Agentic vs Non-Agentic Systems: Everything You Need to Know

Artificial intelligence systems are broadly categorized into agentic and non-agentic types. 

Agentic systems possess autonomy, make independent decisions & perform complex tasks without human intervention. Think autonomous driverless cars.

In contrast, non-agentic systems operate within predefined parameters, & require explicit instructions for each task. Think cars with drivers with maps. 

In this article we are going to dig deep & look at the differences in architecture, workflows, UI, scalability between agentic & non-agentic workflows while providing industry & functional specific examples across banking, healthcare, transportation, customer support and sales.

Lots to cover. So let’s dive in. 

But First, Why Care About Agentic Systems?

Well because all the data points towards all workflows being agent led in the next decade.

The AI agent market was valued at ~$3.86 billion in 2023 & is projected to grow at a 45.1% CAGR from 2024 to 2030. (Refer:  InfoWorld)

The global autonomous vehicle market is expected to grow from $1,921.1 billion in 2023 to $13,632.4 billion by 2030, exhibiting a CAGR of 32.3% during the forecast period.

And According to PwC, Generative AI, a subset of agentic AI, is poised to contribute between $2.6 trillion and $4.4 trillion annually to global GDP by 2030 across various sectors.

And finally by 2025, it is projected that 70% of organizations will have operationalized AI architectures with agentic workflows playing a crucial role in this expansion.

Ok, since the data substantiates it, lets jump into the overall differences between agentic & non-agentic workflows

1. Overall Differences

Aspect	Agentic Systems	Non-Agentic Systems
Autonomy	Operate independently, making decisions without human intervention. Example: An AI-powered virtual assistant that schedules meetings, sets reminders, and manages emails without human intervention	Require explicit human input for each task; lack decision-making capabilities. Example: A basic calendar app where users manually input events and reminders.
Adaptability	Learn from experiences and adapt to new information and environments.	Operate within predefined rules; limited ability to adapt to changes.
Complex Tasks	Capable of executing complex, multi-step tasks autonomously. Example: An autonomous drone that plans its flight path, avoids obstacles, and captures images based on real-time analysis.	Handle specific, singular tasks as directed by users. Example: A remote-controlled drone that requires a human operator to navigate and capture images.
Decision Making	Possess the ability to set goals, plan strategies, and execute actions.	Follow predetermined scripts without independent decision-making.

Technical Architecture Differences

1. Agentic Systems Architecture (Complex)

1. Perception Module: Gathers data from the environment (Text, Camera, Audio, Sensors)
2. Reasoning & Cognition Engine: Processes the perceived data to make informed decisions. Ex: Memory, Knowledge Base
3. Action Module: Executes decisions by performing specific actions
4. Learning Component: Enables the system to learn from experiences and adapt over time. Ex: Human Re-enforced Learning
5. Communication Interface: Allows interaction with other systems or agents. (Voice/Text)

Example of an Agentic System: An AI-powered personal assistant that autonomously manages your schedule, emails, and reminders. It adapts to your preferences, learns from past interactions, and proactively suggests optimizations.

2. Non-Agentic Systems Architecture 

Non-agentic systems operate based on predefined rules and require human input for decision-making. Their architecture generally comprises:

• Input Interface: Receives commands or data from users.
• Processing Unit: Executes predefined algorithms to process inputs.
• Output Interface: Delivers results based on processed data.

Example of a Non-Agentic System: A traditional chatbot that provides predefined responses to specific queries. It requires explicit user input for each interaction and cannot perform tasks beyond its programmed capabilities.

What are some of the other architectural differences?

Aspect	Agentic Systems	Non-Agentic Systems
Architecture Type	Modular and flexible, allowing for dynamic integration and customization.	Monolithic and rigid, making integration and customization challenging.
Workflow Logic	Implemented outside the core business logic, enabling interchangeable and adaptable workflows.	Embedded within the core business logic, leading to inflexible workflows.
Integration Capability	Seamlessly incorporate new functionalities without disrupting existing systems.	Adding new features often requires complex workarounds and can disrupt current operations.
Modularity	Agentic System: A modular CRM platform that allows seamless integration of new features like AI-driven analytics without overhauling the entire system.	Non-Agentic System: A monolithic legacy CRM where adding new features requires significant restructuring.

