What is the difference between agentic and non-agentic AI?

Short answer:
No, Google’s “AI Summary” (the feature that turns a list of search‑engine results into a concise paragraph) is not an agentic AI.

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What “agentic AI” means

Term	Typical characteristics	Example
Agentic AI	• Holds a goal or purpose (e.g. “get the user an answer”). • Can perceive its environment (receive input). • Decides how to act (planning, policy selection). • Acts autonomously to achieve the goal (e.g. executing steps, influencing the environment). • May learn continuously from outcomes.	Autonomous vehicles, trading bots, personal digital assistants that can request permission or take actions on behalf of a user.

Google’s AI Summary is:

1. Goal‑driven – its target is to provide a short answer, but only when the user supplies a query. Stores no long‑term goal beyond “show you a summary on request”.
2. Perceptual – it receives a search query from the user.
3. Decision‑making – it selects a template (“It will take”) and pulls a coherent paragraph from a set of matched pages.
4. Action – it outputs the paragraph on the UI.

The key missing element is autonomy: the system never initiates a request or acts independently once a query is received. It doesn’t interact with the outside world beyond the user’s input and doesn’t possess an internal policy that adapts or learns beyond the pretrained model. Hence it is a servant or assistant AI rather than an agent.

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Where it sits on the spectrum

User  -->  Query  -->  AI‑Summary Engine  -->  Result

• No planning module: The engine simply feeds the user’s prompt to a language model, post‑processes the output with regex/rules, and displays it.
• No interaction loop: Unlike a true agent, it’s not continually supervising an environment, adjusting actions, or learning from success/failure feedback.
• No intentionality: Its “intent” is injected by the user each time; it has no self‑defined mission.

In contrast, an agentic system (e.g., a home service robot) would:

Perceive -> Plan -> Act -> Observe → Learn

and operate independently of explicit user prompts for each step.

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Practical Implications

Aspect	AI Summary	True Agent
Initiation	Requires a user query each time.	Can start actions on its own.
Learning	Only retrained at the system level, not on a per‑interaction basis.	Can adapt policies during operation.
Decision Complexity	Runs a simple , generate → post‑process pipeline.	Complex decision‑trees, reinforcement learning policies.
Autonomy / Control	User fully controls when it runs.	Can influence or override user preferences.

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Bottom line

Google’s AI Summary is a generative‑support tool, not an autonomous agent. It can be seen as an intelligent assistant that assists with summarization, but it lacks the autonomy and online decision‑making that characterize “agentic AI.”

No, Google’s AI Overviews (what I believe you’re referring to as “AI Summary search”) isn’t truly agentic AI, though it’s a step in that direction. Let me break it down quickly.

What is Agentic AI?

Agentic AI refers to systems that exhibit autonomy, goal-directed behavior, and the ability to plan, reason, and act in complex, multi-step ways to achieve objectives. Think of it as AI that can break down tasks, interact with tools or environments (e.g., browsing the web, running code, or chaining API calls), adapt to new information, and operate with minimal human intervention. Examples include research prototypes like Auto-GPT or more practical tools like Devin (an AI software engineer).

What is Google’s AI Summary Search?

This is Google’s AI-driven feature in Search (rolled out in 2024 as AI Overviews) that generates concise, synthesized summaries of search results based on your query. It pulls from web sources, reasons over them, and outputs a boxed overview—often with citations—to help you quickly grasp key info without clicking through links. It’s powered by models like Gemini and integrates multimodal understanding (e.g., handling images or videos).

Why Not Agentic?

• Reactive, not proactive: It responds to your explicit query but doesn’t independently pursue goals, monitor environments, or initiate actions. You ask, it summarizes—no ongoing agency.
• Limited autonomy: It doesn’t plan multi-step workflows, use external tools dynamically (beyond internal search indexing), or self-correct in real-time loops. It’s essentially a sophisticated query-response system, like an enhanced FAQ generator.
• Scope: While it shows emergent reasoning (e.g., connecting facts across sources), it’s bounded by Google’s ecosystem and doesn’t “act” in the world (e.g., no booking flights or editing files on your behalf).

That said, it’s a clever blend of retrieval-augmented generation (RAG) and summarization, making search more efficient. True agentic search might evolve from this—imagine it autonomously refining queries or taking actions based on your preferences. For now, it’s more “helpful assistant” than “autonomous agent.” If you meant a different Google feature, feel free to clarify!

The question of whether Google’s AI-powered summary search results can be considered “agentic AI” is an interesting one, and the answer largely depends on how you define “agentic AI.”

Here’s a breakdown to help understand the nuances:

What is “Agentic AI”?

