Table of contents
Highlights
- An AI agent handles a single, well-defined task. Agentic AI is the system that coordinates many of these agents — along with data sources and tools — to execute broader, multi-step workflows that span teams and systems.
- When this difference is unclear, organizations can end up expecting single-purpose agents to solve complex, cross-functional problems they weren’t designed for. That leads to disconnected automations, inconsistent logic, and gaps between systems.
- Agentic AI provides the orchestration layer enterprises need: it reasons about the user’s goal, plans the sequence of steps, chooses the right agents, and adapts as conditions change. This system-level intelligence helps reduce manual work, maintain consistency across workflows, and support operational scale.
- AI agents remain valuable for well-defined, predictable tasks, but on their own they are not designed to manage complex, cross-system workflows or continuously adapt to changing business conditions without an agentic unifying layer.
AI is evolving fast, and enterprise leaders are under pressure to make smart bets. That pressure is showing up in the data: McKinsey reports that 62% of organizations are already using AI agents.Another report by Gartner shows their rapid growth, expecting 40% of enterprise applications to include task-specific AI agents in 2026, up from less than five percent in 2025.But one source of confusion keeps slowing teams down — the difference between AI agents and agentic AI.
On the surface, the terms sound similar. But the reality is the difference between a single-task automation tool and a broader, goal-oriented system. The distinction matters now more than ever because “agent” has become the default label for everything, including copilots embedded in apps, agent marketplaces, and point solutions built to automate a single step in a vacuum. In practice, mixing them up can lead to fragmented automation, stalled ROI, and initiatives that do not scale.
By understanding the nuances of AI agents and agentic AI, leaders can understand which tools are best suited for their goals, and use more resilient architectures that can adapt to changing conditions and deliver measurable business outcomes.
What is an AI agent?
An AI agent is a software-based system that is able to perceive information, reason over that information, and take action to achieve a defined goal. In the enterprise, this usually means automating well-scoped tasks — like retrieving a record from a system, validating data, routing a request, or generating a response based on defined logic.
AI agents operate within explicit boundaries set by their design and permissions. AI agents can use rules, machine learning, or natural language processing to interpret inputs and make decisions, and they often interact with enterprise systems and tools to carry out actions. For example:
- An access-management agent might process software requests by checking eligibility, verifying permissions, and updating a ticket.
- A finance agent might extract data from invoices and compare it against cost-center policies.
While an individual AI agent is designed to perform a specific function, it can also serve as a building block within larger multi-agent or agentic AI systems.
On their own, AI agents excel at focused automation but are not designed to orchestrate complex, end-to-end workflows that span across multiple systems, require dynamic planning, or adapt to broader business context without additional coordination, governance, and reasoning layers.
How AI agents work
AI agents operate with autonomy inside defined boundaries. Each one is built to understand a specific input, determine the appropriate next step, and take action within the constraints of its design. In practice, this means an agent can make decisions — but only for the task or slice of work it is responsible for.
Some agents behave reactively, responding to user prompts or system events. Others incorporate limited planning, allowing them to follow simple multi-step sequences. But even the most capable agents remain scoped to their domain. On their own, they are not designed to coordinate complex workflows across systems, handle broader business objectives, or adapt their strategy without an automation layer.
This distinction is important: having multiple AI agents does not automatically result in agentic AI, which requires coordination, planning, and goal-directed reasoning across agents.
Without a unifying layer to maintain shared context, plan across systems, and trigger actions, each agent continues to execute independently, which often leads to fragmented workflows rather than cohesive automation.
Types of AI agents
AI agents come in several forms, each designed to handle a specific style of decision-making or interaction. These categories reflect the types most relevant to enterprise automation — and where leaders are most likely to encounter them in real workflows.
AI agent type | Enterprise examples |
Reactive (reflex) agents: These agents respond directly to incoming inputs or events using predefined rules. |
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Model-based agents: These agents maintain an internal representation of their environment, allowing them to make more informed decisions. |
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Utility-based agents: These agents weigh potential outcomes to choose the action that offers the highest “value” based on predefined criteria. |
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Learning agents: These agents improve over time by adjusting their behavior based on outcomes, feedback, or new data. |
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Across these categories, each agent is typically designed for a specific slice of work. Even when multiple agents are deployed together ( and even when they interact with several systems) — they still require cross-tool sequencing (i.e. orchestration) to operate cohesively across tools and goals.
At Moveworks, we classify three core types of AI agents, each purpose-built for a specific role and designed to work together as a cohesive, intelligent system within the enterprise, which include:
- Aggregation agents: These agents are designed to cut through the complexity of information overload by gathering, synthesizing, and presenting relevant data to the user or other agents.
