Training course · AI Agent Development · PHP & Laravel/Symfony

AI Agent Development for PHP Developers

Build AI agents professionally: architectures, tool use, agent SDKs, LangGraph, memory, evals, guardrails and deployment.

Duration
2 days (14 hrs)
Format
Online or on-site
Language
German or English
Participants
open dates or exclusive for your team
Certificate
Certificate of attendance

Overview

This course connects AI Agent Development with the everyday processes of PHP & Laravel/Symfony. Participants work on realistic scenarios and leave with prompts, workflows and checklists they can use straight away.

AI agents go beyond conventional chatbots: they break tasks down, call tools and APIs, evaluate the results and keep iterating until a goal is met. In this two-day course you learn to build such systems so that they work reliably, transparently and safely in day-to-day business. You compare architectural patterns ranging from simple workflows through the agent loop to multi-agent systems, and you implement them hands-on with the Claude Agent SDK, the OpenAI Agents SDK and LangGraph.

The focus is not on a quick demo but on the path to production. You design tool interfaces and context deliberately, connect tools via the Model Context Protocol, plan short-term and long-term memory and build evaluations that surface regressions early. On top of that come guardrails against prompt injection and harmful actions, human-in-the-loop approvals, tracing, cost control and deployment. By the end you can judge when an agent is worthwhile, when a deterministic workflow is enough and which framework fits your project.

PHP powers a large share of the web – from Laravel and Symfony applications to Shopware and Magento stores and bespoke platforms that have evolved over many years. As a result, modern framework projects frequently sit alongside legacy code without types, tests or Composer structure. AI coding assistants and agents help on both fronts: they write PHPUnit and Pest tests, bring code up to modern language standards with Rector, work through PHPStan findings level by level, generate Eloquent or Doctrine models with migrations and explain tangled legacy logic. The prerequisite is good context on framework conventions, coding standards and the test environment. This course teaches you to use agents so that modernisation becomes predictable and safe.

The course is delivered live – either online in a virtual classroom or on-site in our training rooms in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart and Kaiserslautern. We can also run it exclusively for your team.

Who is this course for?

  • PHP developers working with Laravel or Symfony
  • Developers of e-commerce systems such as Shopware or Magento
  • Agency teams juggling many client projects in parallel
  • Teams modernising legacy PHP applications

Typical challenges we address

  • Legacy code without type declarations, namespaces or automated tests
  • Laborious upgrades of PHP versions and major framework releases
  • Security risks from SQL injection, XSS and unsafe file uploads in old code
  • Tight deadlines on agency projects combined with high quality expectations

What you will be able to do

  • You choose the right pattern for a task, from a fixed workflow to an autonomous agent.
  • You design tools and context so that agents work reliably and cost-effectively.
  • You implement agents with the Claude Agent SDK, the OpenAI Agents SDK or LangGraph.
  • You set up evaluations and tracing to measure quality and detect regressions.
  • Use AI agents for tests, static analysis and upgrades in Laravel, Symfony and legacy projects.
  • Modernise legacy code incrementally and safely with AI support.
  • Recognise typical security flaws in generated and existing PHP code and fix them in a targeted way.

Course outline

Day 1

Module 1 Agent architectures and design patterns
  • Workflows versus agents: prompt chaining, routing, parallelisation, orchestrator-worker
  • The agent loop: plan, act, observe, reflect
  • Multi-agent systems, subagents and handoffs between agents
  • Criteria for choosing model, framework and level of autonomy
Module 2 Tool use and context engineering
  • Function calling and structured outputs with JSON Schema
  • Designing good tools: granularity, naming, error messages, idempotency
  • Connecting and reusing tools via MCP servers
  • Managing the context window: summarisation, compaction, retrieval and prompt caching
Module 3 Frameworks in practice
  • Claude Agent SDK: agent loop, tools, subagents, permissions and hooks
  • OpenAI Agents SDK: agents, handoffs, guardrails and tracing
  • LangGraph: stateful graphs, checkpointing and human-in-the-loop
  • Side-by-side comparison on the same task and criteria for choosing a framework
Module 4 Memory, evals and observability
  • Short-term and long-term memory, storage strategies and forgetting
  • Evaluation datasets, LLM-as-a-judge and task-based success metrics
  • Tracing tool calls, token usage and latency
  • Regression testing when prompts, models or tools change

Day 2

Module 5 Guardrails, security and deployment
  • Protecting against prompt injection, data exfiltration and over-privileged tools
  • Input and output checks, approval steps and sandboxing
  • Deploying as a service, asynchronous execution, retries and cost limits
  • Governance, audit logging and requirements under GDPR and the EU AI Act
Module 6 Practice lab: PHP & Laravel/Symfony
  • Generating PHPUnit and Pest tests
  • Raising PHPStan levels step by step
  • Upgrades with Rector
  • Building Laravel and Symfony features
  • Understanding and untangling legacy code
  • Finding security vulnerabilities
Module 7 Data protection, quality & transfer to daily work
  • PHP applications frequently process customer data from shops and portals, so database dumps and logs containing personal data must never go into prompts unfiltered. Check new Composer packages for licence and vulnerabilities, keep credentials from .env files out of context and, as an agency, agree contractually whether client code may be processed by AI services.
  • Ground rules for safe AI use in your team
  • Checking results: facts, sources, tests and the four-eyes principle
  • Personal action plan for the next 30 days
  • Open questions, outlook and resources for further learning

Practical scenarios from your field

Generating PHPUnit and Pest tests

The agent writes unit and feature tests for controllers, services and models, uses factories and database transactions, and runs the tests iteratively until they pass consistently.

