How to create a content agent (WriterAgent) in Laravel using Laragent.ai

production-backends

how can I embed an LLM agent in Laravel so that it is manageable, scalable, and ready for growth?

Laragent.ai

agent-layer

how to assemble a WriterAgent in practice

What are we going to build

WriterAgent

• forms the structure of the content

• writes a text

• adds CTA and meta data

• generates a prompt to illustrate

• saves the result to the admin panel

He doesn't just respond with text

Architectural model Laragent.ai

Laragent.ai It relies on 4 basic roles:

It is this model that allows us to move away from "magic chains" and move to a managed architecture.

Step 1. Installation and basic integration

inside the app

After installation, you get:

• infrastructure for agents

• LLM single point of call

• the tools (function calling) mechanism

• hooks and lifecycle events

This immediately makes the agent part of the backend logic, rather than a "side chatbot."

Step 2. Create a WriterAgent (Agent role)

An agent is a class that answers a question:

Who are you and by what rules do you work?

For WriterAgent, this is usually:

• Role: content performer

• Style: brand, tonality, prohibitions

• Response format: strictly structured

• Strategy: don't ask questions, but use tools

An example of the concept:

• always returns JSON

• Doesn't make up facts

• if there is not enough data, use the tool

• publication-oriented, not "draft-oriented"

This immediately distinguishes a production agent from a regular LLM chat.

Step 3. Tools — Agent communication with business logic

the active component of the system

The minimum set of tools for WriterAgent

1. Getting the brand context
   
   

2. SEO brief
   
   

3. Quality control
   
   

4. Saving a draft
   
   draft_id

5. Publication (optional)
   
   

He doesn't know

what tools are available?

Step 4. Memory — Context management

In Laragent.ai memory is not rigidly imposed, and this is a plus.

It is logical for WriterAgent to divide the memory into levels:

1. Session memory

2. Project memory

3. Knowledge memory (RAG)

Step 5. Orchestrator — the agent's work scenario

managed pipeline

Basic workflow WriterAgent

1. Get a brand context

2. Get an SEO Brief

3. Generate a plan and theses

4. Generate Text

5. Check the quality

6. Refine it if necessary.

7. Save the draft

8. Return a structured result

Every step:

• logged

• can be repeated

• measured by time and cost

This is critical for SaaS and scaling.

Result format: not text, but structure

data

{
  "title": "...",
  "outline": [
    { "h2": "…", "h3": ["…","…"] }
  ],
  "article": "...",
  "cta_blocks": [
    { "position": "after_h2_2", "text": "..." }
  ],
  "meta": {
    "description": "...",
    "tags": ["..."]
  },
  "image_prompt": "...",
  "draft_id": 42
}

This format:

• it is easily displayed in the admin area

• easily editable

• suitable for API

• ready for auto-publishing

How a multi-agent system grows out of WriterAgent

the architecture does not change

When the WriterAgent becomes a bottleneck, you add:

• PlannerAgent is just a structure

• EditorAgent — style and abbreviation

• FactCheckerAgent — facts and sources

• PublisherAgent — publishing and analytics

Multiple agents

Why not Laragent.ai — the right choice for the future

• Laravel-native

• API-first

• without vendor lock-in

• focused on agents, not chains

• It can be easily integrated into existing WEB services.

Result

Laragent.ai

• create a production content agent in Laravel

• embed it in the admin panel or SaaS

• manage quality, cost, and logic

• switch to a multi-agent system without rewriting

think not in terms of “LLM responses”, but in terms of processes and roles.