Agentic AI vs AI agents: What is the difference?
Agentic AI and AI agents are not the same thing. AI agents are tools that perform specific tasks. Agentic AI describes a broader operating model for how work gets done across an end-to-end process.
That difference matters, especially in business operations.
An AI agent is an autonomous entity designed to perform specific tasks within defined parameters. While Agentic AI is the broader system that coordinates multiple agents, tools, and human inputs toward complex, multi-step goals. It plans, adapts, and orchestrates.
This guide breaks down what each approach does, where they excel, and how platforms like Moxo make agentic orchestration actually work in business operations.
Agentic AI vs AI agents at a glance
Moxo's process orchestration platform is built for the agentic AI column, coordinating AI agents, human actions, and connected systems within structured workflows where accountability stays clear.
5 key distinctions between AI agents and agentic AI
Understanding these five differences helps operations leaders make smarter investment decisions about where each technology fits.
Autonomy and decision-making. AI agents operate within predefined frameworks, adapting based on learned patterns. Agentic AI steps it up with goal-driven decision-making, proactively identifying objectives, evaluating multiple options, and learning from experience to refine performance. Moxo's workflow orchestration leverages this distinction by letting AI agents handle bounded tasks while the broader system coordinates toward business outcomes.
Complexity and learning. AI agents improve through programming updates and pattern recognition within their scope. Agentic AI learns from individual interactions, adjusts approaches based on results, and generates new solutions to emerging challenges. This adaptability matters for dynamic environments where conditions change and exceptions are common.
Functionalities. AI agents are designed for specific tasks within defined parameters. Agentic AI operates on a broader scale, combining multiple capabilities to handle complex, multi-step processes requiring coordination across systems and domains. This is exactly where Moxo's multi-party workflows create value, spanning the boundaries that single agents cannot cross.
Proactiveness. Individual AI agents tend to be reactive, responding to specific triggers. Agentic AI is proactive, combining multiple agents to adapt, create solutions, and take action without explicit prompts. For instance, it could spot patterns in process breakdowns and suggest fixes before problems escalate.
Planning. AI agents handle tasks within their designated scope. Agentic AI coordinates multiple systems simultaneously, keeping actions aligned with organizational goals, managing complex workflows, and improving processes over time. Research from Gartner confirms this trajectory: by 2028, 15% of day-to-day work decisions will be made autonomously through agentic AI.
What is agentic AI?
Agentic AI is the orchestration layer that coordinates multiple AI agents, systems, and human actions toward strategic outcomes.
Think of it as the difference between having a great sous chef and running an entire kitchen. An AI agent expertly handles one station. Agentic AI runs the whole service, reading conditions, adjusting timing, routing work, and making sure nothing stalls while someone's still waiting on a decision.
In enterprise terms, agentic AI breaks down high-level goals into actionable steps, assigns work to the right agents or humans, monitors progress, and adapts when conditions change.
By 2028, Gartner predicts 33% of enterprise software will include agentic AI, up from less than 1% in 2024, enabling 15% of day-to-day work decisions to be made autonomously.
Agentic AI treats AI agents as building blocks, not endpoints. It orchestrates them within workflows designed to achieve business outcomes. Moxo embeds this model directly into multi-party workflows, where AI handles coordination while humans retain accountability for every critical decision.
What are AI agents?
AI agents are autonomous entities that perform specific tasks within defined environments, capable but bounded.
They're the workhorses of modern automation. An AI agent might review documents for completeness, route support tickets, extract invoice data, or generate first-draft responses. They operate with autonomy within their scope, but that scope has clear edges.
For operations teams, AI agents solve specific coordination problems. They handle repetitive, rule-based work that currently consumes hours of human attention: validation, routing, notifications, data entry.
Moxo's AI agents operate inside workflows to handle this preparation and coordination work, so humans focus only where judgment matters.
The limitation is scope, not capability. AI agents excel at bounded tasks. They struggle when work requires coordinating across multiple systems, adapting to unexpected inputs, or pursuing goals that span departments and stakeholders.
How AI agents compare to AI assistants
This comparison anchors the spectrum of autonomy.
AI assistants are reactive. They wait for prompts, respond to questions, and execute on explicit requests. Think chatbots and copilot-style tools. Helpful, but they don't take initiative.
AI agents are proactive within boundaries. They monitor conditions, trigger actions based on rules, and execute without constant prompting. Autonomous, but within a defined task scope.
