INTRODUCTION
Artificial Intelligence (AI) is evolving rapidly, and two terms are increasingly coming up in discussions: "AI Agents" and "Agentic AI." While they sound similar, they represent very different approaches and capabilities. This article explains what each term means, how they differ, and what they have in common, complete with simple ASCII diagrams of their architectures.
DEFINITIONS
AI Agent:
An AI Agent is a software entity designed to perform specific tasks by following predefined workflows or scripts. These agents often use large language models (LLMs) for understanding and generating language, and they may cooperate with other agents to complete complex tasks. However, their autonomy is limited:
- They do not make independent decisions or plans.
- They operate within boundaries set by their fixed workflows.
Agentic AI:
Agentic AI refers to advanced, autonomous AI systems capable of setting their own goals, creating plans, making decisions, and adapting their behavior based on feedback from the environment.
- They are self-directed and can operate without constant human intervention.
- They can reason, strategize, and learn from outcomes to improve over time.
KEY DIFFERENCES
+-------------------+-------------------------------------------——-+—------------------------------------------------+
| Aspect | AI Agent | Agentic AI |
+-------------------+--------------------------------------------——+—------------------------------------------------+
| Autonomy | Low - follows fixed, predef. workflows | High – sets own goals, plans, decisions |
| Workflow | Scripted, rule-based, or flowchart-driven | Dynamic, self-adaptive, and open-ended |
| Cooperation | Works with other agents | May cooperate, can act independently |
| Use of LLMs | For specific tasks (e.g., parsing text) | Broader reasoning/planning |
| Decision-mak. | Limited to preprogrammed options | Can generate novel solutions,strategies |
| Planning | Task execution only | Can plan, re-plan, and adjust strategies |
| Learning | Usually static behavior | Can adapt and learn from feedback |
+-------------------+----------------------------------------------+---------------------------------------------———-+
KEY SIMILARITIES
- Both may use LLMs for language understanding and generation.
- Both can interact with users and other software systems.
- Both can be components of larger AI systems.
ARCHITECTURES
AI Agent Architecture:
+-------------------+
| User Input/API |
+-------------------+
|
v
+-------------------+
|Workflow Engine|
|(predefined steps)|
+-------------------+
|
v
+-------------------+
| LLM Module |
+-------------------+
|
v
+-------------------+
| Task Execution |
+-------------------+
|
v
+-------------------+
|Output/Response |
+-------------------+
Explanation:
- The agent receives input, follows a fixed workflow, uses an LLM for specific tasks, executes the required actions, and returns output.
Agentic AI Architecture:
+------------------------+
| Perception/Input |
+------------------------+
|
v
+------------------------+
| Goal Setting/Intent |
+------------------------+
|
v
+------------------------+
| Planning/Reasoning | <--- Feedback Loop
+------------------------+
|
v
+------------------------+
| Action Selection |
+------------------------+
|
v
+------------------------+
| LLM & Tool Use |
+------------------------+
|
v
+------------------------+
| Environment/Output|
+------------------------+
Explanation:
- The system perceives its environment, sets its own goals, plans actions, selects and executes actions (possibly using LLMs and other tools), and adapts based on feedback from the results.
SUMMARY TABLE
+------------------+--------------------------+----------------------+
| Feature | AI Agent | Agentic AI |
+------------------+--------------------------+------------------———-+
| Workflow | Fixed | Dynamic |
| Autonomy | Low | High |
| Planning | None or minimal | Central capability |
| Cooperation | Often in teams | Solo or in teams |
| Adaptation | Static | Learns and adapts |
| LLM Use | For specific tasks | Integrated into |
| | | reasoning |
+------------------+--------------------------+----------------------+
ILLUSTRATIVE EXAMPLES
AI Agent:
An email sorting agent that routes emails according to a set of predefined rules and uses an LLM to extract entities from the text.
Agentic AI:
An autonomous business assistant that identifies business opportunities, plans outreach strategies, writes and sends emails, follows up, and adapts strategies based on success rates—making its own decisions throughout.
CONCLUSION
While both AI Agents and Agentic AI leverage modern AI technologies, their core difference lies in autonomy and adaptability. AI Agents are powerful tools for structured, repetitive tasks, while Agentic AI represents the next step: systems that can independently set goals, plan, and learn. Understanding this distinction is key as AI continues to integrate further into our daily lives and work.
No comments:
Post a Comment