September 2025 Summaries

The newsletter highlights the upcoming events and updates from Temporal, including the announcement of Replay ‘26 and the open Call for Speakers. It showcases new product features such as the integration of Pydantic AI with Temporal for building durable agents, Task Queue Fairness for better task management, and the Kubernetes Worker Controller. The newsletter also shares success stories from Retool and OpenPhone, who are leveraging Temporal to enhance the durability and reliability of their AI agents. It invites readers to participate in various events, such as San Francisco Tech Week and the Open Source in Finance Forum, where Temporal will demonstrate its capabilities in building resilient, scalable applications. Additionally, it introduces new educational resources like the Worker Versioning Course and the Temporal 101 SDK with Ruby, aiming to help developers and organizations streamline complex systems and drive reliability.

Temporal Cloud offers a secure and efficient solution for orchestrating mission-critical workflows while maintaining high levels of data privacy and security. It prevents data exposure by encrypting sensitive payloads within the user's environment and ensuring no inbound connectivity is required, as workers communicate with Temporal over outbound HTTPS connections. Key features include mutual TLS secured with client certificates from the user's Certificate Authority, role-based access control, and private connectivity options through AWS PrivateLink or Google Cloud Private Service Connect. Temporal's design supports compliance with standards like SOC 2 Type II, HIPAA, and GDPR, and provides operational defense through SSO-gated access, regular audits, and penetration testing. By maintaining data and code within the user's environment and offering secure debugging tools, Temporal ensures that enterprises can run sensitive workflows without compromising speed or security, ultimately reducing risk and facilitating faster, secure deployment of business applications.

Grid Dynamics embarked on a challenging journey to build a production-ready deep research AI agent for a Fortune 500 client using LangGraph, but encountered numerous obstacles, prompting a shift to Temporal for enhanced scalability and efficiency. The initial LangGraph-based architecture faced hidden costs related to development, support, and state management, leading to issues with error handling and resource expenses. The migration to Temporal revolutionized state management, allowing it to become an integral part of the workflow rather than a separate object, which streamlined the process and eliminated the need for custom retry logic. This transition also simplified scaling, as Temporal's architecture enabled effortless horizontal scalability and decoupled the monolithic system into self-contained activities. By leveraging Temporal, the team shifted focus from managing low-level system operations to concentrating on core business logic, ultimately creating a more robust and efficient solution. The experience highlighted the importance of understanding production requirements, scalability, and orchestration in developing stable, production-ready AI applications.

As internet software becomes more complex and autonomous, Temporal Workflows offer a solution to the challenges posed by asynchronous, long-running, multi-process workflows, particularly concerning Workflow Versioning. Worker Versioning, a new feature by Temporal, addresses version compatibility issues by ensuring each Workflow operates on a single version of code, known as Workflow Pinning. However, effective deployment strategies are essential to manage this, with rainbow deploys emerging as a robust solution for high-availability workflows, surpassing traditional rolling and blue-green deployments. Temporal's Worker Versioning allows developers to maintain flexibility and safety in deployments, offering features like instant rollbacks and pre-deployment testing, and supports both open-source and cloud environments. The system is under continuous development, with plans for general availability and additional features by 2025, and a focus on ease of adoption, reliability, and deployment safety.

Individual contributors (ICs) and decision makers (DMs) often perceive problems from different angles, with ICs focusing on daily frictions like latency and complex workflows, while DMs emphasize broader business risks such as security and cost management. The State of Development 2025 survey aims to align these perspectives to improve planning and decision-making. Both groups agree on the need for enhanced reliability, reduced manual effort, and informed tool choices, but their priorities vary, with DMs prioritizing security and cost efficiency and ICs valuing performance and flexibility. The survey suggests that these issues can be addressed by adopting solutions like Durable Execution, which benefits both ICs and DMs by improving reliability and reducing operational burdens without increasing headcount. The report provides a roadmap for improving workflow efficiency and reducing technical debt over the next 12–24 months, highlighting the importance of aligning IC and DM goals to achieve business success.

A recent tech meetup at Essent's offices in Den Bosch brought together experts to discuss the current state and future of agentic AI systems in software engineering. The discussion highlighted the "Wild West" nature of the field, characterized by rapid changes, uncertainty, and a lack of established best practices. Quality assurance for AI systems poses unique challenges, as traditional testing methods struggle with non-deterministic outputs, necessitating new strategies like evaluations and guardrails. Participants agreed on the importance of selectively delegating tasks to AI systems while maintaining control over critical functions through deterministic guardrails. The conversation also emphasized the need for strong foundational software engineering practices to support AI implementation, as many enterprises face infrastructure readiness gaps that hinder effective AI deployment. The transition from "prompt engineering" to "context engineering" was noted as a shift in focus to improve AI outcomes by providing precise context. The economic implications of running LLMs and the strategic importance of laying solid foundations for AI adoption were also discussed, with an urgency for organizations to act promptly to secure competitive advantages.

Building a 24/7 crypto trading platform with multiple AI agents, orchestrated by Temporal and using the Model Context Protocol (MCP) for tool interfaces, proved to be both challenging and enlightening. The system revolves around three core AI agents: a broker agent managing intents, an execution agent for trading decisions, and a judge agent for performance evaluation and strategy updates. Inspired by concepts from AI conferences, the project integrates proactive AI and ambient intelligence, allowing agents to operate continuously without waiting for explicit prompts. Temporal's features, such as Schedules, Signals & Queries, and its UI, provided critical components for the orchestration of these agents, enabling proactive actions, seamless communication, and real-time observability. The use of Temporal allowed for reliable, durable, and extendable operations, including providing audit trails and fault tolerance, which are vital in financial systems. The architecture emphasizes extendability and team-friendly design, allowing the addition of new agents or tools through modular workflows. This innovative approach offers a robust framework for complex AI systems, focusing on high-level agent logic while Temporal manages timing, communication, and system reliability.

A major financial services firm successfully tackled their scaling challenges by transitioning to Temporal Cloud, reducing their payment settlement times from over 45 seconds to sub-seconds during peak periods. According to a Forrester Total Economic Impact™ study, this shift reflects a broader trend in the banking sector, where institutions are replacing fragile, homegrown workflow systems with Temporal Cloud, leading to significant economic benefits, including $14.3 million in preserved revenue over three years and nearly eliminating downtime. The study highlights the accelerated delivery of new features, with development times halved, and a 15% productivity improvement for engineers using Temporal. The transition to Temporal Cloud not only reduced the need for additional infrastructure, saving millions in staffing costs, but also maintained data sovereignty and compliance, making it particularly attractive for regulated environments. The adoption of Temporal has enhanced talent retention by improving the daily work experience, supported transformation initiatives like monolith decomposition, and provided competitive differentiation by allowing banks to deliver features faster. These strategic advantages, along with detailed financial benefits and transparent methodologies outlined in the study, underscore Temporal Cloud's transformative impact on banking operations.

Temporal is rapidly advancing to meet the demands of the evolving AI landscape by enhancing its infrastructure and support for developers. As AI agents transition from experimental to production-ready tools, Temporal focuses on simplifying the creation of reliable, scalable systems that integrate both traditional and AI-driven workflows. The company has intensified efforts to engage with the developer community through updated SDKs, partnerships with organizations like OpenAI, MongoDB, and Pydantic, and collaborations with Grid Dynamics. Their recent acquisition of Crystal DBA, known for its systems and AI expertise, strengthens their mission to ease software development complexity. Temporal's dedication to improving development processes is further reinforced by their commitment to fostering a supportive company culture, led by their new VP of People, Kate Caulfield.