- Action agents: These agents execute tasks on behalf of a user with precision. Their role is to translate a user's request into a reliable, automated action across integrated enterprise systems.
- Ambient agents: These agents operate quietly in the background, constantly monitoring the environment and spring into action as needed in response to signals from other systems or changes in the environment.
In this way, aggregation, action, and ambient agents are the building blocks that our Reasoning Engine uses to orchestrate solutions to complex enterprise problems, effectively creating the high-level agentic systems that go far beyond simple chatbots or reactive tools.
These specialized agent types work to achieve the larger goal of agentic systems by coordinating to solve complex, multi-step problems.
From AI agent to agentic AI
AI agents are powerful building blocks, but they operate independently unless something coordinates their work. That coordination is what shifts an environment from “a collection of agents” to agentic AI.
Agentic AI operates at the workflow and outcome level — not the task level.
Agentic AI introduces the ability to plan, reason, and route across multiple agents and systems. Instead of executing one task at a time, an agentic system understands the broader goal, determines the sequence of actions required, selects the right agents or tools for each step, and adapts as conditions change.
Consider a few enterprise examples:
- IT ticket management
Multiple agents classify the issue, look up device information, check prior incidents, and propose next steps — while an agentic layer determines the workflow, triggers actions across systems, and manages escalations.
- HR onboarding
Agents gather role requirements, provision access, notify stakeholders, and update systems; the agentic layer coordinates timing, dependencies, and exceptions to keep the end-to-end experience consistent.
- Security incident response
Agents detect anomalies, gather logs, validate access patterns, and recommend containment steps; the agentic layer assembles these actions into a coherent response and tracks it through resolution.
This is the key to turning a set of capable agents into a unified system that delivers outcomes, not just isolated task completions.
What is agentic AI?
Agentic AI refers to systems that can autonomously plan, reason, and coordinate actions across multiple agents, tools, and enterprise systems to achieve broader business goals. Unlike a single AI agent that focuses on one task at a time, agentic AI understands the overall objective, determines how to get there, and adapts its approach based on real-time context and outcomes.
Large language models (LLMs) are the foundational AI models that enable this intelligence. Trained on vast amounts of text and data, LLMs understand and generate human-like language, reason over complex information, and interpret user intent.
Within agentic AI systems, LLMs act as the “brain” that drives planning, decision-making, and coordination — translating high-level goals into actionable steps and dynamically adjusting behavior as new information becomes available.
At its core, agentic AI brings together four capabilities that most AI agents do not combine end to end:
Goal-oriented reasoning: It interprets the end goal — not just the next step — and selects the right sequence of actions needed to reach it.
Multi-step planning: It can break complex workflows into sub-tasks and coordinate the necessary agents, data, and systems to complete them.
Dynamic adaptation: It adjusts plans based on new information, exceptions, or changing conditions, improving performance over time.
Cross-system orchestration: It executes work across applications, APIs, and enterprise platforms while maintaining context, continuity, and governance.
In practice, agentic AI allows organizations to automate entire processes end-to-end rather than stitching together isolated task-level automations. It’s the difference between completing a single step and reliably delivering the full outcome.
What’s the difference between agentic AI and AI agents?
Although the terms sound similar, AI agents and agentic AI operate at different layers of an automation strategy.
AI agents are task-focused components that carry out specific actions, while agentic AI is the higher-level capability that plans, reasons, and orchestrates multiple agents across systems to achieve broader outcomes.
The table below illustrates the contrast between the two.
Category | AI Agents | Agentic AI |
Definition | Multiple software entities, each designed to perform a specific, goal-oriented task within defined boundaries. | A system that plans, reasons, and coordinates actions across multiple agents, tools, and systems to achieve broader goals. |
Scope of action | Narrow — focused on one task or domain. | Broad — spans workflows, systems, and teams. |
Decision-making | Bounded autonomy within predefined rules, data, and inputs. | Strategic autonomy — understands goals and makes multi-step decisions within enterprise policies and guardrails. |
Planning ability | Limited — may follow simple, predefined sequences for a specific task or domain. | Robust — can break goals into sub-tasks and assemble the right sequence of actions across agents and systems. |
Adaptation | Can improve with data but typically responds to local context only. | Dynamically adjusts plans as conditions, data, or system states change, maintaining progress toward the goal. |
Cross-system coordination | Can interact with multiple systems but does not coordinate them end-to-end without an orchestration layer. | Orchestrates actions across agents, systems, and data sources with shared context and centralized oversight. |
Ideal use cases | Repetitive, well-defined tasks contained within one system or functional boundary. | Complex workflows requiring reasoning, cross-system execution, and consistent outcomes. |
Primary limitation | Operates independently. Can create fragmentation if deployed without orchestration. | Requires high-quality integrations, governance, and clearly defined enterprise goals to perform reliably. |
Five core distinctions between AI agents and agentic AI
AI agents and agentic AI both contribute to enterprise automation, but they operate at different layers. Below are the distinctions that matter most when understanding how these systems behave inside real workflows and their potential capabilities.
