An agent in Laravel’s AI SDK is a dedicated PHP class that packages an AI interaction’s instructions, conversation context, tools, and optional output schema. It is a reusable unit—not automatically an autonomous system or a multi-agent workflow. Start with one agent for a focused task; compose multiple steps only when the work calls for distinct stages, independent analyses, routing, or iteration.
What an agent represents
Laravel’s 13.x documentation describes agents as the fundamental building block for interacting with AI providers through the SDK. An agent class gives a model interaction a defined role and a place to configure the parts that shape it: instructions, conversation context, tools, and an output schema.
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That class is the reusable unit; a workflow is the larger arrangement of work. A workflow may consist of one agent call, several calls to the same agent, or multiple agents performing different steps. The SDK’s agent abstraction does not, by itself, mean the model operates autonomously or makes independent decisions.
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Instructions
Instructions establish the agent’s role and how it should approach its task. Keep them aligned with a focused responsibility; a class is easier to reuse and reason about when its purpose is clear.
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Conversation context
Context gives the interaction relevant conversation information. It helps the agent respond with continuity rather than treating every request as isolated.
Tools
Tools let an agent use capabilities beyond producing text. Add them when the task needs an action or access to a capability; a simple response task does not require tools just because the SDK supports them.
Output schema
An output schema defines a structured shape for the result when an application needs predictable fields rather than unconstrained prose. It is optional: use structured output when the next part of your application depends on a defined result shape.
Laravel’s AI product page also describes broader SDK capabilities such as memory, streaming, broadcasting, queues, built-in web and file tools, retrieval and search features, provider failover, and testing. These are capabilities around the toolkit, not requirements for every agent.
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Generate an agent class
Laravel’s 13.x documentation provides Artisan commands for creating an agent. Run the command from a Laravel application:
php artisan make:agent SalesCoachTo generate an agent for structured output, use the documented option:
php artisan make:agent SalesCoach --structuredThese commands create the starting class; configure its responsibilities and capabilities to suit the application. Check the documentation for your installed SDK version before relying on any particular generated-file details.
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When one agent is enough—and when to compose a workflow
Laravel’s guidance is to begin with a single agent() call for most tasks. Keep the flow simple when one focused interaction can do the job. Add steps only to meet a real requirement—for example, a fixed review stage, different handling for different request types, or analyses that can run independently.
Laravel’s workflow guidance describes five patterns. Choose among them by looking at how the work is structured, whether steps depend on one another, whether independent work can run concurrently, and whether there is a clear quality bar for evaluation.
| Pattern | Task shape | How the steps relate | When it fits |
|---|---|---|---|
| Prompt chaining | A defined sequence, such as draft, review, and improve | One step’s output feeds the next | Use when the order is known and each stage benefits from the preceding result. |
| Routing | Requests differ by type or complexity | A classification step directs work to a suitable specialist | Use when different requests need different handling. |
| Parallelization | Several independent analyses are needed | Analyses can happen concurrently, then be synthesized | Use when the analyses do not depend on each other and concurrency is useful. |
| Orchestrator-workers | The work plan is not known up front | A coordinating agent delegates focused subtasks as tools | Use when a coordinator needs to determine and distribute the subtasks. |
| Evaluator-optimizer | A result needs refinement against a quality bar | Generation and evaluation repeat in a bounded loop | Use when evaluation criteria are explicit and iteration is justified. |
Start with a small example, then add only the needed step
Suppose an application needs a sales-coaching response. A focused SalesCoach agent can receive the request and return guidance. If the product requirement later calls for a separate review before showing the response, a prompt chain can add a review step that receives the first result. The second step is justified by that review requirement—not by a general assumption that more agents produce better answers.
Likewise, route to specialists only if request categories genuinely need different treatment; run parallel analyses only if they are independent; and use evaluator-optimizer iteration only when the application has a defined standard to evaluate against. Extra steps add orchestration and model interactions, so their shape should follow the work rather than precede it.
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As listed on Packagist on October 9, 2026, laravel/ai is at v1.1.0, published October 5, 2026. Its package metadata requires PHP ^8.3 and Illuminate components for Laravel 12.x or 13.x, with specific JSON-schema component constraints. These are version-sensitive package facts, not a guarantee that every Laravel application meets all dependency requirements.
Before adding the package, compare its current metadata and the SDK documentation with your application’s PHP and Laravel versions, including the required JSON-schema constraints. Do not assume an example or compatibility statement for one SDK release applies unchanged to another.
Quick Recap
- Laravel 13.x AI SDK documentation
- Laravel: AI workflows with the Laravel AI SDK
- Laravel AI product page
- Packagist package metadata for laravel/ai
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