Effective AI-driven CRM implementations depend on integration with field service, inventory, ERP, and logistics so predictive insights can be executed successfully.
Catch a dangerous agent skill before an agent ever runs it: review it automatically, block it in CI if it fails, and only let your agent load skills that passed.
Most teams log, but log badly: wrong severity levels, no trace IDs, inconsistent fields, and logs siloed from traces. Fix that, and incidents go from hours to minutes.
A tiny, type-safe bridge for mounting React microfrontends across Module Federation boundaries — without repetitive lifecycle wrappers, shared stores, or code generation.
GraphQL was good at a time, then it simmered off. Is GraphQL about to make a comeback because of AI? Will GraphQL be able to serve better for AI Agents?
In this article, I will be introducing a pipeline designed to identify sensitive data columns before masking steps and improve the efficiency of the data masking process.
We model a supply chain in Neo4j using Apache Spark to load data, NetworkX to identify critical nodes, and Cypher to find alternative routes after a disruption.