Understand the Workflow Differences

How Systems Have Evolved to Do More for Users?

Non-agentic AI systems require users to provide direct instructions for every task, acting as responsive tools. In contrast, agentic AI systems take the initiative by understanding context, making decisions, and acting independently when needed.

This shift is redefining how we interact with technology, making it more proactive and collaborative.

Aspect	Agentic Systems	Non-Agentic Systems
Interface Type	Transition from traditional GUIs to human-AI interfaces capable of understanding natural language.	Depend on static GUIs requiring manual navigation and input.
User Interaction	Enable intuitive interactions, reducing the learning curve and improving accessibility.	Can become cumbersome, especially when dealing with complex tasks or multiple systems.
Customization	Generate contextual interfaces on-the-fly, tailored to user needs.	Limited customization options, often leading to user frustration.
Interaction	Example Agentic System: A voice-activated smart home system that understands natural language commands to control devices.	Non-Agentic System: A traditional remote control requiring button presses for each function.

Connecting the Dots: Integration and Data Flow

The way systems integrate and share data has evolved, shaping how efficiently they work together. Early systems relied on siloed operations and manual data transfers, while modern approaches enable easier data flow and integration across platforms, driving smarter and more connected outcomes.

Aspect	Agentic Systems	Non-Agentic Systems
Data Integration	Facilitate seamless cross-application and data integration by maintaining neutrality. Agentic System: An AI platform that consolidates data from various sources, providing real-time insights and recommendations.	Often operate in silos, making data integration across different applications challenging. Non-Agentic System: Separate databases requiring manual data extraction and analysis.
Ecosystem Collaboration	Allow users to work across ecosystems holistically, enhancing efficiency and collaboration.	Separation leads to inefficiencies and data inconsistencies.
Scalability	Easily scalable due to modular architecture and dynamic data flow.	Scalability is limited by rigid structures and isolated data systems.

Examples of Agentic & Non-Agentic Workflows Across Industries

The evolution of workflows is increasingly characterized by the integration of agentic systems—autonomous agents capable of performing tasks with minimal human intervention. 

As we have covered the differences, lets look at how it trickles into industries.

Here is a detailed comparison of non-agentic versus agentic workflows across multiple industries:

1. Healthcare → Patient Monitoring

Non-Agentic Workflow: Patients in a hospital are connected to static monitoring devices. Nurses manually record vitals (e.g., heart rate, blood pressure) every few hours. Critical anomalies between recordings may go unnoticed. Example: A patient’s blood pressure spikes at 2 AM, but it isn’t detected until the nurse’s 6 AM round.

Agentic Workflow: AI agents continuously monitor patient vitals in real-time, analyzing data for anomalies. When a critical issue arises, the system alerts the medical team and suggests immediate interventions. Example: The AI detects a patient’s rising blood pressure at 2 AM, alerts the nurse on duty, and recommends administering medication.

2. Transportation: Logistics Optimization 

Non-Agentic Workflow: Delivery schedules are created using static maps and historical traffic data. If unexpected events like road closures occur, drivers must manually adjust their routes. Example: A delivery driver encounters heavy traffic and a closed road, delaying the shipment because the system lacks real-time adaptability.

Agentic Workflow: AI agents dynamically plan and adjust routes in real-time, considering current traffic, weather, and road conditions. They also optimize delivery schedules based on package priority and location. Example: The AI reroutes the delivery truck before the driver encounters traffic, ensuring timely shipment while optimizing fuel efficiency.

3. Sales and Marketing: Lead Generation

Non-Agentic Workflow: Lead Generation: Sales teams manually qualify leads by reviewing lists from static CRM systems. This process is time-consuming and prone to human error. Example: A salesperson calls 50 potential leads from a downloaded list, only to find that most are unqualified.

