Generative AI vs Agentic AI
From Content Creation to Autonomous Action
As we all move on from AWS DeepRacer (well I try to) and look towards what has replaced it, the "AWS AI League", we find that it's a big step from model ML designing through the console to AI engineering.
AI is evolving fast, but not all AI is created equal. Two terms you’ll hear more and more are Generative AI and Agentic AI. While they’re often grouped together, they represent very different capabilities and very different futures.
What is Generative AI?
Generative AI refers to systems designed to create content. This includes text, images, code, audio, and more. These models are trained on vast datasets and learn patterns that allow them to generate new, "human like" outputs.
Think of Generative AI as a highly capable assistant that responds to prompts such as;
- Asking it to write a project proposal, it creates one
- Generating code to a requirements document, done, order up!
- Instant summaries to books, papers, design patterns, instant, you'll never need to read again!
However, generative AI is typically reactive. It waits for input, produces output, and basically just stops there. It doesn’t independently decide what to do next or take action beyond the initial ask.
What is Agentic AI?
Agentic AI takes things a step further. Instead of simply generating content, it can plan, decide, and act towards a goal/contextual thinking, ideas, goals or scenarios.
An agentic system can:
- Break down a high-level objective into smaller tasks
- Execute those tasks across multiple steps
- Use tools (APIs, databases, systems)
- Adapt based on results or new information
In short, Agentic AI behaves more like an autonomous worker than a tool.
For example:
- Instead of just writing a report, an agentic system could gather data, analyze trends, generate the report, and email it to stakeholders.
- Rather than suggesting code, it could build, test, debug, and deploy an application.
Key Differences
Generative AI
Role: Content creator
Behaviour: Reactive
Workflow: Single-step outputs
Autonomy: Low
Tool use: Limited
Agentic AI
Role: Goal-driven actor
Behavior: Proactive
Workflow: Multi-step processes
Autonomy: High
Tool use: Extensive
Why Does This Matter?
The transition from Generative to Agentic AI marks a fundamental shift from assisting humans to executing work on their behalf.
But can have big implications.
Productivity gains: Tasks that once required coordination across people and tools can be automated end-to-end.
A new software paradigm: Instead of apps, we may interact with systems that do things for us.
Workforce transformation: Roles may shift from execution to oversight and strategy.
Challenges to Overcome
Agentic AI also introduces new complexities:
- Reliability: Multi-step systems can fail in unpredictable ways and may not be easy to find root causes if the design is not understood
- Control: Ensuring actions align with user intent
- Safety: Preventing harmful or unintended outcomes
- Evaluation: Harder to measure success than simple outputs, test and record
These challenges mean that while agentic systems are powerful, they require careful design and monitoring.
The Future path that leads to Convergence, (hopefully)
It’s important to note that Agentic AI is not separate from Gen AI but builds on top of it. Generative models provide the intelligence, while agentic frameworks provide the structure for action.
The future likely isn’t one or the other, but a combination:
- Use Gen AI for thinking and creating
- Use Agentic AI for planning and doing
Final Thought
If Gen AI gave machines a voice, Agentic AI gives them initiative. And that shift from responding to acting may be one of the most important transitions in the next era of tech.
Hopefully these are the concepts we can explore more in a gamified way, perhaps through the AI league!
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