Raising PHPStan levels step by step

The AI works through static analysis findings systematically, adds type declarations, generics annotations and return types, and raises the analysis level in controlled steps without changing behaviour.

Upgrades with Rector

Rector rules are configured and run, remaining incompatibilities in PHP or framework upgrades are fixed by hand and deprecations surfaced in logs and tests are cleared systematically.

Building Laravel and Symfony features

From a functional description, the assistant creates migrations, Eloquent or Doctrine entities, form requests or validators, controllers, policies and tests – all in line with framework conventions.

Understanding and untangling legacy code

The assistant explains procedural legacy logic, extracts functions into namespaced classes and introduces Composer autoloading, laying the groundwork for further modernisation. Characterisation tests pin down the existing behaviour first.

Finding security vulnerabilities

The AI checks code for unsafe SQL queries, missing output escaping, CSRF gaps and insecure uploads, replacing problem areas with prepared statements and built-in framework mechanisms.

Possible hands-on projects

Laravel admin panel

A small management back end with authentication, roles, CRUD screens and feature tests, built by an AI agent from a short specification.

Symfony console importer

A Symfony console command that validates CSV or XML data, imports it into Doctrine entities and logs errors in a clear, readable way.

Upgrade readiness check

A script that runs PHPStan, a Rector dry run and Composer audit against a PHP project and produces a plain-language report on upgrade effort.

Prerequisites

Solid programming skills in Python or TypeScript, experience with REST APIs and some hands-on experience with LLM APIs or AI assistants. Solid PHP skills and experience with Laravel or Symfony, Composer and Git.

Technical requirements

A laptop with Python or Node.js, Git and a code editor, plus API access to at least one LLM provider such as Anthropic or OpenAI. Credentials for the exercises can be provided if needed.

Data protection & compliance

PHP applications frequently process customer data from shops and portals, so database dumps and logs containing personal data must never go into prompts unfiltered. Check new Composer packages for licence and vulnerabilities, keep credentials from .env files out of context and, as an agency, agree contractually whether client code may be processed by AI services.

Suggested dates

All dates are suggestions – your preferred date is always possible.

  • Wed, 25 November 2026 – Thu, 26 November 2026 Online Request
  • Thu, 10 December 2026 – Fri, 11 December 2026 Salzburg Request
  • Tue, 15 December 2026 – Wed, 16 December 2026 Online Request
  • Mon, 11 January 2027 – Tue, 12 January 2027 Bremen Request
  • Thu, 28 January 2027 – Fri, 29 January 2027 Online Request
  • Wed, 10 February 2027 – Thu, 11 February 2027 Augsburg Request

Frequently asked questions

Which agent framework should we use?

It depends on your model strategy, the level of control you need and your team’s skills. Vendor SDKs offer a lean start with tightly integrated features, while LangGraph gives you finer control over state and flow. In the course you solve the same task with several frameworks and derive clear selection criteria for your own project.

How do you stop an agent from taking wrong or dangerous actions?

With several layers of protection: narrowly scoped tools with minimal permissions, input and output validation, approval steps for critical actions, sandboxing and continuous evaluation. Prompt injection via external content is treated as a realistic threat. You practise these measures directly on your own agent.

Does every automation need an agent?

No. Many tasks are handled more reliably, cheaply and transparently by a fixed workflow of a few LLM calls. Agents pay off when the solution path cannot be planned in advance and the system must react flexibly to intermediate results. We train exactly this trade-off using practical examples.

Is AI-assisted coding worthwhile for old PHP code without a framework?

That is exactly where it shines. AI assistants explain convoluted procedural logic, write characterisation tests that pin down existing behaviour and then introduce namespaces, autoloading and types step by step. This gives you a safe foundation for modernising legacy applications instead of attempting a risky full rewrite.

How should agencies handle client code and AI?

Agencies should agree with their clients contractually whether, and with which services, source code may be processed. On the course we discuss options such as business licences with no training on your data, exclusion rules for sensitive files and documented review processes that give your clients full transparency.

How much does “AI Agent Development for PHP Developers” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 2 training day(s) with a live trainer, course materials, a lab environment and a certificate of attendance.

Can I suggest my own date?

Yes. Our suggested dates are just a guide. In the request form you can always describe your preferred date or period in free text – we check availability and confirm within one working day.

Is the course online or on-site?

Both are possible: live online in a virtual classroom or on-site in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart or Kaiserslautern. We can also run the course exclusively for your team.

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