# What is agentic AI? Definition, how it works and examples (2026)

Canonical: https://theagenticweekly.com/what-is/agentic-ai · Last verified: 2026-10-25 · Published: 2026-10-25 · Author: Agentic Weekly (operated independently from Texas, US) · Evidence: Evidence: definitions are Reported from Google Cloud's and Anthropic's own pages, read on 2026-10-25. We did not build or run an agentic system for this page. Example tools are linked to their own pages, where each carries its own evidence label.

> Agentic AI is AI that can pursue a goal on its own: it gathers information, plans steps, takes actions through tools and checks the results, with minimal human intervention. It builds on generative AI but goes beyond creating content by acting on other systems, and it is made of AI agents, which are the individual building blocks. Our evidence here is Reported from Google Cloud and Anthropic documentation; we did not run an agentic system for this page.

## Agentic AI meaning in one paragraph

Google Cloud defines agentic AI as "an advanced form of artificial intelligence focused on autonomous decision-making and action" that, unlike traditional AI which mostly responds to commands or analyzes data, "can set goals, plan, and execute tasks with minimal human intervention". Anthropic uses the broader term agentic systems for two designs: workflows, where models and tools follow predefined code paths, and agents, where the model dynamically directs its own process and tool use.

## The five parts of an agentic system

Google Cloud lists five components that repeat in a loop:

1. **Perception:** gather information from sensors, databases and user interfaces.
2. **Reasoning:** an LLM analyzes the data, understands context and proposes solutions.
3. **Planning:** set goals, break them into smaller steps and choose how to achieve them.
4. **Action:** carry out tasks, make decisions and interact with other systems.
5. **Reflection:** evaluate the results and adjust future plans and actions.

Anthropic's description is simpler: agents are "typically just LLMs using tools based on environmental feedback in a loop", and its basic building block is an LLM augmented with retrieval, tools and memory.

## Agentic AI vs generative AI vs AI agents

Table: Three terms often confused (Google Cloud definitions, summarised)

|  | Generative AI | AI agent | Agentic AI |
|---|---|---|---|
| What it is | AI that creates new content such as text, images, code or music from prompts | An autonomous entity built to perform a specific task | A coordinated system that uses multiple agents for complex workflows |
| Value comes from | What the model can do, plus simple extensions | Doing one job with tools | Orchestrating agents toward a higher-level goal |
| Example | Create marketing materials | An agent that handles one task | Deploy the materials, track results and adjust the strategy |

Google Cloud's analogy: AI agents are individual tools in a toolbox, and agentic AI is the coordinated use of them to build a house. It also describes agentic AI as a subset of generative AI centred on orchestrating agents that use LLMs as a "brain" to act through tools.

## Workflows vs agents: when autonomy is worth it

Anthropic recommends finding the simplest solution possible and adding complexity only when needed, noting that agentic systems often trade latency and cost for better task performance. Workflows suit well-defined tasks where predictability matters. Agents suit open-ended problems where you cannot predict the number of steps or hard-code a path, and where you can trust the model's decisions in a controlled environment.

- **Cost and errors:** Anthropic says agents' autonomy means higher costs and the potential for compounding errors, and recommends extensive testing in sandboxed environments with guardrails.
- **Tools matter:** Anthropic stresses that agents need clearly designed tools and documentation.
- **Stopping conditions:** examples include a maximum number of iterations, and pausing for human feedback at checkpoints.

## Examples of agentic AI

Google Cloud lists customer service, supply chain management, healthcare, financial services and software development as areas where agentic AI is applied. Anthropic highlights customer support and coding agents as two practical domains, because both have clear success criteria, feedback loops and room for human oversight.

In software, see our explainer on [AI coding agents](https://theagenticweekly.com/what-is/ai-coding-agent) and the tool pages for [Claude Code](https://theagenticweekly.com/tools/claude-code), [Codex CLI](https://theagenticweekly.com/tools/codex-cli), [goose](https://theagenticweekly.com/tools/goose) and [opencode](https://theagenticweekly.com/tools/opencode). For how agents connect to other systems, see [what is an MCP server](https://theagenticweekly.com/what-is/mcp-server).

## What to consider before using it

Google Cloud's list of considerations includes clear objectives, data quality, ethical implications, governance and security, explainability, integration, and monitoring and evaluation. It specifically names guardrails such as workspace sandboxing and having human oversight in place to prevent unintended consequences.

## FAQ

### What is agentic AI in simple terms?

Agentic AI is AI that can work toward a goal by itself: it gathers information, makes a plan, takes actions through tools and learns from the results, with little human involvement.

### What is the difference between agentic AI and generative AI?

Generative AI creates content such as text, images or code from prompts. Agentic AI goes further by taking actions in other systems to reach a higher-level goal, and can use generative AI as one of its tools.

### What is the difference between agentic AI and an AI agent?

Per Google Cloud, AI agents are the building blocks: each performs a specific task. Agentic AI is the overarching system that coordinates multiple agents for complex workflows.

### What is the difference between an AI workflow and an AI agent?

Anthropic defines workflows as LLMs and tools orchestrated through predefined code paths, and agents as systems where the LLM dynamically directs its own process and tool use.

### When should I not use an agent?

When a simpler approach works. Anthropic recommends the simplest solution possible and notes that agents trade cost and latency for task performance and can compound errors.

### What are examples of agentic AI tools?

In coding, Claude Code, Codex CLI, goose and opencode are agent tools we have smoke-tested. Google Cloud's examples include Google Antigravity and its Agent Development Kit.

## Sources

- [What is agentic AI? (Google Cloud)](https://cloud.google.com/discover/what-is-agentic-ai) (fetched 2026-10-25)
- [Building effective agents (Anthropic)](https://www.anthropic.com/engineering/building-effective-agents) (fetched 2026-10-25)
- [Claude Code overview (Anthropic docs)](https://code.claude.com/docs/en/overview) (fetched 2026-10-25)

## Related

- [What is an AI coding agent?](https://theagenticweekly.com/what-is/ai-coding-agent)
- [What is an MCP server?](https://theagenticweekly.com/what-is/mcp-server)
- [What is vibe coding?](https://theagenticweekly.com/what-is/vibe-coding)
- [Best AI agent tools](https://theagenticweekly.com/best/agent-building)
- [How we pick and test](https://theagenticweekly.com/methodology)

## Changelog

- 2026-10-25: First published. Facts read from the official pages listed under Sources on this date.

## How to cite

Agentic Weekly, "What is agentic AI? Definition, how it works and examples (2026)", last updated 2026-10-25, https://theagenticweekly.com/what-is/agentic-ai