imagine a future where security is autonomous, adaptive and always-on. The development of AI-native protocols, such as MCP (model context protocol) and A2A (agent-to-agent communication) is the first step. These standards will allow AI systems to reason collectively and operate as a unified, secure defence fabric. The biggest barrier to this future isn't technology - it's fragmentation. Today, too many organizations still operate in silos, deploying point solutions that don't talk to each other. That's a losing strategy when adversaries are more coordinated than ever. To truly harness AI's potential, we need radical collaboration, shared intelligence across cloud platforms, cybersecurity tools and AI systems. Vendors, customers and even competitors must work together to close the gaps and eliminate blind spots. Ultimately, we must reason together, combining human insight and machine intelligence to anticipate threats before they materialize. In the world of AI-driven attacks, time is the most precious commodity. Traditional patch cycles and response protocols are too slow. We need infrastructure designed for machine-speed resilience. The speed of innovation will spark fresh thinking around trust models, governance and ethics. Ultimately, the promise of AI won't be realized unless we can trust the platforms supporting it. Building that trust requires shared standards, transparent policies and relentless focus on securing data, identities and outcomes. The convergence of AI, cybersecurity and cloud computing is reshaping the digital landscape. The challenges are immense, but so is the opportunity. By embracing collaboration, prioritizing real-time observability and developing intelligent, adaptive systems, we can tip the balance in favour of defenders. Agentic AI can learn from every attack, adapt in real time and prevent threats before they spread. It has the potential to establish a new era of cyber resilience, but only if we seize this moment and shape the future of cybersecurity together.

What Agentic AI really means for enterprise

Businesses can unlock unprecedented gains with Agentic AI, but how? Automation has been a cornerstone of industrial revolutions throughout history, driving productivity, efficiency, and profitability. From Britain's late 18th and 19th-century industrial revolution to the United States' post-World War II boom, automation has radically transformed economies and societies. Today, we stand on the brink of a new wave of automation, powered by agentic AI, which promises to revolutionize business operations in unprecedented ways. In fact, our recent research found that 82% of organizations plan to integrate AI agents by 2027. AI agents are, at their core, software programs that interact with their environment, collect data, and autonomously perform tasks to meet predetermined goals. They represent an evolution from traditional automation technologies like robotic process automation and machine learning that have powered enterprise operations for the past two decades. An agentic AI workflow employs technology such as Large Language Models (LLMs) that perform specific tasks and integrates these in a system that can interact with users and perform tasks autonomously and effectively. Unlike their predecessors, AI agents can perceive, reason, and act in changing environments to achieve their goals, often deciding independently how to reach them, thanks to the explosion in advanced reasoning capabilities of LLMs in recent years. The benefits of agentic AI in enterprises are widespread. We will undoubtably see enhanced customer service, IT support, and overall business functions because of agentic systems in the coming years. By automating complex tasks and integrating with external tools such as web searches, APIs, and dedicated databases, AI agents can execute more sophisticated tasks and collaborate with each other, driving productivity and efficiency. Businesses can use agentic AI to improve and differentiate their offers to customers ahead of competitors, adding communication channels and styles that appeal to specific customer bases. They will also reduce the cost of operations as trust in agents is built, and human oversight is reduced. To build agentic AI systems that deliver real impact and return on investment, businesses have a clear set of tasks. AI agents must have defined roles, need to be able to easily find and locate the data they will use, seamlessly define the tasks or goals they will execute, and set boundaries with guardrails. Multiple agents, each with its own specialized role, can cooperate in a decentralized structure to solve more complex tasks collaboratively. For example, in processing insurance claims, one agent verifies documentation, another evaluates policy criteria, and a third processes payments, completing the task jointly and the user only needing to engage with one interface. As organizations transition toward agentic systems, it's vital that leaders collaborate closely with AI specialists to effectively design and streamline these processes. Integrating AI agents into existing systems can be complex and disruptive if not managed carefully. Building