Agentic AI: Transforming Enterprises and Shaping Our Future

The landscape of artificial intelligence is experiencing a profound shift with the emergence of agentic AI. This advanced form of AI is not merely following predefined instructions; instead, it adapts, learns, and operates autonomously to achieve specific goals, often interacting with other AI agents without direct human oversight. This rapid evolution is poised to deliver not only swift technological breakthroughs but also a significant societal transformation, altering how we live, work, and engage with the world.

Industry experts anticipate a dramatic increase in agentic AI adoption within the enterprise. Research firm Gartner projects that by 2028, a substantial 33% of enterprise software applications will incorporate agentic AI, a sharp rise from less than 1% in 2024. This integration is expected to enable autonomous decision-making for 15% of daily work tasks. Unlike conventional AI, which relies on static rules or algorithms, agentic AI systems are designed to make decisions and execute tasks independently, even communicating directly with other AI agents with minimal or no human involvement. This inherent autonomy allows machines to engage with the physical world with an unprecedented level of intelligence, facilitating complex operations in dynamic environments. Such capabilities hold particular promise for sectors grappling with labor shortages or hazardous working conditions. However, the ascent of agentic AI also introduces critical security and ethical considerations, necessitating robust governance frameworks and rigorous testing to ensure these autonomous systems operate safely, transparently, and responsibly.

The Rise of Autonomous Systems in the Enterprise

The growing influence of agentic AI within the enterprise signifies a fundamental paradigm shift, moving beyond traditional AI applications to systems that exhibit genuine autonomy. This evolution brings both immense opportunities and complex challenges, forcing organizations to rethink their operational strategies and risk management frameworks.

Strategic Imperatives for CIOs

Chief Information Officers (CIOs) are at the forefront of this transformation, tasked with aligning strategic IT priorities, mitigating novel security risks, and reskilling their workforce for an era defined by agentic AI. As early as September 2025, enterprises recognized the need to construct production-grade agentic AI platforms. Key components for such systems include modular orchestration, fail-safe design, hybrid memory management, and seamless integration of large language models (LLMs) with specialized domain knowledge. These elements are crucial for enabling agentic AI systems to reason, act, and adapt at scale effectively.

The retail giant Walmart, for instance, is actively leveraging its artificial intelligence strategy to maintain its market dominance, with its U.S. EVP and CTO emphasizing AI’s pivotal role. Similarly, Adobe has made its Experience Platform (AEP) Agent Orchestrator and six new AI agents generally available, designed to build, deliver, and optimize customer experience and marketing campaigns. Adobe also introduced Experience Platform Agent Composer, allowing customization of AI agents based on brand guidelines and organizational policies. These developments highlight the practical application of agentic AI in enhancing business efficiency and customer engagement.

Evolving IT Structures and Workforce Dynamics

The proliferation of agentic AI is expected to dramatically reshape organizational charts and potentially eliminate traditional functional roles. Microsoft envisions an “agent boss” era where human roles shift towards delegating and orchestrating a multitude of bots. This necessitates a re-evaluation of how IT organizations are structured, how teams collaborate, and what skill sets are most valuable. The debate extends to who within the enterprise should manage AI agents and how these autonomous entities should be governed. Some experts predict that agentic AI could even disrupt the Software-as-a-Service (SaaS) market, with groundbreaking AI tools potentially rendering current business models obsolete. This theory, voiced by figures like Microsoft CEO Satya Nadella, underscores the transformative potential of agentic AI to redefine core business processes.

Salesforce AI Research is also actively contributing to this evolution, unveiling a simulated enterprise environment, a new benchmark, and a tool for unifying account data. These initiatives are designed to assist customers in their transition to agentic AI enterprises, streamlining complex workflows and enhancing data utilization. Meanwhile, companies like IFS are integrating agentic AI capabilities directly into their ERP platforms, as demonstrated by the acquisition of TheLoops. This acquisition brings a full Agent Development Life Cycle (ADLC) platform, enabling enterprises to design, test, deploy, monitor, and fine-tune AI agents with built-in support for versioning, compliance, and performance optimization. These industry moves illustrate a strong commitment to embedding agentic AI deeply within enterprise operations.

Navigating the Security Landscape of Autonomous AI

While agentic AI promises remarkable advancements, it simultaneously introduces a new frontier of cybersecurity risks. The autonomous nature of these systems, their ability to interact with diverse data sources, and their potential to make independent decisions create complex security challenges that demand immediate attention.

Red Teaming and Threat Mitigation

As more enterprises deploy agentic AI applications, the potential attack surface expands significantly in complexity and scope. To counter this, cybersecurity experts are advocating for the adaptation of traditional red teaming and penetration testing techniques for the AI world. These updated methodologies focus on identifying vulnerabilities specific to AI agents and autonomous systems. CrowdStrike, a major cybersecurity firm, is betting heavily on agentic AI, launching its Agentic Security Platform and Agentic Security Workforce. These offerings aim to leverage real-time intelligence, automation, and a common language for defense to stay ahead of AI-driven adversaries. Cisco is similarly enhancing its AI-driven security capabilities, including improved threat detection and response in its XDR and Splunk Security platforms, new AI agents, and integration with ServiceNow SecOps.

