To make the most of Large Language Models (LLMs) like ChatGPT and Gemini, we need to ensure that they have access to the most up-to-date information and can interact with external systems, transforming them from simple text-generation engines into powerful and flexible AI Agents.

Implementing Temporal Cloud can present challenges for organizations offering cloud services that may be used by the federal government. One of the largest buyers of cloud technology, the federal government requires cloud services to be certified by the Federal Risk and Authorization Management Program (FedRAMP®). Each Cloud Service Offering (CSO) must have an independent authorization.

Are you tired of relying on Directed Acyclic Graphs (DAGs) for your workflow orchestration? Consider transitioning from Apache Airflow to Temporal. With a focus on scalability and fault tolerance, Temporal is the perfect solution for complex workflows. Migrating from Airflow to Temporal can improve error handling and even solve looming tech debt.

Deploying an updated version of Temporal workflow code can result in errors if there are non-deterministic changes to the code. Determinism is verified during the “replay“ process that rebuilds the last known state of an ongoing workflow in order to continue its execution. Rebuilding execution state enables Temporal to support long-sleeping workflows and reliably relocate workflow executions to another worker when one crashes.

Using cloud services is standard practice for most backend application architectures. When using cloud services, it is important to understand and control what data is leaving your network and being sent to the cloud. Temporal Cloud has great options available to ensure that data sent to and from the cloud is securely encrypted. This post will showcase how Temporal Cloud might interact with your infrastructure by default and how you can customize Temporal to prevent any user or business-related data from being sent to the cloud.