February 2026 Summaries

A recent live session introduced durable endpoints, a feature that simplifies building robust API endpoints by integrating durability directly into HTTP handlers, eliminating the need for job queues or state management infrastructure. Traditional durable execution models like Inngest, Temporal, and Azure Durable Functions focus on asynchronous background workflows, but many real-time API endpoints fail to fit this model, especially when involving multiple stages of AI processing, leading to reliability issues. Durable endpoints address these challenges by allowing steps within an API handler to be individually retried if they fail, thus maintaining state and avoiding redundant operations, which is particularly cost-effective for expensive processes like LLM calls. During the demo, durable endpoints were used to develop a deep research agent that efficiently handled complex AI workflows in real-time while providing full observability and seamless retry mechanisms. The session's example code is open source and available on GitHub, with upcoming features including streaming support and advanced flow control mechanisms.

The text explores the latency challenges in durable execution systems, particularly focusing on how each step in a workflow can introduce delays due to factors like synchronous state persistence, HTTP-based dispatch, and constraint checking. These issues become critical in modern use cases requiring both speed and reliability, such as AI agents and real-time data pipelines. To mitigate latency, it suggests optimizations like decoupling state persistence from execution, using persistent connections over HTTP, and separating constraint management from the queue. The document emphasizes the importance of checkpointing and a push-based dispatch model to significantly reduce orchestration overhead, offering insights into how Inngest implements these solutions. It advocates for evaluating durable execution platforms based on inter-step latency, constraint handling, and dispatch methods, highlighting the need for these optimizations in user-facing applications.

Inngest has introduced the Constraint API, a new system designed to enhance flow control features such as concurrency, throttling, and rate limiting, by decoupling constraint enforcement from the queue. Previously, flow control was tightly integrated with the queue, leading to challenges in scaling and flexibility, particularly with the introduction of Durable Endpoints. The Constraint API provides a dedicated service to handle capacity management through a system of capacity leases, which automatically expire to reclaim resources if necessary, enabling idempotent and atomic operations without manual intervention. This decoupling simplifies the queue's role, optimizes performance, and allows constraint enforcement to scale independently, providing lower latency, better visibility, and enabling flow control for Durable Endpoints. Looking forward, Inngest plans to migrate its queue infrastructure to FoundationDB, which will support greater scalability and consistency, further enhancing throughput and reliability. This strategic shift, facilitated by the Constraint API, sets the stage for substantial improvements in Inngest's infrastructure capabilities.

Durable execution, a programming model that ensures code completion even in the face of failures, reached the early majority in 2025, driven by AI agent infrastructure needs and new offerings from AWS, Cloudflare, and Vercel. AI agents, which introduce multiple failure points such as orchestration and human-in-the-loop (HITL) interactions, benefit from durable execution's capabilities, including automatic state persistence, retries, and workflow resumption. These features allow agents to become production-ready by handling complex, probabilistic, compositional, and stateful operations seamlessly. Human-in-the-loop patterns, crucial for AI agent oversight, align well with durable execution by enabling workflows to pause and resume without losing state. The reliable execution of tool calls depends on the ability to checkpoint between calls and maintain execution context across failures. As the demand for interactive, user-facing AI agents grows, durable execution is evolving to support low-latency patterns, allowing real-time conversational experiences. This involves innovations such as durable endpoints, optimistic execution, and edge-based execution, which are being integrated into durable execution engines to enhance latency and reliability.

Inngest transitioned from an ad-hoc support system to a structured two-tier support rotation to improve product development and support efficiency. Initially, every engineer was involved in customer support, causing frequent interruptions and context switching that hindered productivity and slowed product development. The company first attempted daily support rotations, which failed due to persistent cognitive load and expertise mismatches. The implemented two-tier system now involves a dedicated support engineer for triage and two rotating engineers for in-depth problem solving, allowing for clearer ownership and focus. This change improved response times, reduced burnout, and facilitated feedback into the product roadmap. While the system is effective at the current scale, Inngest continues to explore ways to balance reactive support with proactive education and ensure long-term scalability. The underlying principle is to structure engineer involvement in support, turning it from a disruption into a source of valuable insights for product improvement.

ETL (Extract, Transform, Load) pipelines are integral to many web applications but often become overly complex and problematic when improperly managed, evolving from simple scripts to intricate orchestration systems. This complexity is exacerbated by the integration of AI features, which introduce non-deterministic transformations that can lead to unpredictable outputs and increased costs due to retries. Developers often face challenges such as tracking job status, handling retries, and implementing idempotency, which can complicate data management and lead to bugs like data duplication. Inngest offers a solution by providing a step-level retry system that simplifies ETL processes by decoupling events and reactions, allowing for independent, event-driven functions that automatically manage retries and provide built-in observability without requiring developers to build extensive infrastructure. This approach enables developers to focus on domain-specific tasks while ensuring reliable and scalable data pipelines, ultimately making ETL infrastructure management a less burdensome task.

Durable Endpoints, introduced by Inngest, represent a paradigm shift in API endpoint design by providing unbreakable, low-latency solutions that integrate durability directly into API handlers. This approach mitigates the traditional trade-off between speed and reliability by allowing steps within the API to be automatically retried, recovered, and observed without requiring background workflows or additional infrastructure. By using Inngest's step API, developers can create durable endpoints that smoothly handle complex AI operations, such as recursive research queries, as well as critical non-AI tasks like payment processing and third-party integrations. This new model enables a more cohesive implementation of tasks that would traditionally require separate systems for responsiveness and reliability, offering a streamlined process that can be set up quickly and without significant architectural changes. The public beta of Durable Endpoints is available, allowing developers to integrate this functionality with minimal setup, enhancing the resilience of applications in areas where failure can be costly.