Implementing robust security practices is essential for protecting your Laravel application, safeguarding sensitive information, and maintaining user trust. Developers need to review application security regularly as frameworks, dependencies, and potential attack methods evolve. At Mallow, we combine technical expertise with an understanding of the challenges involved in developing and maintaining Laravel applications. This article covers […]
The first production incident in a multi-agent system almost always goes the same way. Something in the output is wrong. Someone files a bug. An engineer opens the logs. The logs show that every individual agent reported success. The orchestrator reported success. The output is still wrong. The engineer spends four hours in traces before […]
The internal RAG demo always looks impressive. The team asks questions. The system retrieves the right documents. The answers are clear, well-structured, and grounded in actual product content. Someone says “we’re ready to ship.” Then the first real users arrive. Real users do not ask the questions you rehearsed. They do not know what the […]
Most teams start a RAG project by evaluating vector databases. They compare embedding models, read benchmarks, set up a LangChain prototype, and have something running against a handful of test documents within a week. That week goes well. The problems arrive three months later, after the index has grown to thousands of documents, after real […]
There is a version of RAG failure that nobody in the team notices in time. The system doesn’t crash. The latency looks fine. The monitoring dashboards stay green. A user asks a question. The retriever pulls something that looks relevant. The model produces a clean, well-structured answer in the product’s tone. The answer is wrong. […]
Every product team building an AI agent that takes real action eventually asks the same question – is this agent trustworthy enough to act on a user’s behalf without a human checking every step? The instinct is to answer that question with reassurance. Add a confirmation dialog. Add a disclaimer. Add more hedging language to […]
You shipped the chatbot to take pressure off your support team and give users instant answers. The dashboards looked healthy at first. Sessions were happening. Then you looked closer at what those sessions actually contained, and the picture changed. Most people open the chat, type one or two messages, and leave. Seven out of ten […]
You are two weeks from shipping the AI feature. The model is trained. The endpoint is deployed. The product manager has written the release notes. And then someone asks: “has anyone actually checked whether the rollback plan is documented?” That question, asked two weeks before launch, is manageable. Asked two days after launch when the model starts producing […]
You ran the assessment. You sat down with your team, worked through the questions honestly, and found something you did not expect to find. Maybe it was the data, years of records with no labelling, no outcome signal, nothing a model can learn from. Maybe it was the use case, three people in the room […]
The conversation in your last board meeting probably included the phrase “we need to be doing more with AI.” Your investors are asking about it. Your competitors are announcing it. And somewhere in the middle of all that, you are trying to figure out whether your organisation is actually in a position to build something that works. The honest […]