Cracking the code: How AI agents and Agentic AI are not the same

Artificial Intelligence (AI) has become a catch-all term, but within it lies a key distinction between AI agents and agentic AI. While related, these terms define different levels of autonomy, complexity, and scope. Understanding their differences is crucial for developers, executives, and anyone deploying intelligent systems.

What defines an AI Agent

An AI agent is a software system built to autonomously perform a defined task by perceiving its environment, processing diverse inputs, and taking purposeful action. These agents combine advanced reasoning, contextual understanding, and tool integrations to deliver outcomes with a high degree of reliability.

They are designed to operate within a well-scoped domain, guided by objectives, prompts, or user intent. Common examples include chat assistants responding to FAQs, scheduling tools coordinating calendars, or models classifying documents. While their focus is often narrow, AI agents apply intelligent decision-making within those boundaries, using learned patterns and contextual cues to produce consistent results.

Typically, an AI agent senses a stimulus, reasons over it using its knowledge or models, and then carries out the next best action. This cycle is optimised for clarity, accuracy, and predictability, following clearly defined, sequential decision pathways. It is streamlined for consistent, high-confidence outcomes within its scope, without maintaining broader cross-task memory beyond the immediate context.

Agentic AI

By contrast, agentic AI signifies systems that exhibit broader autonomy, multi-step reasoning, and dynamic goal pursuit. Beyond merely responding to prompts, these systems can:

• Receive or infer high-level objectives
• Decompose them into actionable subtasks
• Plan and strategise across multiple steps
• Coordinate multiple specialised sub-agents
• Maintain memory and adapt as they work
• Learn and iterate based on outcomes

In essence, agentic AI transitions from executing isolated tasks to orchestrating workflows across tools, models, and actions, much like a digital project manager. These systems plan ahead, reconsider decisions based on evolving conditions, and operate proactively rather than reacting to each prompt.

Autonomy and decision-making

A central differentiator lies in autonomy. Conventional AI agents typically perform learned or programmed actions within a defined task boundary. While they may adapt to known variations, they can still struggle with entirely unanticipated scenarios. In contrast, agentic AI adapts as it goes, shifting priorities, recalculating plans, and choosing new strategies mid-course.

This distinction allows agentic systems to manage complex workflows without human intervention, reshaping how tasks are distributed: routine steps become automated, while critical judgment remains with human overseers.

Architecture and complexity

From an architectural perspective, AI Agents tend to be simpler. They may integrate a language model, a few tools, and execute sequential reasoning. Their design emphasises predictability and clarity, making them easier to build, maintain, and govern.

Agentic AI, however, embodies a richer and more complex architecture. It includes planning modules, memory systems, orchestration layers, and coordination protocols among specialised sub-agents. This multi-agent composition allows for emergent behavior and cooperative reasoning across domains.

Learning, memory, and adaptation

While some AI Agents may incorporate basic learning such as refining responses based on usage, their learning is typically focused on improving performance within their defined domain. They deliver reliable outcomes by reinforcing best practices, with minimal drift or unintended changes.

Agentic AI systems, on the other hand, continuously learn and adapt across a broader set of activities. They maintain memory of past interactions, update strategies as conditions evolve, and optimise processes in real time. This ongoing, holistic adaptation makes them well suited for environments where objectives, inputs, or priorities can shift frequently.

Practical Implications and Decision Criteria

The distinctions between these systems have direct practical consequences:

●      Scope and use cases: AI agents excel at dependable, high-volume tasks — from triaging support requests to managing routine approvals — where consistent outcomes matter most. Agentic AI systems address more dynamic scenarios, coordinating complex workflows, adapting strategies, and achieving broader, goal-oriented results.

●      Cost and complexity: AI agents benefit from streamlined architecture, allowing faster deployment and easier governance. Agentic AI requires advanced planning capabilities, orchestration frameworks, and robust oversight to manage greater autonomy.

●      Governance and safety: With increased autonomy comes higher responsibility. Agentic systems introduce more complex decision paths, potential emergent behaviors, and a greater need for transparency, auditability, and safeguards.

Organisations may choose to start with proven AI agents to build confidence, then incrementally introduce agentic capabilities as business demands grow.

Evolutionary path

Organisations can take a phased approach to intelligent system deployment.

First, introduce AI agents to automate reliable, well-scoped tasks. These agents deliver consistent, high-quality outcomes while building familiarity and trust in AI-assisted workflows. By applying advanced reasoning within defined objectives, they serve as dependable partners in streamlining business processes.

Over time, organisations can expand these systems by incorporating planning capabilities, centralised memory, and orchestration layers, enabling more proactive coordination and dynamic strategy shifts. Strengthening governance, audit mechanisms, and human oversight supports responsible scaling as complexity increases.

This gradual evolution moves from specialised, outcome-focused AI agents to collaborative, goal-driven digital partners that manage sophisticated, cross-functional objectives.

To sum it up

In short, AI agents and agentic AI share foundational mechanics of perception, reasoning, and action, but diverge in autonomy, scope, and strategic intent. AI agents focus on well-defined, high-confidence tasks with precision and consistency, making them an ideal starting point for most organisations.

Agentic AI builds upon this, adding proactive planning, multi-agent orchestration, and continuous adaptation for complex, evolving needs.

Looking ahead, these advances lay the foundation for AI employees - intelligent, collaborative systems that work alongside humans as dependable digital teammates, handling entire roles rather than just tasks.

Understanding where your organisation is on this spectrum helps guide your roadmap.

Starting with specialised AI agents delivers quick wins and builds trust, while gradually moving toward agentic systems - and eventually AI employees - unlocks advanced, coordinated, and proactive digital collaboration. The key is balancing capabilities, governance, and risk to responsibly advance your automation strategy.

Vijay Navaluri, Co-Founder and Chief Customer Officer, Supervity