Agentic AI, in which one or multiple AI agents can pursue goals autonomously or collaboratively, represents a new frontier in generative AI. Unlike traditional large language models (LLMs) that operate independently, these systems combine specialized agents with unique roles to handle complex, open-ended tasks. Here, we’ll explore how Pigment uses this approach for data analysis, empowering clients to gain deeper insights.

Unit tests, integration tests, end-to-end tests — like many modern development teams, at Pigment, we rely heavily on these methods to minimize the risk of functional regressions in our codebase. In this post, we'll explore how we added image snapshots to our Cypress end-to-end tests as an extra safeguard for catching visual regressions.

Our R&D team is hard at work tackling the challenges that come with building a modern business planning platform. Engineering Program Manager Chaimaa E. and Tech Lead Benjamin Hewins, recently spoke with Elliot Kipling and Enginears to share their insights on Pigment's engineering team, culture, and the technical problems they're solving each day.

Fuzz testing consists into executing a given piece of code against randomized inputs. It is a known tool when you want to detect bugs in your algorithms, but we rarely talk of it for performance related topics. Let's see how we can turn fuzzers into tools able to help us into detecting slow code paths.

Pigment is a super fast growing company. Each day, new clients present unique challenges, adding to the platform's workload and pushing us to continually enhance its performance. This article explains how we successfully divided the loading time for certain clients by up to 10 times without touching to the execution time.