1. Autonomy and decision-making
- Agents: Makes decisions within a fixed task boundary.
- Agentic AI: Chooses among paths, sequences actions, and adjusts within policies and guardrails.
- Enterprise example:
An AI agent can classify an IT issue. Agentic AI can determine the resolution path, dispatch steps across systems, and manage escalations.
2. Complexity and learning
- AI agents: Improves with data and feedback but typically adapts at the task level, refining how they perform a specific action.
- Agentic AI: Adapts at the workflow level, adjusting plans as new information emerges, exceptions occur, or constraints change.
- Enterprise example:
An agent may refine how it populates HR fields. Agentic AI updates the entire onboarding workflow when role requirements or approval paths change.
3. Functional scope
- AI agents: Specializes in narrow, well-defined tasks that live within a single domain or system.
- Agentic AI: Manages dependencies, permissions, and recovery when workflows hit exceptions or failures.
- Enterprise example:
An agent can fetch a knowledge article, whereas agentic AI can gather information across systems, verify access, synthesize an answer, and trigger follow-up actions.
4. Proactiveness
- AI agents: Reacts to events or user inputs, even if they incorporate some limited predictive logic.
- Agentic AI: Identifies emerging bottlenecks and adjusts workflows before they fail — within defined policies.
- Enterprise example:
An agent resets a password when asked; agentic AI detects recurring access issues, identifies root causes, and suggests or initiates remediation workflows.
5. Planning and process execution
- AI agents: May follow simple sequences but do not own or coordinate end-to-end workflows on their own.
- Agentic AI: Manages end-to-end completion, monitoring progress, handling exceptions, and keeping work moving through error handling and escalation. performs true workflow routing, breaking goals into sub-tasks, coordinating specialized agents, sequencing actions across systems, and ensuring the full process completes successfully with monitoring and error handling.
- Enterprise example: An agent updates a field in a ticketing system; agentic AI manages the entire ticket lifecycle, from intake through routing, resolution, escalation, and communication.
In short: AI agents complete tasks. Agentic AI runs the workflow towards an outcome.
How to choose between AI agents and agentic AI
Most enterprises don’t need to choose between AI agents and agentic AI — they need to understand where each one fits. AI agents excel at task-level automation, while agentic AI is built for workflows that require planning, reasoning, and coordinated action across systems.
Use the framework below to identify which approach aligns with the workflow you’re evaluating.
Ideal conditions | Example tasks | |
AI agents |
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Agentic AI |
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Key use cases for agentic AI and AI agents
AI agents and agentic AI both play important roles in enterprise automation, but which is best will depend on the complexity and scope of the task at hand.
AI agents are designed for targeted, repeatable functions that operate within a narrow set of rules. They excel at:
- High-speed, consistent tasks such as retrieving records
- Tagging IT tickets
- Validating inputs
- Extracting structured data from documents
Because they focus on specific actions — like auto-filling fields or triggering simple notifications — they are ideal for improving efficiency at the individual task level rather than managing a broader process.
In contrast, agentic AI is built for sophisticated workflows that require reasoning, adaptation, and multi-step planning. This approach is most effective when automation must:
- Span multiple systems, such as coordinating end-to-end employee onboarding across HR and IT
- Managing complex security incidents that require real-time decision-making
By synthesizing context from various sources and handling unexpected exceptions, agentic AI enables continuity and governance across entire business ecosystems, making it the superior choice for orchestrating dynamic, cross-functional operations.
The future of agentic AI and AI agents for automation
The future of enterprise automation isn’t about replacing AI agents with agentic AI — it’s about combining them. AI agents will continue handling precise, task-level work, while agentic systems coordinate those tasks into reliable, end-to-end outcomes.
Several trends are shaping how enterprises will use these technologies in the years ahead:
1. AI moves from task automation to outcome automation
Enterprises are shifting from automating individual steps to automating entire workflows. Agentic AI can support this transition by coordinating many specialized agents to deliver outcomes rather than isolated actions.