At its core, an agentic AI is typically understood to be an AI system that exhibits the following characteristics:

• Autonomy: It can operate independently to achieve goals without constant human intervention.
• Goal-Oriented: It has a clearly defined objective or set of objectives it is trying to achieve.
• Perception: It can take in information from its environment (e.g., read text, analyze data).
• Decision-Making/Reasoning: It can process information, understand context, and make choices or draw conclusions based on its goals.
• Action: It can take steps or produce outputs to influence its environment or achieve its goals. This might involve generating text, executing code, or controlling a robot.
• Adaptation/Learning (often): Many agentic AIs will learn and adapt over time to improve their performance.

Applying this to Google’s AI Summary Search:

Let’s analyze Google’s AI summary search against these characteristics:

• Autonomy: To a degree, yes. Once you perform a search, the AI system autonomously generates a summary. You don’t instruct it step-by-step on how to summarize each result.
• Goal-Oriented: Yes, its primary goal is to synthesize information from various search results and present it in a concise, informative summary to answer a user’s query.
• Perception: Definitely. It “reads” and comprehends the content of countless web pages and other indexed information.
• Decision-Making/Reasoning: Absolutely. This is where a lot of the AI’s power lies. It has to:
  • Determine the most relevant information from multiple sources.
  • Identify key concepts and facts.
  • Synthesize disparate pieces of information.
  • Structure the summary logically.
  • Avoid redundancy and irrelevance.
  • (Ideally) understand the intent behind the user’s query.
• Action: Yes, its action is to generate and display the summary text on the search results page.
• Adaptation/Learning: Google’s underlying AI models are constantly being updated and refined through vast amounts of data and feedback, so the quality and accuracy of the summaries improve over time.

The “Agentic” Gray Area:

Here’s why it’s not a clear-cut “yes” and why some might hesitate to call it fully agentic:

• Limited Scope of Action: The “actions” are primarily textual (generating summaries). It doesn’t, for example, then go on to book a flight based on your query or execute a complex multi-step task in the real world. Its agency is confined to the information retrieval and summarization domain.
• Reactive vs. Proactive: It’s largely reactive. It responds to a user’s explicit query. It doesn’t independently decide to go out and “summarize the internet” without a prompt. A more “agentic” system might proactively seek out information or perform tasks without direct human initiation in a continuous loop.
• Lack of “Personal” Goals: The AI itself doesn’t have its own desires or long-term personal goals beyond its programmed function. It serves the user’s goal.
• Not a “Body” or “Environment Interaction”: While it interacts with the digital environment (web pages), it doesn’t have a physical embodiment or sensors that allow it to interact with the physical world in the way we often imagine more futuristic agents.

Conclusion:

Google’s AI Summary search exhibits many characteristics of agentic AI, particularly in its autonomous information processing, goal orientation, and decision-making for a specific task.

• You could argue it’s a form of “weak” or “narrow” agentic AI: It’s an agent acting in the domain of information summarization, with clear goals and autonomous execution within that domain.
• It’s generally not considered “strong” or “general” agentic AI: It lacks the broader autonomy, proactive behavior, and ability to operate across diverse, real-world tasks that are often associated with more advanced agentic systems.

Think of it as a highly specialized, intelligent agent designed for a very specific job: summarizing information from the web to answer your search queries. It’s a step in that direction, but not the full manifestation of what people often envision as a truly autonomous, multi-purpose AI agent.

To determine if Google’s AI Summary (such as AI Overviews in Search) qualifies as agentic AI, we need to analyze its core functionality against the definition of agentic systems:

Key Characteristics of Agentic AI:

1. Autonomy: Operates independently toward goals without continuous human input.
2. Goal-Oriented Action: Acts strategically to achieve specific objectives.
3. Proactivity: Initiates tasks or decisions based on context.
4. Adaptability: Learns and evolves from interactions.

Google AI Summary’s Functionality:

• Passive Response: Generates summaries only in response to user queries (no self-initiated action).
• Static Processing: Uses predefined models to retrieve/summarize existing information. Does not set goals, reason dynamically, or strategize.
• No Persistent Agency: Lacks memory, long-term planning, or adaptive learning beyond its training data.
• Tool-Like Behavior: Acts as an information filter—similar to a sophisticated search algorithm—not an autonomous agent.

Verdict:

No, Google’s AI Summary is not agentic AI. It is a reactive, task-specific tool (generating summaries from indexed data) rather than a proactive, goal-pursuing agent. It exhibits narrow AI capabilities, focused solely on information retrieval and summarization within rigid parameters.

True agentic AI would involve systems capable of end-to-end problem-solving (e.g., booking flights, negotiating, or iterating on feedback autonomously), which Google Search’s summaries do not demonstrate.