Agentic Workflow: AI agents analyze data to autonomously qualify and prioritize leads based on engagement, behavior, and predefined criteria. Agents also initiate personalized outreach. Example: The AI filters a list of 50 leads, prioritizing 10 with the highest likelihood of conversion, and sends tailored introductory emails to each.

4. Customer Support: Issue Resolution

Agentic Workflow: AI agents manage complex customer issues by understanding context, processing refunds, and updating account details autonomously. Escalation is only required for exceptions. Example: The AI processes the customer’s refund request, updates their account, and sends a confirmation email within minutes, requiring no human intervention.

5. Banking: Fraud Detection

Non-Agentic Workflow: Transactions exceeding a predefined threshold trigger a generic fraud alert. Customers must verify the transaction manually by contacting the bank. Example: A customer’s credit card purchase is flagged as fraudulent, requiring them to call the bank and verify the transaction.

Agentic Workflow: AI agents monitor transactions in real-time, analyzing spending patterns and context to determine fraud probability. They proactively notify customers and allow verification via voice or app. Example: The AI detects an unusual transaction, sends a notification to the customer, and verifies the purchase through a quick app interaction.

What are some of the most commonly asked questions?

1. How do agentic systems handle complex tasks compared to non-agentic systems?

Agentic systems are designed to autonomously manage complex, multi-step tasks by perceiving their environment, making decisions, and adapting to new information without human intervention. They can set goals, plan strategies, and execute actions independently. In contrast, non-agentic systems operate based on predefined rules and require human input for each step, limiting their ability to handle intricate tasks.

2. What are the implications of agentic systems on data privacy and security?

Agentic systems, due to their autonomous nature, often require access to vast amounts of data to function effectively. This raises concerns about data privacy and security, as these systems must handle sensitive information responsibly. Ensuring robust data encryption, compliance with privacy regulations, and implementing strict access controls are essential to mitigate potential risks. An agentic personal assistant that manages your emails, calendar, and financial transactions needs access to sensitive personal information, necessitating stringent security measures to protect your data.

3. What are the cost implications of implementing agentic systems compared to non-agentic systems?

Implementing agentic systems can involve higher initial costs due to their complexity and the advanced technology required. However, they can lead to long-term savings by automating tasks, improving efficiency, and reducing the need for human intervention. Non-agentic systems may have lower upfront costs but could incur higher operational expenses over time due to their limited automation capabilities.

4. How do agentic systems integrate with existing technology infrastructures compared to non-agentic systems?

Agentic systems are designed with flexibility in mind, allowing seamless integration into existing technology infrastructures. They can adapt to various platforms and workflows, often requiring minimal adjustments for compatibility.

For example, Lyzr’s agentic framework integrates with 200+ LLMs, tooling across all functions (Ex: Gmail,Calendar, Hubspot) and accepts all forms of input (video, audio and text) as RAG.  Non-agentic systems, however, may necessitate significant modifications to existing infrastructures to accommodate their rigid, predefined processes.

5. What are the ethical considerations when deploying agentic systems versus non-agentic systems?

Deploying agentic systems raises ethical considerations, including decision-making transparency, accountability, and potential biases in autonomous actions. Ensuring these systems operate within ethical guidelines and societal norms is crucial. Non-agentic systems, being more controlled and predictable, present fewer ethical dilemmas but still require oversight to prevent misuse. For example, an autonomous hiring platform that screens candidates without human intervention can lead to pre-trained biases while a non-Agentic System like a keyword-based resume scanner that highlights specific terms for human review can also lead to the right candidates not being selected. 

Experience agentic AI that’s built to lead

Well since agentic workflows are the future, if youre looking to explore agents & are looking to automate workflows, come speak to us at Lyzr.

We are happy to walk you through what is possible. And similarly if you’re looking to build on your own, do explore our pre-built agents on Lyzr Studio. We have quite a few for banking, sales, marketing, customer support and more.

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