Traditional Generative AI vs Agentic AI: What’s Different and Why It Matters

Tags: AI, Generative AI / Agentic AI

Published: Mar, 13th, 2025

Author: Left Field Labs

AGI vs Generative Image side by side view stacks of features they are contained.

2025 has us all talking about a lot of things—an inauguration, lackluster Super Bowl and polemic half-time show, intense Arctic cold snap, and everything AI always. Related to the latter, there’s been a clear shift this year in AI evolution with the rise of AI agents. While there is a lot of hype building about agentic AI, I wanted to move beyond speculation to look at what’s really going on.

Both generative AI and agentic AI fall under the broader artificial intelligence umbrella. However, their capabilities, applications and implications for business differ significantly. Understanding these differences is especially crucial for organizations looking to make smart choices about where to invest in AI.

What is generative AI?

Generative AI refers to artificial intelligence systems designed to generate text, images, code, and other outputs based on input prompts.

By now everyone has heard of generative AI. Let’s make sure we’re all talking about the same thing. Generative AI refers to artificial intelligence systems designed to generate text, images, code, and other outputs based on input prompts. These models rely on deep learning techniques to analyze patterns and produce contextually relevant content. While highly effective for content creation, data analysis, and automation, we should always remember that generative AI operates reactively, responding to inputs rather than proactively making decisions or taking actions.

What is agentic AI?

On the other hand, agentic AI goes beyond content generation by incorporating autonomy, decision-making, and long-term context management. Agentic AI can initiate actions, adapt to new information, and solve complex problems without constant human intervention—notably different from generative AI, which primarily responds to prompts.

These AI agents are able to interact with systems, execute multi-step processes, and refine their approaches based on real-time data, making them the more dynamic and capable cousins to generative AI, ideal for business applications.

What are AI agents?

When you hear AI agents, you might be tempted to imagine the nearly unlimited capabilities of virtual Jarvis from the Marvel Cinematic Universe. While AI agents are advanced AI systems designed to perform tasks with minimal human guidance, they’re far from limitless…for now.

They combine generative AI’s ability to understand and process information with agentic AI’s capacity for independent action. AI agents can analyze data, manage workflows, interact with multiple platforms, and make informed decisions based on evolving circumstances.

Rather than simply responding to prompts, they can:

Process complex problems from start to finish
Maintain context over extended periods
Make autonomous decisions about next steps
Interact with multiple systems and databases
Request additional information when needed

As Eric Lee notes in How AI Assistants Everywhere Will Fail to Deliver on the Promise of AI: “Companies have lost sight of AI's initial promise: to act as a true personal assistant, to remove the tedium of life's clerical duties, to surface the ‘right’ information at just the right moment, and to be an omnipresent support system able to adapt to your context in real time.”

“Companies have lost sight of AI's initial promise: to act as a true personal assistant, to remove the tedium of life's clerical duties, to surface the ‘right’ information at just the right moment, and to be an omnipresent support system able to adapt to your context in real time.”

That original promise is finally coming back into focus, with agentic AI enabling more human-centered, context-aware experiences that feel less like tools, and more like trusted collaborators.

Generative AI vs AI agents: The GPS vs the driver

Here’s another way to think about the difference between these two: A traditional GenAI model is like a GPS—it provides directions based on input but requires a driver to follow them. It excels at guidance but doesn’t take action on its own.

An AI agent, on the other hand, is like a driver—it not only navigates but also operates the car, adjusts routes in real time based on traffic, refuels when necessary, and ensures a smooth journey from start to finish.

How generative AI and AI agents will shape business

The evolution from traditional GenAI to AI agents represents a fundamental shift in how organizations can leverage artificial intelligence. However, success requires more than just adoption—it demands strategic thinking and careful implementation. At this point, we need to be careful to avoid the “cacophony of help”—a fragmented ecosystem of AI assistants, each operating in its own silo.

Instead, organizations should aim for an open, decentralized approach centered on:

Individual data ownership
Granular permissions for information sharing
Interoperability between systems
Focus on user-centric solutions

The promise vs. reality of AI Agents: Key challenges and adoption trends

While the potential of AI agents is compelling, current implementation challenges highlight the importance of strategic adoption. According to Gartner, 30% of generative AI projects will be abandoned after proof of concept by 2025, often due to unclear business value or inadequate infrastructure.

Here are four common approaches to AI adoption:

1. All-Iners: Typically startups betting everything on new technology
‍2. Big Betters: Established companies making substantial, long-term investments
‍3. Toe Dippers: Organizations taking measured approaches through pilots
‍4. Wait and Seers: Companies watching market developments before acting

Ensuring AI agents deliver real business value

Align with business strategy and risk tolerance
Focus on concrete user needs
Build gradually based on proven value
Maintain flexibility in approach

As we move forward, the distinction between traditional GenAI and AI agents will become increasingly important. Organizations must avoid the trap of adopting technology for technology's sake and instead focus on solving real business problems.

The most successful implementations won't be determined by who adopts fastest, but by who adopts smartest:

As Eric Lee puts it in Techno-Panic: “By putting human needs first, making strategic decisions around how to invest, and properly executing upon these decisions, companies of any size can transform innovation from a risky gamble into a reliable engine for meaningful growth.”

“By putting human needs first, making strategic decisions around how to invest, and properly executing upon these decisions, companies of any size can transform innovation from a risky gamble into a reliable engine for meaningful growth.”

It’s a timely reminder: meaningful transformation doesn’t come from chasing hype—it comes from designing with purpose and scaling with intention.

Strategic implementation of AI agents: A framework for success

To successfully implement AI agents, organizations should follow a human-centered approach:

1. Start with real problems‍‍

Identify specific business processes that could benefit from automation
Focus on areas where maintaining context and making decisions is crucial
Ensure the solution addresses actual user needs rather than technological novelty

‍2. Assess integration requirements‍‍

Evaluate existing systems and data sources
Determine necessary connections and APIs
Consider security and compliance requirements

3. Build for scale

Start with pilot programs in controlled environments
Establish clear metrics for success
Plan for gradual expansion based on proven results

4. Maintain human oversight

Implement appropriate confidence thresholds
Establish clear escalation paths
Ensure transparency in decision-making processes

Leverage agentic AI and AI agents with Left Field Labs

You’ll need three things to navigate the shift from traditional generative AI to AI agents: expertise, strategy, and the right technology. At Left Field Labs, we specialize in designing and implementing AI solutions that go beyond automation—helping businesses build intelligent, adaptive AI agents that drive real impact.

Whether you're exploring generative AI, transitioning to agentic AI, or looking to integrate AI agents into your workflows, our team can guide you through the process. From strategy development to implementation and optimization, we ensure your AI investment aligns with your business goals and delivers tangible results.

Ready to move beyond the hype and build AI that works for you? Get in touch with Left Field Labs today.