an architecture that accurately reflects real-world activities requires creating digital descriptions and definitions of business operations. Clearly defined tasks can then be mapped to AI agents as needed. Designing systems for human/AI collaboration needs to be front of mind, ensuring that AI agents collaborate seamlessly with human workers. This involves careful orchestration to maintain human oversight and compliance with safety regulations. Data quality and optimization is an easily-overlooked element to consider for the entire agentic architecture. Fragmented data will block AI agents from working effectively. Organizations must assess data quality, implement robust governance and security measures, develop pipelines for real-time data availability, and continuously enhance processes through feedback loops. Governance strategies are also essential for managing AI agents. Human intervention must be a safeguard in case decision-making from AI agents appears to be biased, inaccurate, or breaches company ethics. Testing for compliance and failure, including for bias, fairness, and operational performance, is non-negotiable. Systematic logging of agent activity, capturing performed tasks, actions taken, evaluation metrics, and the agent's internal state, is necessary for effective monitoring and error tracing. The integration of AI agents in various sectors is already underway, and there are huge benefits to be reaped for enterprises. In customer service, we're seeing AI agents automatically draft responses to customer queries based on historical interaction data, taking ownership of client issues and resolving them without human input. For example, an AI agent can request more information from a customer, analyze the enquiry, and offer a solution, even overriding standard procedures if circumstances justify making an exception. This level of autonomy and adaptability enhances customer satisfaction and loyalty. In financial services, AI agents can create personalized investment strategies and dynamically monitor client portfolios. They can also detect fraud by identifying suspicious transactions and initiating appropriate responses. Looking at life sciences, AI agents can support drug discovery by extracting actionable insights from drug mechanisms, disease progression, and clinical outcomes. They can refine clinical trial design and monitor real-time data for mid-trial adjustments, improving the efficiency and effectiveness of research. Manufacturing and retail sectors can also benefit from AI agents. Smart camera-based process monitoring can improve shopfloor performance and safety compliance, while agentic systems monitor shelves in-store and warehouses, automatically triggering stock replenishment using stock-keeping unit codes. The agentic AI future is here, and it's set to revolutionize business operations. Organizations must seize this moment to review their processes for suitability and unlock unprecedented gains in productivity and cost saving. As expertise in adoption spreads, more sectors will join the agentic AI journey, transforming how we work and interact. By harnessing the autonomous, goal-oriented, and adaptive capabilities of AI agents, enterprises can supercharge their functions and stay ahead of the competition. The future of business is agentic, and the time to embrace this transformative technology is now. I tried 70+ best AI tools.

The internet of agents: Why Matteo Zamparini believes interoperability will define the agentic revolution

When people talk about the future of AI, they usually imagine bigger models, faster inference, and smarter chatbots. But what if the real breakthrough isn't about intelligence at all, but coordination? For engineer and innovator Matteo Zamparini, a more fundamental and far less flashy challenge must be solved first: the lack of a common language and shared standards among disparate models. As the founding engineer at PropRise, Zamparini builds autonomous AI agents that help commercial real estate professionals find sites and properties by tapping into data sources ranging from listings to zoning codes, city council meetings, and local news. However, without shared standards and frameworks, building generalized and scalable AI Agents becomes a real struggle. A shared "Internet of Agents," complete with common protocols and frameworks among competing technologies, may provide the answer and unlock AI's potential to fully integrate into the workforce. "These innovations will be the backbone of the autonomous agent era, much like shared internet protocols underpinned the digital revolution," says Zamaparini. PropRise's goal is to automate the mundane work involved in real estate analysis. The platform tracks millions of commercial properties across the United States, using public sources, zoning and housing policies, shifting demographics in individual neighborhoods, ownership data, and purchasing trends to uncover investment opportunities that human analysts might miss or take months to compile. Zamparini's complex AI models run in the background, constantly analyzing new sources and providing new recommendations. These agents go beyond simple