What is an agentic enterprise?

Agentic enterprises benefit from high adaptability, as advanced agentic systems not only streamline workflows and automations but also dynamically respond to changing business conditions in real time. By assigning artificial intelligence (AI) agents more autonomy and responsibility, humans can prioritize driving innovation, refining strategy and maintaining relationships with partners and customers, among other high-value objectives.

More than 60% of CEOs say that their organization is actively adopting AI agents, according to a 2025 IBM study. But few organizations have managed to integrate agentic AI solutions across every department at scale.

One reason is that, as a new technology, agentic AI comes with novel security, technological and governance risks and considerations. The Harvard Business Review found that just 6% of organizations fully trust agents “to autonomously handle core end-to-end business processes.” Top concerns include privacy and cybersecurity vulnerabilities, data output quality and infrastructure limitations.

As a result, many agentic solutions remain fragmented and siloed across disparate services and departments, limiting their effectiveness. Organizations might deploy agents to tackle narrow use cases or confine agents to testing or staging environments, paired with strict guardrails and extensive human oversight. Others might use agents embedded in third-party SaaS applications—but have yet to design and manage internal agentic applications.

Due to these operational challenges, the agentic enterprise—where advanced, multi-agent workflows are embedded and integrated across every part of an organization’s ecosystem—remains a theoretical concept more than an operational reality. However, this paradigm is quickly shifting as organizations rebuild processes and security frameworks to accommodate emerging agentic capabilities.

Agents can now call external tools and “reason” through multi-step problems, expanding both their usefulness and cost-effectiveness. At mature implementation stages, agents not only handle automated, routine tasks but also collaborate on more complex workflows, such as drafting marketing campaigns, troubleshooting customer service issues or forecasting supply chain disruptions.

The role of humans is also shifting away from repetitive, manual work toward high-level management. Employees increasingly use enterprise AI as a copilot or collaborator, and in multi-agent systems, they guide agents through complex tasks by providing high-level orchestration, administration and monitoring.

Before exploring the key elements of agentic enterprises, it’s important to understand the distinctions between different types of agents.

While enterprises have used traditional software agents and automations for decades, the agentic enterprise introduces generative AI-powered agents (or simply AI agents), which are more sophisticated but also more operationally complex. Exact definitions vary, but “software agent” is an older term that generally refers to any program that can act on a user’s behalf. Examples include web crawlers, customer service assistants, spam filters and recommendation engines.

An automation is a specific type of software agent that uses scripts to perform repetitive tasks with limited human intervention. One basic example is a payroll software that can automatically distribute funds on payday. This automation requires relatively little maintenance and oversight—the same task can be performed repeatedly with minimal variation.

However, traditional, rule-based automations often struggle to handle uncertainty and edge cases, and generally react to events as they occur, rather than anticipating them in advance. The automated payroll system, for example, might be unable to resolve an urgent, mid-cycle correction without human intervention.

Generative AI agents, meanwhile, can proactively tackle open-ended tasks (often defined through natural language prompting) by evaluating multiple options and by using prior training to inform decisions. Developers do not have to explicitly program their actions beforehand.

Automations, software agents and GenAI-powered agents exist on a continuum, and the distinctions between each can be context-dependent. Automation tools, for example, increasingly incorporate generative AI capabilities.

Examples of automation include:

• An observability platform that sends a notification to IT teams each time an application goes offline.
  
  
• An e-commerce site that continuously corresponds with an inventory management platform (often through APIs) to maintain accurate stock levels.
  
  
• A telemetry system that routinely sends logs and metrics to a data lake for long-term storage.

Examples of agents include:

• A programming platform that can generate code based on natural language prompts or autonomously spot errors in existing code.
  
  
• An AI-powered note-taking app that not only generates summaries of meetings, but also schedules follow-ups, emails participants and updates project management systems with new tasks.
  
  
• A supply chain platform that can anticipate product shortages, create contingency plans and initiate orders (with human oversight).

