How to Build a Durable AI Agent with Inngest

Building an AI agent using Inngest offers a robust solution to overcoming common production challenges such as tool call failures, rate limits from LLM providers, and task timeouts. The process involves creating a loop that enables the AI agent to think, act, and observe by using three key primitives: step.run() for durable task execution, step.invoke() for synchronous delegation to sub-agents, and step.sendEvent() for asynchronous task delegation. These primitives ensure that each step in the agent's workflow is tracked, retryable, and resumable, allowing the function to continue from the last completed step after a crash, thus maintaining durability. The agent loop, which is a returned function for reusability across different functions, handles tasks such as loading context, managing user interactions, and executing tools. Inngest's dashboard provides detailed observability of each function run, offering insights into step sequences, inputs, outputs, and performance metrics. This setup not only facilitates debugging and error handling but also allows for the delegation of long-running tasks to sub-agents, enhancing the flexibility and scalability of AI-driven workflows.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.