Agentic AI is proactive and adaptive across boundaries. It pursues goals, not just tasks. It orchestrates agents and systems, coordinates human involvement, and adjusts strategy based on changing conditions.
The practical implication: AI assistants are tools. AI agents are automation. Agentic AI is orchestration.
Moxo provides the orchestration layer that makes agentic workflows actually function across teams and external stakeholders.
When to use AI agents vs agentic AI
AI agents make sense when the work is bounded and predictable. Clear inputs, defined rules, consistent outputs. Document validation, data extraction, notification triggers, and routine routing all fit this profile. When compliance guardrails are tight, bounded agents are easier to govern and audit.
Agentic AI makes sense when work spans multiple teams, systems, and stakeholders. When outcomes matter more than tasks. When conditions change and exceptions are common.
The most effective operational model isn't "replace humans" or "keep everything manual." It's AI handling coordination and preparation so humans can focus on the judgment calls that actually require expertise.
"Moxo has helped us completely streamline our project management and client communication process. Before using it, our team juggled multiple tools, emails, and chats to keep projects moving, which often led to missed details or delays." —G2 Reviewer.
Risks and governance considerations
AI agents are easier to govern. Bounded scope means clearer accountability and simpler audit trails. When an agent makes a mistake, you know exactly where to look.
Agentic AI requires intentional governance. Greater autonomy means more potential for unexpected outcomes. Operations leaders need clear operational constraints, monitoring that surfaces what's actually happening, audit trails showing who decided what and when, and escalation points ensuring humans stay accountable.
A process without clear accountability isn't a process. It's a shared assumption. Moxo addresses this by keeping humans accountable at every critical step while AI handles the coordination overhead. Every action is tracked and logged, ensuring the auditability that enterprise operations require.
Why Moxo for agentic workflows
The real challenge with agentic AI isn't the AI. It's the orchestration.
Most organizations attempting agentic workflows discover the same problem: agents complete their tasks, but handoffs break down. Decisions get made, but nobody knows what happened next. Work moves forward in some places and stalls in others.
Moxo is a process orchestration platform built for exactly this complexity. It structures multi-party workflows where AI agents, human actions, and connected systems work together with clear accountability at every step.
Here's what that looks like: An AI agent validates incoming documents and flags issues before they reach a human reviewer. Another agent prepares context, pulling relevant history and pre-filling forms.
The workflow routes work to the right person at the right moment, sends intelligent nudges when action is needed, and tracks progress across every step. Humans make the calls that require judgment. AI handles the coordination overhead.
AI handles the work around the work. Humans handle the decisions that matter. That's not a compromise. That's the model.
What’s better?
Agentic AI and AI agents aren't competing approaches. They're different points on the automation continuum.
AI agents reduce manual effort for specific, bounded tasks. Agentic AI orchestrates multiple agents, systems, and human touchpoints toward complex outcomes. For processes spanning teams and external stakeholders, orchestration is what makes the difference.
The question isn't which technology is "better." It’s what matches your operational reality. If you're automating tasks, start with agents. If you're orchestrating outcomes, you need the coordination layer that makes agentic systems work.
Either way, the goal is the same: humans focusing on decisions that matter, while AI handles the coordination overhead that used to slow everything down.
Get started with Moxo to see how process orchestration brings agentic workflows to life.
FAQs
What's the simplest way to explain the difference between agentic AI and AI agents?
AI agents handle specific tasks autonomously within defined boundaries. Agentic AI orchestrates multiple agents, systems, and human actions toward complex, multi-step goals. The agent executes a task; the agentic system pursues an outcome.
Can we use AI agents without building a full agentic system?
Absolutely. AI agents deliver value independently for bounded, predictable tasks. Agentic AI becomes valuable when work spans multiple teams, systems, and decision points that require coordination.
What does "human-in-the-loop" mean for agentic AI?
Humans remain accountable for critical decisions while AI handles preparation and coordination. AI agents might complete twenty steps automatically, but humans make the judgment calls that matter. Moxo's platform is designed around this principle.
How do we govern agentic AI when it's making autonomous decisions?
Build accountability into the design: clear operational constraints, outcome monitoring, audit trails showing decision paths, and explicit escalation points. The key is ensuring autonomy operates within defined boundaries with clear human ownership.
What's the first step to evaluate whether we need agents or agentic AI?
Map your most painful process end-to-end. If the problem is "this one task takes too long," start with an agent. If the problem is "we can't see where things are and work keeps falling through cracks across teams," you need orchestration.
