Agentic AI in enterprise software is generative-AI-based software that can interpret a goal, choose steps and take actions through connected tools or business systems, within defined permissions and oversight. It differs from a fixed script because it can respond to less predictable situations and work across multiple steps—but the term “agent” is not standardized, and products described that way do not all have the same capabilities or autonomy.
What makes enterprise software “agentic”?
In this article, an enterprise AI agent means a generative-AI-based software system that can interpret a goal, choose steps and take actions through tools or connected business systems, within defined permissions and oversight. This is a practical synthesis of Microsoft’s adoption guidance and IBM’s overview of the agentic enterprise, not a formal industry standard.
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The key distinction is that an agent can connect language-based reasoning to actions. A conventional automation generally follows a predefined sequence for repeatable tasks. An AI agent may use tools, account for changing inputs and decide what to do next across several steps. The boundary is not universal: older software agents and scripted automation are also sometimes called agents, and an agent’s actual authority depends on how it is built and configured.
Why the topic matters now
Agents are drawing attention because they offer a way to link generative AI with business workflows rather than limiting it to producing text or answering questions. IBM’s May 2026 overview reports that more than 60% of CEOs said their organizations were actively adopting AI agents, attributing that figure to an IBM study conducted in 2025. The same page reports that 6% of organizations fully trust agents to autonomously handle core end-to-end business processes, attributing the figure to Harvard Business Review without stating its year. These are differently sourced signals of interest and caution, not directly comparable measures of deployment.
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Those figures do not establish that agents are widely running core business processes without supervision. Moving from trials to dependable operations involves access controls, monitoring, business ownership, escalation paths and ongoing maintenance. Microsoft’s adoption guidance and agent governance guidance address these organizational requirements.
What enterprise agents might do
IBM describes potential applications across business functions. These are examples of workflows organizations might explore, not evidence that a given deployment will perform reliably, reduce costs or deliver a particular return.
IT operations
An agent could classify and route support tickets, resolve some requests, help identify coding errors or assist with anticipating service problems. The scope of its actions would depend on the systems it can access and the permissions it receives.
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An agent could help troubleshoot a customer issue by combining the conversation with permitted customer records and workflow tools. More consequential steps—such as changing an account or committing to an outcome—may need human review.
Marketing workflows
A coordinated workflow might gather relevant data, draft copy and generate graphics. The potential value is in linking steps; it does not remove the need to check factual accuracy, brand requirements or approvals.
Supply-chain planning
An agent might flag a possible shortage, develop contingency options and prepare an order. IBM’s example includes human oversight, an important distinction when a recommendation can lead to a financial or operational commitment.
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What organizations need before scaling
Microsoft’s maturity framework treats adoption as more than a software decision. It covers AI strategy and experience, business strategy and process transformation, governance and security, technology and data, and organization and culture. Its adoption guidance also groups the work around planning, governance and security, building, and managing agents. A useful readiness review therefore connects each proposed workflow to its business owner, technical environment and risk level.
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- Identity and data permissions: establish which identity the agent uses and what information and actions that identity is permitted to access.
- Governance fit: match safeguards to the purpose and risk of the initiative rather than treating every agent as equally consequential.
- Observability and audit: determine how teams will inspect the agent’s behavior, decisions and actions.
- Human oversight: specify which actions require approval and who handles exceptions or escalation.
- Lifecycle ownership: assign responsibility for monitoring, maintenance, evaluation and retirement.
These are practical evaluation dimensions derived from Microsoft’s published guidance; they are not a certification checklist or a vendor ranking. IBM’s and Microsoft’s materials are vendor-published guidance, not independent head-to-head comparisons.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Risks created by delegated access
An agent that can use business systems operates with some degree of delegated authority. Microsoft’s governance guidance emphasizes an enforceable baseline that fits existing identity, data-governance and security practices. Without clear boundaries, possible problems include unintended data exposure, inconsistent behavior, unclear accountability, uncontrolled proliferation of agents and operational costs.
For each proposed deployment, decision-makers should be able to answer:
- What identity does the agent use, and which systems and data can it access?
- Which actions can it take independently, and which require approval?
- Are its actions and relevant behavior recorded in logs or telemetry that the responsible team can review?
- Who responds when it encounters an exception, behaves unexpectedly or needs to escalate?
- Who monitors and maintains it over time, and who decides when it should be changed or retired?
Controls described in Microsoft’s governance guidance include baseline security and compliance boundaries, observability, defined human oversight and escalation, lifecycle ownership, proactive monitoring and risk management. The aim is not simply to limit an agent’s capabilities; it is to make its authority understandable, reviewable and accountable.
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How to judge an agent proposal
Start with a bounded workflow and a clear owner, then judge the proposed agent by what it is allowed to do—not by the label attached to the product. An agent with read-only access and a human approval step is materially different from one authorized to update records or initiate transactions. Assess how it handles exceptions and how the organization will observe, maintain and govern it as the workflow changes. The cited guidance supports these evaluation questions, but does not establish comparative performance, implementation costs or return on investment for specific use cases.
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