About AI agents and agentic workflows
AI agents and agentic workflows pursue a goal across multiple steps, planning and acting with limited human intervention to resolve an issue or complete a request. Where a generative AI skill completes one task on demand, an agentic workflow coordinates one or more agents to carry a process from start to finish.
An AI agent works toward a goal rather than performing a single action. Given an objective, an agent can reason about the steps required, use tools to gather information and act on your systems, and adjust its approach as conditions change. An agentic workflow brings one or more agents together to complete an entire process, so work that once required a person to coordinate each step can run with limited human intervention.
Why AI agents and agentic workflows matter
Agents extend generative AI from assisting with tasks to completing outcomes. Instead of returning a draft or a summary for a person to act on, an agentic workflow can interpret a request, retrieve what it needs from your knowledge bases and enterprise systems, perform the required actions, and report the result. This work shifts routine, multi-step effort away from your teams and lets them focus on the cases that need human judgment.
Because agents adapt to changing conditions, they suit processes that vary from one case to the next. An agent can respond to what it finds at each step rather than following a fixed script, which helps it stay focused on the objective even when the details differ.
AI agents compared with generative AI skills
Generative AI skills and AI agents address different kinds of work. A skill completes one bounded task when a user asks for it, then returns control to that user. An AI agent pursues a broader goal across several steps and can decide what to do next, which makes it suited to work that no single task can finish on its own.
The two are complementary rather than competing. Skills provide focused capabilities, and agents coordinate capabilities and tools to move a process toward an outcome. Many agentic workflows draw on the same enterprise data and generative AI foundation that skills use, applied to a larger, multi-step goal.
When to use AI agents and agentic workflows
Choose an AI agent and an agentic workflow when an outcome depends on several coordinated steps, when decisions vary with conditions that you can't fully predict, or when the work spans multiple systems and would otherwise require a person to drive each step. Agentic workflows suit processes that you want to complete with limited human intervention while keeping oversight of the actions taken.
When the work is a single task that a person starts and reviews in the moment, such as summarizing or drafting, a generative AI skill is the more direct choice.
--- title: About AI agents and agentic workflows description: AI agents and agentic workflows pursue a goal across multiple steps, planning and acting with limited human intervention to resolve an issue or complete a request. Where a generative AI skill completes one task on demand, an agentic workflow coordinates one or more agents to carry a process from start to finish. locale: en-US canonical_url: https://www.servicenow.com/docs/r/australia/intelligent-experiences/platai-understanding-ai-agents-agentic-workflows.html release: australia topic_type: concept last_updated: "2026-09-10" reading_time_minutes: 2 keywords: [AI agents, agentic workflows, agentic AI, AI agents versus generative AI skills, autonomous workflows, when to use AI agents] breadcrumb: [AI agents and agentic workflows, Enable AI Experiences] --- # About AI agents and agentic workflows AI agents and agentic workflows pursue a goal across multiple steps, planning and acting with limited human intervention to resolve an issue or complete a request. Where a generative AI skill completes one task on demand, an agentic workflow coordinates one or more agents to carry a process from start to finish. An AI agent works toward a goal rather than performing a single action. Given an objective, an agent can reason about the steps required, use tools to gather information and act on your systems, and adjust its approach as conditions change. An agentic workflow brings one or more agents together to complete an entire process, so work that once required a person to coordinate each step can run with limited human intervention. ## Why AI agents and agentic workflows matter Agents extend generative AI from assisting with tasks to completing outcomes. Instead of returning a draft or a summary for a person to act on, an agentic workflow can interpret a request, retrieve what it needs from your knowledge bases and enterprise systems, perform the required actions, and report the result. This work shifts routine, multi-step effort away from your teams and lets them focus on the cases that need human judgment. Because agents adapt to changing conditions, they suit processes that vary from one case to the next. An agent can respond to what it finds at each step rather than following a fixed script, which helps it stay focused on the objective even when the details differ. ## AI agents compared with generative AI skills Generative AI skills and AI agents address different kinds of work. A skill completes one bounded task when a user asks for it, then returns control to that user. An AI agent pursues a broader goal across several steps and can decide what to do next, which makes it suited to work that no single task can finish on its own. The two are complementary rather than competing. Skills provide focused capabilities, and agents coordinate capabilities and tools to move a process toward an outcome. Many agentic workflows draw on the same enterprise data and generative AI foundation that skills use, applied to a larger, multi-step goal. ## When to use AI agents and agentic workflows Choose an AI agent and an agentic workflow when an outcome depends on several coordinated steps, when decisions vary with conditions that you can't fully predict, or when the work spans multiple systems and would otherwise require a person to drive each step. Agentic workflows suit processes that you want to complete with limited human intervention while keeping oversight of the actions taken. When the work is a single task that a person starts and reviews in the moment, such as summarizing or drafting, a generative AI skill is the more direct choice.
| title | About AI agents and agentic workflows |
| description | AI agents and agentic workflows pursue a goal across multiple steps, planning and acting with limited human intervention to resolve an issue or complete a request. Where a generative AI skill completes one task on demand, an agentic workflow coordinates one or more agents to carry a process from start to finish. |
| locale | en-US |
| canonical_url | https://www.servicenow.com/docs/r/australia/intelligent-experiences/platai-understanding-ai-agents-agentic-workflows.html |
| release | australia |
| topic_type | concept |
| last_updated | 2026-09-10 |
| reading_time_minutes | 2 |
| keywords | AI agentsagentic workflowsagentic AIAI agents versus generative AI skillsautonomous workflowswhen to use AI agents |
| breadcrumb | AI agents and agentic workflowsEnable AI Experiences |