chatbots, they are general‑purpose planners that leverage linear agentic workflows and are expected to complete human‑level tasks with incredible accuracy in a fraction of the time a human would take. AI agents like those behind PropRise cannot rely on a simplistic chat interface, tool calling or Chain of Thought. Instead, they rely on a comprehensive orchestration infrastructure, which is why a common language and standard becomes essential. From access to structure: building a unified agent infrastructure PropRise's challenge is not unique. Across industries, large language models highly depend on information inputs and the context, or 'state' as engineers might call it, they need to analyze. Data access only matters if that data is accurate and reaches the right Agent at the right time or 'state.' In Zamparini's eyes, this simple fact makes the emergence of open standards like Model Context Protocol (MCP), AGNTCY or the language BAML the single most important trend in AI. To him, they are the foundational building blocks for the next generation of AI systems, and they already power his architecture. MCP, for example, standardizes how AI models connect to data and tools. Similar to a country's electricity being accessible through the same type of wall outlet, it enables AI agents to seamlessly plug into diverse databases and APIs that follow the same model. Even if considered flawed by many engineers, MCP is the right idea in the correct direction. BAML, on the other hand, is a unified programming language that developers can use to create reliable data outputs, again making it easier for Agents to act upon the correct information and context. "BAML offers a way to build far more reliable and predictable agents", Zamparini explains. Other developers agree, calling the language "faster andcheaper" for developers looking to build and scale AI agents designed to interact with humans and the internet at large. Much like shared internet protocols were essential for the rapid spread of the World Wide Web, shared standards and languages will drive the adoption and usability of AI agents. Interoperability as the missing piece of successful AI adoption Startups are continually looking to add their own flavor of automation in new industry niches or use cases, driving the rapid innovation and adoption of AI technology. However, as the ecosystem of AI agents continues to expand, so does the risk of information fragmentation. If the software powering those agents is difficult to test and maintain, or even worse, is poorly architected, agents risk producing inconsistent results also called "hallucinations". Agents analyzing the same information risk coming to different, or even contradictory, conclusions. This would cast doubt on the industry and fail the promise of AI to power the next industrial revolutions.. Staying with the PropRise example, two real estate investment AI agents tapping into different types of information, or even the same information through different entry points, might not recommend the same property for moving forward due diligence. This could lead to investors comparing the results and losing faith in either recommendation being comprehensive or trustworthy. Zamparini sees parallels to the early days of the internet, which needed shared protocols like HTTP, SMTP, and TCP/IP to become a truly global and comprehensive system. Frameworks like MCP and languages like BAML could have a similar effect, and with efforts like AGNTCY, lead to a developer collective with the aspiration of building an integrated "Internet of Agents". "My aspiration is to contribute to a tech ecosystem where any business can deploy AI agents that work with each other and produce results with four nines of accuracy past the decimal point(thanks to efforts like AGNTCY), access any information needed via standard protocols (like MCP), and output results that are trusted and verifiable (using a language like BAML)," says Zamparini. Without these standards, the future of AI will see isolated and ultimately brittle systems that can't scale. With them, AI systems can become more scalable, interoperating more easily across tools and platforms to create an ecosystem of automated workflows. Why protocols, not platforms, will define the future of AI Matteo Zamparini isn't just advocating for shared standards and a more interconnected AI ecosystem in theory, he's actively working towards it. PropRise has shown the potential of AI agents, and now he's ready to take the next step. The models he builds are only as good as the standards on which they rely and their ability to connect to other information inputs and outputs. Creating this interconnected landscape enables them to become truly comprehensive and able to accurately perform even the most complex tasks. In turn, this ecosystem provides a glimpse into the potential of AI agent models to take on all themundane tasks that slow down professionals and innovators across industries. "In a future where mundane work is no longer a necessary burden, innovation will accelerate at the speed of computation," Zamparini concludes. "I believe it's a future that's much closer than we think."

