Prompting
Agentic Loop
The core cycle that powers AI agents: observe the current state, think about what to do next, act by calling a tool or generating output, then repeat.
In depth
The core cycle that powers AI agents: observe the current state, think about what to do next, act by calling a tool or generating output, then repeat. Each iteration feeds the result of the last action back into the model's context so it can decide the next step. This observe-think-act pattern is what separates agents from single-shot prompt-response interactions.
Example
The core cycle that powers AI agents: observe the current state, think about what to do next, act by calling a tool or generating output, then repeat.
Go deeper at Developers Digest
Hands-on guides, comparisons, and tutorials that cover Prompting.
FAQ
What is Agentic Loop?
The core cycle that powers AI agents: observe the current state, think about what to do next, act by calling a tool or generating output, then repeat.
Why does Agentic Loop matter for AI developers?
Agentic Loop sits in the Prompting part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Agentic Loop?
Developers Digest publishes tutorials and videos that cover Prompting topics including Agentic Loop. Check the blog and YouTube channel for hands-on walkthroughs.
Related terms
Prompting
Few-Shot Learning
A prompting technique where you include a small number of input-output examples in the prompt to show the model the pattern you want it to follow.
Prompting
Guardrails
Safety constraints and validation layers applied to AI model inputs and outputs.
Prompting
Scaffold (AI)
The surrounding code and infrastructure that turns a raw language model into a useful application.
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