Agentic AI systems move beyond single prompt-response cycles into multi-step workflows where models plan, call tools, and act autonomously. These articles cover how to build and harden that class of system in Laravel — with full control over what the model can and cannot do.
Expect practical implementations, not conceptual overviews. Every article assumes you’re building something real.
Schema validation, retry logic, and observability are treated as requirements here, not optional extras.
AI coding agents write bad Laravel code not because they lack capability, but because they lack context — they cannot see your schema, your routes, or your installed package versions. Laravel Boost is the official MCP server from the Laravel team that closes that gap, giving agents live, structured access to your running application.
Integrating Laravel Boost into Your Development Workflow Read More »
There’s no official Laravel AI SDK — but Prism PHP fills that gap with a production-ready, multi-provider interface that supports tool calling, RAG, and real-time streaming out of the box. This guide walks you through building real agentic Laravel applications with working code, from pgvector similarity search to SSE streaming and response caching.
Building Agentic Laravel Apps with Prism PHP Read More »