2. Non-technical teams can increasingly build automations
Our research shows 91% of IT executives believe that non-technical employees are driving agentic AI initiatives. As agentic platforms evolve, teams outside engineering, HR, finance, operations , can assemble automations without writing code. This shift could broaden access to AI, allowing domain experts to shape workflows while IT maintains governance and integration standards.
3. Governance and risk management become central
As automation grows more autonomous, enterprises will prioritize auditability, permissions, policy enforcement, and explainability. Capgemini notes that by 2026, nearly half of all enterprise AI governance frameworks will include real-time edge monitoring and adaptive compliance. Agentic AI systems that log decisions, actions, and data access may become essential for deploying automation safely and at scale.
4. Integrations become the foundation of effective automation
Enterprises rely on many distributed systems. While individual agents may touch multiple tools, reliably automating outcomes will increasingly depend on seamless, policy-aware integrations — a core function of end-to-end automation.
5. Adaptive workflows become the norm
Static workflows age quickly in dynamic environments. Agentic AI can adjust plans in real time based on new inputs, exceptions, or system changes — a valuable capability as organizations navigate shifting requirements, distributed teams, and continuous operational updates.
6. Employees, not just IT, drive AI adoption
Our report shows that 78% of executives believe their most successful projects started with support staff solving persistent challenges. As agentic workflows become easier to build and apply, bottom-up innovation may play a larger role in shaping automation strategies.
Taken together, these trends point toward an automation future defined not by a single technology, but by the interplay between the two: AI agents handling precise tasks, and agentic AI assembling those tasks into reliable, governed workflows across systems.
Quickly build custom agentic workflows
Agentic AI delivers outsized value only when enterprises have a way to coordinate many agents, systems, and data sources — and that’s where Moveworks provides a distinct advantage. Without a way to coordinate agents and systems reliably, enterprises end up with scattered automations that are difficult to govern, scale, and measure.
Moveworks offers a unified agentic AI platform that helps organizations build, deploy, and manage agentic workflows without stitching together dozens of point automations.
Moveworks connects directly to your existing systems and tools, providing the orchestration layer required for goal-driven automation. The platform is designed to help enterprises:
- Coordinate multiple agents and tools to complete multi-step workflows end to end
- Integrate across enterprise systems through a wide range of secure connectors and APIs
- Apply role-based permissions, policies, and governance so automations are able to operate safely and with compliance
- Adapt workflows dynamically based on real-time context, system state, or new information
- Build custom automations quickly using natural language and low-code configuration via Agent Studio and the AI Agent Marketplace
Instead of managing isolated task automations, enterprises can use Moveworks to bring everything together — creating agentic workflows that help reduce manual effort, improve operational efficiency, and support employees across the business.
Ready to see how agentic AI can power your enterprise workflows?
Explore how to build AI agents and connect agentic automation with Moveworks:
👉 Moveworks AI Agent Studio
Frequently Asked Questions
Agentic AI systems can log every action, decision path, and system interaction, making workflows easier to audit and review. With a properly configured platform, enterprises can use agentic AI to enforce policy-based guardrails, role-based permissions, and human-in-the-loop checkpoints while maintaining visibility into how decisions were made — helping enable automations to operate within established governance and compliance frameworks.
Without a coordinating layer across tools and systems, organizations can end up with fragmented automations that operate in isolation, follow inconsistent logic, or leave gaps between processes. These gaps can introduce operational risk — including missed handoffs, duplicated steps, or mismatched permissions that create potential security or compliance blind spots.
A unified orchestration layer helps align automations to shared rules, data permissions, and governance policies. It allows agents or workflows to sequence actions safely across systems, apply enterprise standards consistently, and deliver outcomes that serve end-to-end business objectives rather than isolated tasks.
Yes. Agentic AI is built to work alongside existing automation tools and AI agents — not replace them. It sits above the current tech stack as an orchestration layer, coordinating systems, workflows, and tools already in use. This approach lets organizations preserve their existing investments while improving how those components operate together as cohesive, end-to-end workflows.
Teams typically need foundational knowledge of workflow design, system integrations or APIs, and governance or risk frameworks. Modern agentic platforms abstract much of the underlying complexity, enabling IT to focus on defining goals, policies, and guardrails — while technical and non-technical users can configure or extend workflows using low-code or natural-language tools.
If the task is simple, repetitive, and contained within one system or a tightly scoped domain, an AI agent is usually sufficient. If the workflow spans multiple systems, requires planning or reasoning, or depends on shared policies, context, and coordination, an agentic AI approach will likely deliver better, more reliable results.
