AI agents for business

Understanding AI agents for business

What is agentic AI, and how does it differ from traditional AI?

AI agents are autonomous reasoning engines that can understand context, plan workflows, connect to external tools and data, and execute actions to achieve a defined goal, learning and adapting as they go. Agentic AI refers to the broader set of systems that orchestrate AI agents, Collectively, these systems have emerged as subset of artificial intelligence, alongside other subsets like machine learning, natural language processing, and deep learning.

AI agents differ from preceding AI innovations in several ways:

• Large language models (LLMs) provide logical outputs based on fixed inputs and rules. They’re trained on highly structured data sets, outputting text, numbers, dates, true/false results, etc. within a narrow domain of operation.
• Generative AI (GenAI) introduced self-learning capabilities and expanded content generation to composition, imagery, video, and sound. It can analyze unstructured data sets and learn how to produce and improve outputs instead of relying on predefined rules.
• AI agents don’t just react; they reason and take action on behalf of users—evolving the input-output paradigm. Instead of serving in assistive, reciprocal roles, AI agents act as skilled collaborators, breaking down goals into steps, making decisions based on context, working with humans and other agents as multiagent systems, and learning from results.
  

AI agents for business are a breakthrough that helps organizations go beyond productivity and efficiency into wholesale process reinvention. For a deeper look at multiagent AI systems, read our seminal report covering AI agent use cases, benefits, and impacts on business.

How do multiagent systems work in enterprise environments?

Multiagent systems are taking business process automation to the next level through their ability to understand context, learn dynamically, make decisions autonomously, and act collaboratively. Whereas traditional robotic process automation (RPA) automates structured, repetitive, rules-based tasks, agentic AI-powered automation automates entire workflows, elevating humans to high-level visioning and opening the door to enterprise reimagination. Imagine outcomes such as:

• Personalized, adaptive processes and experience design;
• Self-healing automation that corrects inefficiencies; and
• Agent-managed supply chains, contracts, and more.
  

Learn about the difference between RPA and autonomous AI for business process automation with a comparison guide, examples of agent-driven processes, and a breakdown of current and future intelligent automation trends.

What are the top AI agent use cases?

The number of AI use cases and applications is increasing across every industry and sector. In practice, AI agents can serve in a user-facing capacity, collaborating with and acting on behalf of people, or they can work autonomously in the background as part of larger processes and systems. Industry-specific AI agent use cases include:

Explore more industry-specific agentic AI use cases in our AI Dossier and filter by Type > Agentic AI.

What ROI can businesses expect from agentic AI?

ROI from AI varies based on use case complexity, implementation scope, and organizational readiness, but early adopters are seeing returns across multiple dimensions. According to our 2026 State of AI in the Enterprise report:

For high-volume transactional tasks, such as customer service, data entry, and claims processing, AI agents can improve cost reduction and speed.

For knowledge work, like research, analysis, and content creation, ROI takes the form of productivity and quality through error reduction and insight generation.

For more complex decision support in areas like fraud detection, risk assessment, and supply chain optimization, agentic AI can improve fraud detection rates, inventory optimization, and loss and waste prevention.

Agentic AI insights about implementation and operations

What are the principles of AI agent architecture and design?

Like human workers, AI agents must be carefully selected, well trained, and properly equipped to do their jobs. As a result, the principles of agentic architecture echo themes from organizational design and human resource management. These principles include:

• Using a domain- and role-based approach;
• Balancing agent responsibilities;
• Controlling access to data and tools; and
• Employing reflective cycle of improvement through self and peer assessments.

Principles of more complex multiagent system design and management include explainability, human-in-the-loop supervision, and careful consideration of the data patterns, ecosystem integrations, and ethical guardrails the system needs to achieve its goals.

Explore the full list of AI agent architecture principles in detail along with a sample reference architecture for agent-powered transformation.

How do you get started with agentic AI enterprise adoption?

Agentic AI enables leaders to reimagine business with new capabilities like cognitive automation, agent-to-agent communication, and agent orchestration in mind. Consequently, it’s helpful to take a step back and ask: “How would you recreate your organization in light of agentic AI?”

Our agentic AI enterprise adoption guide outlines four steps to get started:

• Imagine an agent-first future state.
• Articulate the agent-enabled process.
• Experiment, pilot, and learn.
• Shift into agentification at scale.

To move forward with implementing AI agents, you’ll need to define a strategy. Two prevailing agent deployment strategies have emerged from our agentic AI research in recent years: an iterative approach, where organizations graduate from foundational architecture and single-task pilots to fully autonomous AI systems, and a radical approach, where you agentify target processes at an accelerated rate.

For more detail on strategy, costs, vendor ecosystems, workforce management, and risk mitigation, read our guide to enterprise agentification.

What are the risks and governance considerations for agentic AI implementation?

The shift from human-powered to agent-powered operations requires stronger measurement and oversight mechanisms, as AI agent actions affect business outcomes, customer relationships, and compliance obligations. Without proper AI agent observability and monitoring, autonomous AI agents can compound errors at scale, amplifying risk rather than mitigating it.

AI agent risk and governance considerations span several categories:

• Technical and operational risks: Accuracy is a key concern. AI agents may “hallucinate”—confidently presenting inaccurate information or taking action based on flawed reasoning or confidently presenting inaccurate information or taking action based on flawed reasoning.
• Security and privacy concerns: Providing AI agents with access to sensitive data and system privileges creates new attack surfaces and points of failure, especially within complex integrations. Prompt injection attacks, where bad actors manipulate agents to output private information, are also a concern.
• Regulatory and compliance concerns: In addition to existing frameworks like the EU AI Act, GDPR, industry regulations, and consumer protection laws, our research suggests more agent-focused regulation is coming in the future.

Read our report on implementing AI governance and AI agent observability to monitor and enhance performance while protecting against unexpected costs and business risk.

More agentic AI trends

What predictions can we make about the future of AI agents?

With its ability to reshape business models, workforce roles, and operational paradigms, multiagent collaboration has set the stage for profound business transformation. Our Agentic enterprise 2028 report highlights the most significant anticipated advancements in the near term, including:

• Higher levels of autonomy and proactive decision-making;
• Complex, agent-orchestrated workflows; and
• Enterprise-wide applications driven by low/no-code integration capabilities.

Explore the future of AI agents with a detailed side-by-side comparison of today versus tomorrow and an “autonomy ladder” framework to help organizations grow into autonomous AI maturity.

Putting our agentic AI research to work

The journey to understand and implement AI agents for business is unfolding across industries. Leading enterprises start with clear use cases tied to business value, invest in proper architecture and data foundations, establish governance from the outset, and commit to organizational change—fundamentally reimagining how work gets done.

Meanwhile, the agentic AI landscape is evolving rapidly, with new capabilities, frameworks, and best practices emerging constantly. Whether you’re exploring your first pilot or scaling across your enterprise, bookmark these agentic AI insights to stay current and build incrementally while keeping your future state in focus. The question isn’t whether AI agents will transform your industry—it’s whether you’ll lead that transformation or follow it.