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Traditional software typically follows a static execution flow where developers explicitly define each step through method calls, conditions, and workflows. AI systems introduce a different paradigm. Instead of following a predetermined path, the model evaluates a goal and dynamically determines which actions should be executed.
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The Planning Pattern enables the model to decompose a request into smaller tasks, select the appropriate tools, retrieve information, and compose a final response. This allows enterprise applications to combine deterministic business logic with the flexibility of AI-driven decision-making.
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For example, when a user asks:
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[source,mermaid]
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----
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%%{init: {
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"theme": "base",
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"themeVariables": {
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"primaryColor": "#F8F7F7",
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"primaryTextColor": "#1D5183",
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"primaryBorderColor": "#1D5183",
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"lineColor": "#019DDC",
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"secondaryColor": "#F8F7F7",
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"secondaryBorderColor": "#019DDC",
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"tertiaryColor": "#1D5183",
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"tertiaryTextColor": "#F8F7F7"
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}
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}}%%
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sequenceDiagram
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actor User
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participant UI as JSF + PrimeFaces
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participant Agent as AI Travel Agent<br/>(TravelService)
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participant CityTools as CityTools
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participant AttractionTools as AttractionTools
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participant MongoDB as MongoDB
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User->>UI: Create a historical itinerary in Portugal
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