Becoming an agentic enterprise requires not only infrastructure and technology upgrades but also a shift in AI strategy. Key features include:

Because specially trained agents are often more effective at scoped tasks compared to general-purpose large language models (LLMs), agentic enterprises can deploy multiple agents, each with their own strengths, to collaborate on shared projects.

For example, a marketing department might use a team of intelligent agents to generate new campaign ideas by using a data agent to surface relevant statistics, an LLM-based agent to write copy and a vision language model (VLM), which combines natural language processing and computer vision capabilities, to generate graphics. Other agent instances can be used to oversee and review each step, make revisions and send drafts to a human for further review.

Agents can perform actions across disparate services, environments and data sources, helping teams overcome incompatibilities, drive deeper and more accurate analysis and improve workflow optimization. Unlike traditional automations, agents can draw from both short-term and long-term memory to continually improve outputs.

They can also perform web searches, make API calls, write code and use third-party services, extending their capabilities. When embedded across the ecosystem, these features help agentic enterprises improve efficiency, reduce costs and give developers more time to innovate.

Agentic enterprises use AI to help anticipate errors and inefficiencies, suggest potential fixes and deploy updates with limited human oversight. Employees can use natural language to describe business goals and expect agents to design their own workflows to tackle them. With agents handling repetitive, low-risk work, humans can focus on high-level strategy and innovation.

Because agents are non-deterministic and can act unpredictably, agentic enterprises must employ robust guardrails, controls and risk management pipelines to help maintain governance and compliance. A centralized control interface, where developers can monitor agentic behaviors and adjust permissions, cost controls and other parameters, can help teams maintain end-to-end control, balancing agentic autonomy with security and auditability.

A wide range of businesses have begun incorporating autonomous agents into both internal workflows and customer-facing products. Organizations might begin by embedding agents in just one or two workflows before expanding these capabilities across the enterprise. Use cases include:

Transforming into an agentic enterprise can be a daunting task, especially for organizations with limited development and technical resources. While exact steps vary by enterprise size and industry, and the particular goals and capabilities of each organization, this playbook can serve as a practical starting point:

Organizations can embrace industry standards and open protocols so that AI tools can interact with connected services and data sources without the need for custom connectors. Container-based frameworks provide high interoperability and portability (they work across a wide range of environments, including multicloud and hybrid) by packaging components into standardized units. This approach helps teams balance security and oversight with flexibility and agility.

Although the goal might be to eventually integrate agents across the business, organizations can begin by identifying a few critical tasks that are well suited for agentic automation, especially time-consuming and resource-intensive workflows that agents can execute with high reliability and relatively low risk. For example, lead generation takes considerable resources and is tied directly to enterprise revenue, making it an ideal candidate for an agentic pilot.

However, teams should carefully review the impact of early experiments before expanding into full-scale production. Scaling too quickly, especially without first verifying a pilot’s effectiveness, can increase the risk of misalignments, security vulnerabilities, runaway costs and downtime.

Instead of using the same general-purpose LLM across multiple departments and use cases, organizations can design specialized agents that are optimized for specific workflows, improving efficiency, performance and security. AI systems built for specialized tasks can incorporate reusable components, reducing operational strain and accelerating development timelines.

Because models use real-world and synthetic data as training materials, data integration is a key prerequisite to establishing an agentic enterprise. With publicly available and proprietary data (both structured and unstructured) fully integrated into the enterprise ecosystem, models can not only make better-informed decisions but also dynamically recalibrate their predictions in real time.

Agentic enterprises aim to maximize the time that employees spend on innovation, customer engagement, research and development and other high-value tasks by delegating routine, repetitive work to agents and shifting employees’ involvement in those tasks to management and supervision. Human-in-the-loop processes, where humans continually review and fine-tune agentic actions, help ensure outputs that are compliant, ethical and aligned with business goals.

Agentic enterprises can identify key performance indicators (KPIs) tied to specific business goals, helping measure the effectiveness of agent implementations. This strategy is especially important as organizations scale up from pilots to production. Teams can invest heavily in agentic tools that result in tangible productivity gains or cost savings while quickly retiring ineffective tools. At the same time, observability tools help teams continuously monitor drift, bias, error rates and other factors to optimize performance and accuracy.

