Update _posts/2025-11-12-agentic-ai-patterns.adoc

mariofusco · cescoffier · web-flow · commit de11e8e0c1c8 · 2025-11-12T15:04:56.000+01:00
Co-authored-by: Clement Escoffier &lt;clement.escoffier@gmail.com&gt;
diff --git a/_posts/2025-11-12-agentic-ai-patterns.adoc b/_posts/2025-11-12-agentic-ai-patterns.adoc
@@ -91,7 +91,7 @@ public class GoalOrientedPlanner implements Planner {
 }
 ----
 
-As anticipated, here the goal coincides with the final output of the planner-based agentic pattern itself, while the path from the initial state to the goal is calculated using a graph, that is built analyzing the input and output keys of all subagents. The sequence of agents to be invoked is then calculated as the shortest path on that graph from the current state to the desired goal.
+As mentioned earlier, in this example, the goal corresponds to the final output of the planner-based agentic pattern itself. The path from the initial state to that goal is computed using a graph built by analyzing the input and output keys of all subagents. The sequence of agents to invoke is then determined as the shortest path on that graph, connecting the current state to the desired goal.
 
 The example discussed in that pull request to demonstrate this pattern at work is an agentic system that generates horoscope-based writeups. The complete source code of this example is available https://github.com/langchain4j/langchain4j/tree/main/langchain4j-agentic-patterns/src/test/java/dev/langchain4j/agentic/patterns/goap/horoscope[here]. This system is composed of multiple subagents, each responsible for a specific task, such as extracting the user's data from the prompt, fetching horoscope data from an external source, generating content based on the horoscope, and formatting the final writeup. In this situation the graph of agents dependencies, also determining the sequence of their activations, is the following:
 

Original file line number	Diff line number	Diff line change
`@@ -91,7 +91,7 @@ public class GoalOrientedPlanner implements Planner {`
`91`	`91`	`}`
`92`	`92`	`----`
`93`	`93`
`94`		`-As anticipated, here the goal coincides with the final output of the planner-based agentic pattern itself, while the path from the initial state to the goal is calculated using a graph, that is built analyzing the input and output keys of all subagents. The sequence of agents to be invoked is then calculated as the shortest path on that graph from the current state to the desired goal.`
	`94`	`+As mentioned earlier, in this example, the goal corresponds to the final output of the planner-based agentic pattern itself. The path from the initial state to that goal is computed using a graph built by analyzing the input and output keys of all subagents. The sequence of agents to invoke is then determined as the shortest path on that graph, connecting the current state to the desired goal.`
`95`	`95`
`96`	`96`	The example discussed in that pull request to demonstrate this pattern at work is an agentic system that generates horoscope-based writeups. The complete source code of this example is available https://github.com/langchain4j/langchain4j/tree/main/langchain4j-agentic-patterns/src/test/java/dev/langchain4j/agentic/patterns/goap/horoscope[here]. This system is composed of multiple subagents, each responsible for a specific task, such as extracting the user's data from the prompt, fetching horoscope data from an external source, generating content based on the horoscope, and formatting the final writeup. In this situation the graph of agents dependencies, also determining the sequence of their activations, is the following:
`97`	`97`