The Model Context Protocol (MCP), while fueling agentic AI by enabling AI assistants to connect to data and services from various vendors, also introduces significant security risks. With thousands of MCP servers now available, and agentic AI seen as the future of IT, the protocol’s widespread use makes it an attractive target for malicious actors. Companies are increasingly recognizing the imperative of data security as AI agents manage internal data movement. To address this, some tech players are adopting “confidential computing,” a concept that has gained renewed relevance with the rise of generative AI. This approach ensures data remains encrypted even during processing, offering an additional layer of protection against sophisticated threats.

The Dual Nature of Agentic AI in Security

Agentic AI presents a paradox for security professionals: it is both a powerful tool for defense and a potential weapon for adversaries. While agentic AI can rapidly generate code, identify and neutralize threats, and significantly boost productivity, its dependability remains a critical concern. Security researchers have noted that AI agents, in their current state, can be easily tricked into performing “stupid or dangerous things,” adding weight to the understanding that AI agents are not yet infallible. This duality necessitates a cautious and strategic approach to deployment, ensuring that guardrails are in place to prevent misuse or unintended consequences.

Microsoft has introduced new AI agents for its Security Copilot platform, designed to automate critical cybersecurity functions like phishing detection, data protection, and identity management. These tools aim to help organizations combat increasingly complex and fast-moving digital threats. Furthermore, Microsoft’s newest Copilot AI agents can detail their reasoning process, providing real-time answers on how data is analyzed and sourced to reach results. These “Researcher” and “Analyst” agents delve into data sources such as email, chat, or databases to produce reports, analyze strategies, or convert raw information into meaningful insights, enhancing transparency and trust in autonomous operations.

The Transformative Potential Across Industries

Agentic AI’s capabilities extend far beyond general enterprise operations and cybersecurity, promising to revolutionize various sectors by automating complex tasks, enhancing decision-making, and fostering innovation. From healthcare to finance and manufacturing, industries are actively exploring and implementing agentic AI solutions.

Sector-Specific Innovations

In healthcare, agentic AI holds the potential to redefine patient care, particularly in fields like oncology. Nigam Shah, CDO for Stanford Health Care, highlighted at Microsoft Build 2025 how agentic AI can alleviate the administrative burden on physicians, combating burnout and improving efficiency. In finance, retail, and healthcare, IT leaders are already leveraging AI agents for current operations and envisioning a future where this technology drives organizational and industry-wide advancements. For example, agentic AI is poised to simplify complex tasks such as cryptocurrency trading and digital asset management by automating decisions, improving accessibility, and masking technical complexities. Google is also advancing this integration, unveiling an Agent Payments Protocol (AP2), an open framework developed with over 60 payment and technology companies, to support secure, agent-led transactions across various platforms and payment methods.

The manufacturing and construction sectors, along with other industries involving significant field operations, are set to benefit immensely from mobile AI agents, leading to enhanced operational agility. Riverbed is bolstering its AIOps platform with predictive and agentic AI, transforming how IT organizations manage complex distributed infrastructure and data. This shift from reactive to predictive IT operations is crucial for maintaining efficiency and stability in an increasingly intricate technological landscape. These advancements demonstrate a broad recognition of agentic AI’s ability to drive efficiency and innovation across diverse industrial applications.

Advancing Development and Interoperability

The development of agentic AI is being actively supported by major technology players. Google is adding a new open-source framework for building agents to its Vertex AI platform, the Agent Development Kit (ADK), which allows developers to create AI agents with minimal Python code. This initiative aims to democratize agent development and accelerate adoption. Furthermore, Google’s Agent2Agent (A2A) open protocol seeks to connect agents across disparate ecosystems, addressing the challenge of interoperability between agents built on different vendor platforms. This protocol is crucial for enabling enterprises to adopt agents more readily, facilitating seamless communication and collaboration among autonomous systems.

Nvidia and ServiceNow have collaborated to engineer an open-source AI model called Apriel, designed to help companies create learning AI agents to automate corporate workloads. This model, available on HuggingFace, will empower AI agents to make decisions across IT, human resources, and customer service functions. Nvidia also launched the AgentIQ toolkit, an open-source software library to help developers connect disparate agents and agent frameworks, further promoting interoperability and the development of robust agentic AI ecosystems. Deloitte has also unveiled Zora AI, a new agentic AI platform offering a portfolio of AI agents across finance, human capital, supply chain, procurement, sales and marketing, and customer service, integrated with major enterprise software platforms. These collective efforts highlight a concerted industry push toward making agentic AI development and deployment more accessible and efficient.