Organizations might struggle to adopt fully agentic workflows due to the high initial cost of implementation and maintenance, the risk of agents producing biased or erroneous outputs and the difficultly of interpreting agents’ decisions (the black box problem). Additional challenges include:

A 2026 Outsystems study found that among 1,900 IT leaders, just 36% have a centralized approach to AI governance. Many organizations manage AI oversight and optimization on an ad hoc, case-by-case basis. This decentralized approach can lead to misalignments, security vulnerabilities and data silos.

To address this problem, many organizations use a hub and spoke approach, where individual teams maintain limited control over their agentic deployments while receiving guidance and support from a centralized IT team. A detailed ownership framework, meanwhile, helps teams understand which deployments they’re responsible for maintaining and supervising.

AI models can extract insights from both structured and unstructured data generated by IoT devices, microservices and other sources. However, this broader data access introduces new risks; models might unintentionally reveal sensitive or private data, generate inaccurate or biased responses or misuse copyrighted materials.

In fact, half of executives cite the risk of intellectual property infringement as a major barrier to enterprise-wide agent adoption, according to a 2025 IBV study. To improve data management, organizations can build robust data classification, lineage tracking and permission systems, paired with human oversight and auditing pipelines.

Employees might be resistant to adopting new AI-driven workflows and tools—or might lack the time or resources needed to learn how to use them. To improve AI literacy and accelerate adoption, organizations can offer training and mentorship programs, reward innovative agentic deployments, develop prompting libraries and guides and tie agentic responsibilities to career development and progression.

Agentic solutions can introduce unexpected or runaway costs, especially as organizations move from experimentation to full-scale deployments. Agents often rely on high-performance GPUs, which use far more compute than traditional CPUs. Inferences (when a model applies its training data to generate new outputs) add extra costs, especially for context-heavy, multi-step queries. Organizations can institute several policies to keep costs under control, including model routing (selecting the optimal model for a specific request), caching, batching and token capping.

Organizations might be unable to rebuild their architectures from scratch to accommodate new agentic workflows. For example, critical automations might involve legacy systems that can’t be easily replaced. To help embed agents into existing frameworks, IT teams can reconfigure APIs and other types of middleware to facilitate connections while incorporating a control plane to manage data routing and governance.

This process might entail embedding APIs with more detailed metadata so that agents know how to interact with them; assigning APIs more granular, reusable functions, which enables agents to combine them to perform complex tasks; and redesigning APIs so that they can support asynchronous, event-driven workflows.

Model context protocols (MCPs) are an emerging standard for connecting agents to external services. Like APIs, MCPs rely on a client-server architecture. Organizations often use them on top of existing APIs to standardize how agents discover and call available tools and services.

While building an agentic enterprise requires significant financial and developmental resources and introduces new operational challenges, agentic investments can greatly improve competitiveness and sustainability in the long term. Benefits include:

An agentic enterprise might deploy agents to provide a personalized customer experience tailored to clients’ specific needs and context. For example, a customer-facing agent can recommend products based on previous purchases, track orders in real time and help clients troubleshoot and resolve technical issues.

Nearly 70% of executives cite “improved decision-making” as the top benefit of incorporating agents, according to a 2025 IBM study. GenAI-powered agents can continuously adjust their decisions based on new evidence, helping teams proactively respond to changing business conditions. Agents also reduce the need for manual handoffs and approvals, and when hosted in the cloud, can dynamically scale based on real-time demand.

Because agents can reason across disparate data sources and applications, they can identify data patterns and dependencies with more precision than traditional analytics systems. With deeper context into service dependencies and historical behavior, agentic systems can help deliver faster root cause analysis, more accurate financial forecasting and improved predictive maintenance, among other benefits.

With agents handling routine tasks, human employees can focus on projects that require creativity and innovative thinking, such as developing and deploying new products and features. Agents can also reduce burnout by streamlining previously tedious processes—for example, surfacing data spread across multiple services or hidden inside large, unstructured databases.