January 2026 Summaries

The January newsletter from Temporal highlights new integrations, tutorials, and community projects, with a focus on enhancing AI application development and observability. The newsletter introduces the AI SDK by Vercel for TypeScript developers, enabling durable state and long-running workflows without altering code, and Braintrust integration for improved AI workflow visibility. It also announces the general availability of an API and UI for viewing audit logs in Temporal Cloud and restructuring of Temporal Docker images for enhanced security. The builder spotlight features projects like deploying Temporal on AWS ECS, a terminal UI for workflow management, and a multi-agent system for catastrophe data extraction. New tutorials demonstrate adding durability and reliability to MCP servers, while community questions address workflow task management and scaling practices. Upcoming events, including the Replay ‘26 conference with industry leaders, offer opportunities for live interaction and learning.

Temporal, a Durable Execution platform that aids developers in building reliable applications by managing error handling and state management, has integrated with ClickStack to enhance observability. ClickStack is a cloud-native observability stack leveraging ClickHouse for efficient storage and querying of logs, metrics, and traces, ideal for handling large volumes of telemetry data. This integration uses OpenTelemetry to scrape metrics from Temporal Cloud, enabling teams to navigate and analyze high-cardinality data generated by Temporal workflows quickly. By combining the strengths of ClickStack and Temporal, users can achieve unified observability, allowing them to track system health, optimize performance, and visualize key metrics through pre-built dashboards. This collaboration ensures that durable workflows maintain scalability and reliability, with the integration providing a comprehensive view of a system’s health and performance.

AI is increasingly becoming a powerful tool in software development, but its success hinges on human guidance, validation, and accountability as it often generates unstable code that requires intervention. This dynamic suggests that humans are not being replaced but are evolving from code writers to strategic roles such as product architects and customer outcome engineers, paralleling how accountants transitioned from calculators to advisors with the advent of Excel. As AI democratizes software creation, it allows for rapid and personalized development, lowering the barriers to production and enabling companies to build custom solutions. This shift requires a new infrastructure to support AI capabilities, focusing on high-level problem-solving while AI handles execution. Temporal emerges as a critical infrastructure, providing orchestration and resilient communication essential for managing complex AI systems at scale. It integrates seamlessly with existing tools, enhancing observability and control, and represents a vital shift in building reliable AI systems. As we approach this new industrial era, developers who adapt by focusing on reliability and innovation will thrive, supported by infrastructures like Temporal that transform AI experiments into reliable production systems.

Sophia Barnes, a Computer Science Master's student at Stanford University, developed a multi-agent AI system to automate and streamline the process of converting unstructured Excel submission pack data into structured catastrophe records in the reinsurance industry. The system employs multiple specialized agents to handle tasks such as extracting catastrophe loss data, matching events to historical records, populating cedant loss data, and comparing it to existing data. The architecture leverages a "human-in-the-loop" approach to ensure accuracy by allowing user intervention and confirmation at crucial steps, which provides flexibility and adaptability to varying submission pack formats. This modular design not only prevents agents from being overwhelmed by excessive information but also facilitates reliable execution through Temporal's Durable Execution, Workflows, and Activities, ensuring that human oversight maintains control over the AI's decisions. The project demonstrates how integrating human input within multi-agent systems can enhance reliability and efficiency, with potential applications beyond the reinsurance sector.

AI agents are increasingly popular for managing complex tasks involving multiple models and external APIs, but their sophistication brings challenges like durability and observability. To address these issues, a new integration between Braintrust and Temporal has been introduced, combining Temporal's Durable Execution capabilities with Braintrust's AI observability tools. This integration ensures that AI agents can handle failures without losing progress and provides visibility into their operations, allowing for better debugging and prompt management. Temporal allows for automatic retries and state persistence, crucial for maintaining workflow integrity during infrastructure failures, while Braintrust offers insights into model performance and prompt effectiveness, helping teams iterate and improve AI outputs. Notable users of these platforms include companies like OpenAI and Stripe, which leverage the integration to enhance the reliability and efficiency of their AI-driven applications.

TypeScript developers using Vercel's AI SDK can enhance their AI applications by integrating with Temporal to add durability and reliability to long-running workflows. The integration allows AI agents to handle issues such as rate limits and network hiccups by automatically managing retries and maintaining state, ensuring that processes can resume seamlessly after interruptions. Temporal's plugin architecture enables developers to extend their familiar AI SDK environment without adding code complexity, as it automatically wraps LLM calls in durable Temporal Activities. This setup offers benefits like automatic retries, state persistence, and full visibility into each workflow step through the Temporal UI, all while maintaining cost efficiency by avoiding repeated work. Developers can leverage these capabilities for complex tasks, such as building a daily news briefing agent that searches for news, summarizes findings, and generates reports, with enhanced durability and monitoring through Temporal's infrastructure.

Goldcast, a B2B event marketing platform, has transformed its operational infrastructure by integrating Temporal Workflows to enhance the reliability and scalability of its email delivery and event duplication processes. Initially an experimental addition, Temporal quickly became central to Goldcast's ability to manage the complexities of sending hundreds of thousands of emails and orchestrating event duplications with rollback-safe and asynchronous phases. This integration has resulted in significant improvements, such as a 99.9% success rate for large email sends and a 99.8% success rate for event duplication, while reducing manual intervention and operational stress. The platform's adoption of Temporal has also streamlined workflows, replacing fragile, bespoke logic with robust, observable processes, allowing the engineering team to focus more on building rather than firefighting. Goldcast plans to further leverage Temporal for additional services, contributing back to the open-source community with their developed patterns and utilities, thus ensuring a dependable foundation for their operations even during peak stress periods.

Companies like Temporal are recognizing the growing importance of AI coding assistants in the developer experience, given the significant adoption rates where 85% of developers use AI tools, and 62% rely on a coding assistant. This shift necessitates a new focus on optimizing AI agents rather than traditional human-centric developer experiences. Temporal is responding by hiring a product manager specifically to enhance AI coding assistants' effectiveness on their platform, emphasizing proficiency with AI tools, rigorous analytics, and fast iteration over traditional product management skills. This role involves creating benchmarks and assets to improve AI-driven development and addressing developer challenges to improve performance. The broader implication is that developer-focused companies will increasingly need roles like this to optimize the AI-assisted development experience, as improvements made for one AI tool can benefit all users, highlighting the substantial leverage and impact of such roles.

Building AI features that perform flawlessly in demos but falter in production is a common challenge due to unexpected failures such as network stalls or process restarts. This experience mirrors Campbell’s Hero’s Journey, where developers venture from the "ordinary world" of demos into the unpredictable realm of production. The key to overcoming these challenges lies in creating systems that gracefully handle failures, a concept referred to as "durability." Temporal, a tool designed for such scenarios, enables developers to write long-running, multi-step logic as Workflows that can endure restarts, while Activities manage the external work, allowing for retries and clean error handling. The transition from unreliable to durable systems involves understanding failure points, orchestrating workflows rather than relying on ad-hoc solutions, and utilizing tools like Temporal to ensure that processes can resume without starting over, even if failures occur. This shift in approach transforms initial prototypes into robust systems, allowing developers to anticipate and manage failures effectively, ultimately leading to more predictable and reliable AI-powered applications.