Dan Farrelly, CTO of Inngest, explains that AI agent architectures rapidly evolve with a “half-life” of about six months, urging developers to decouple the execution, context, and compute layers to avoid technical debt and enable easier updates. He advocates for a durable execution layer that manages state, retries, and orchestration independently, allowing teams to swap models and tools seamlessly while supporting complex, long-running workflows, with Inngest providing such a solution.
Dan Farrelly, CTO and co-founder of Inngest, discusses the rapid evolution of agent architectures in AI systems, emphasizing that these architectures have a “half-life” of about six months. He highlights that many developers find themselves rewriting or significantly modifying their agent systems frequently due to changes in models, frameworks, tools, and patterns. Farrelly encourages developers to reflect on how much of their code from six months ago still functions as originally designed and whether their architecture was intentionally planned or evolved haphazardly.
He introduces a conceptual framework for building agent systems, dividing the architecture into three distinct layers: the execution layer (the “brain”), the context layer (the “knowledge”), and the compute layer (the “hands”). The execution layer handles flow, state, durability, and retries; the context layer manages models, prompts, tools, and memory; and the compute layer involves sandboxes, runtimes, and browsers. Farrelly stresses that these layers have different rates of change, with prompts and models evolving rapidly, while the execution layer can remain stable for years if designed properly.
The core of Farrelly’s argument is that many teams mistakenly couple these layers too tightly, causing the faster-changing components to drag down the stability of the entire system, leading to technical debt. He advocates for decoupling these layers, especially focusing on building a robust and durable execution layer that can manage retries, state persistence, orchestration, and observability independently of the context and compute layers. This separation allows teams to swap out models, prompts, or sandboxes without rewriting the entire system.
Farrelly also discusses the importance of the execution layer in supporting complex, long-running, asynchronous workflows such as background agents and autonomous loops. He explains that these systems require resumability, flexible invocation patterns (like crons, event triggers, and sub-agent delegation), and comprehensive observability to debug and improve performance. Sandboxes, while essential for executing code and tasks, should remain ephemeral and stateless, with the execution layer providing the necessary durability and context.
To conclude, Farrelly presents Inngest as a solution focused on providing a durable execution layer for AI agents, enabling teams to plug in any context or compute components while benefiting from reliable execution primitives, event triggers, scheduling, and full session tracing. He emphasizes that by investing in a well-architected execution layer, teams can better handle the fast pace of change in AI development and build more maintainable, observable, and scalable agent systems.