The $196 Billion Revolution: How Agentic A.I. Is Redefining Corporate Power

With enterprise A.I. expected to top $196 billion by 2034, those deploying agentic agents are poised to shape the next decade of business. A Dutch insurance company quietly automated 90 percent of its automobile claims processing. A global logistics company revolutionized logistics management with A.I. that thinks three moves ahead. Nvidia's security systems now detect and neutralize threats before human analysts even spot them. These aren't experiments -- they're the new reality of business warfare, where the global agentic A.I. market is exploding toward $196.6 billion by 2034, riding a staggering 43.8 percent compound annual growth rate. Sign Up For Our Daily Newsletter Sign Up Thank you for signing up! By clicking submit, you agree to our <a href="http://observermedia.com/terms">terms of service</a> and acknowledge we may use your information to send you emails, product samples, and promotions on this website and other properties. You can opt out anytime. See all of our newsletters As competitors face problems with basic automation, those who have adopted A.I. have systems that plan, decide and work independently. In the next four years, there will be a huge shift in enterprise software; by 2028, 33 percent will feature agentic A.I., up from less than 1 percent in 2024. The companies mastering this technology today will dominate tomorrow's markets. The intelligence gap that's reshaping industries Forget everything you know about A.I. assistants. With agentic A.I., companies move away from reactive tools and get true business partners instead. They handle everything in real time, finding errors, suggesting resolutions and running complex activities without help. Two-thirds of executives using agentic A.I. report measurable productivity boosts, with nearly 60 percent achieving significant cost savings. But the true problems occur at a deeper level. According to Futurum Research, agent-based A.I. will drive up to $6 trillion in economic value by 2028, fundamentally rewiring how business gets done. Real-world transformation in action The evidence is already mounting across industries: Financial Services: A.I. agents at JPMorgan Chase keep an eye on customer finances, find signs of fraudulent activity and instantly stop suspicious transactions. The result? Proactive protection that traditional rule-based systems could never match. Enterprise IT: Jamf's A.I. assistant "Caspernicus" operates directly in Slack, handling software requests for over 70 percent of employees. Staff no longer wait for engineering support -- they get instant help through natural language requests, dramatically improving productivity across all departments. Logistics and Supply Chain: A leading logistics player manages its logistics using intelligent A.I., looking at ongoing data on transport and inventory to improve deliveries without involving humans. Cybersecurity: NVIDIA launched Agent Morpheus, an A.I. framework that uses real-time data processing to automatically detect threats and maintain security, moving from reactive to predictive protection. The economics of autonomous intelligence The economic implications cannot be overstated. In 2024, the agentic A.I. market in the U.S. reached $769.5 million, and it is predicted to grow at a rate of 43.6 percent per year until 2030. But raw market size tells only part of the story. According to MIT, using agentic A.I. to empower employees can make them 40 percent more efficient, and companies that use A.I. for customer experiences have had sales rise by up to 15 percent. The ROI calculations are compelling: 62 percent of polled executives expect returns above 100 percent from agentic A.I. adoption. Enterprise leaders are responding with unprecedented investment. According to a SnapLogic survey, 79 percent of IT decision-makers plan to invest over $1 million in A.I. agents over the next year. The clear message: staying ahead in the market now depends on investing in technology. The multi-agent enterprise: beyond single-point solutions The next evolution is already emerging: networks of A.I. agents collaborating like digital teams. Consider the following scenario that reflects current deployments in leading companies. A logistics agent detects a supply chain disruption. It instantly alerts procurement agents to source alternative suppliers while a finance agent rebalances cash flows to reflect the changes. Customer service agents proactively notify clients with updated timelines. No central system orchestrates this -- the agents self-organize around business objectives. Deloitte predicts that in 2025, 25 percent of companies using generative A.I. will launch agentic A.I. pilots, growing to 50 percent in 2027. The technology has moved from concept to deployment faster than any enterprise technology in recent memory. Platform wars: the new competitive landscape The competitive dynamics are already crystallizing. Over 400,000 A.I. agents were built using Microsoft's Copilot Studio in the previous quarter, which over 160,000 organizations have adopted. Salesforce, IBM, Google and Oracle are racing to capture market share with their own platforms. But the real battlefield isn't in Silicon Valley -- it's in boardrooms where executives must choose between being disruptors or being disrupted. Eighty-nine percent of surveyed CIOs consider agent-based A.I. a strategic priority, yet 60 percent of DIY initiatives fail to scale past pilot stages due to unclear ROI. The implementation reality: success factors and pitfalls Despite the promise, deployment isn't automatic. Nearly three-quarters of senior leaders believe agentic A.I. could give their company a significant competitive advantage. Still, half say it will make their operating model unrecognizable in just two years. Most effective implementations move in this organized direction: Phase 1: Infrastructure Readiness. Exposing enterprise tools and data via APIs, ensuring system interoperability and building monitoring and control frameworks. Phase 2: Targeted Deployment. Starting with high-impact, data-rich processes prone to coordination bottlenecks such as incident resolution, customer onboarding and claims processing. Phase 3: Multi-Agent Orchestration. Allowing agents to collaborate across functions, creating peer-to-peer protocols for coordination. Phase 4: Organizational Redesign. Transitioning to hybrid structures where humans and agents share workflows. The governance challenge The autonomy that makes agentic A.I. powerful also creates new risks. Seventy-eight percent of CIOs cite security, compliance and data control as primary barriers to scaling agent-based A.I. Accountability, bias and ethical issues emerge whenever A.I. systems do things by themselves. Leading organizations have been building robust guardrails since day one. IBM watsonx Agents lead governance with enterprise-ready features including role-based controls, compliance auditing and A.I. explainability safeguards. The disruption timeline: why speed matters The transformation is accelerating beyond most predictions. By 2029, Gartner predicts 80 percent of common customer service issues will be resolved autonomously, and 15 percent of all day-to-day work decisions will be made by A.I. Some companies have already benefited from early action. For example, a leading Dutch insurer automated 91 percent of individual automobile claims by integrating custom A.I. agents, enabling adjusters to focus on complex cases requiring human knowledge. Competitors still processing claims manually face an insurmountable cost and speed disadvantage. Industry-specific disruption patterns Companies across sectors have different use cases and transformation timelines: Financial Services: Leading the charge with fraud detection, credit assessment and regulatory compliance automation. Healthcare: A.I. agents managing appointment scheduling, patient monitoring and treatment personalization are showing early success. Manufacturing: Predictive maintenance and supply chain optimization are delivering immediate ROI. Customer Service: In 2024, the customer service and virtual assistants sector led in revenue generation, driven by A.I. agents' ability to address both straightforward and complicated issues. The strategic imperative: building the agentic enterprise The change to agentic A.I. isn't limited to technology; it becomes a key moment in companies' competitive plans. Organizations face a binary choice: become agentic enterprises where autonomous A.I. agents work seamlessly alongside humans, or fall behind competitors that do. Half of executives surveyed by PwC believe A.I. agents will make their operating model unrecognizable in just two years. In every field, there will be a major and sudden separation between those who adapt and those who do not. The organizations that will do well in 2030 will be smarter, able to spot trends, make changes accordingly and look for opportunities without the need for constant human input. They'll operate at speeds and scales impossible for traditionally-managed competitors. The bottom line Agentic A.I. isn't a technology to deploy -- it's a new way of operating to design. With the global enterprise agentic A.I. market growing at 46.2 percent annually and expected to reach $41.32 billion by 2030, the window for competitive advantage is narrowing rapidly. The companies that master agentic A.I. in the next 18 months will set the terms for the next decade of business competition. People or businesses that don't take risks often fade away in the annals of their industry. The changes we want are happening now, not in the future. The only question is whether your organization will lead it or be left behind.