Scaling AI Agents Without Breaking Reliability — Preeti Somal, Temporal
Preeti Somal from Temporal explains that as AI agents move to production, they face significant reliability and scalability challenges. She introduces Temporal as a platform to abstract away this complexity, allowing developers to build robust, stateful AI agents by focusing on business logic instead of infrastructure plumbing like retries and error handling.
AI agents are not just simple software; they are complex, stateful, and inherently unreliable distributed systems. Building them for production requires a serious approach to orchestration, failure handling, and scalability—challenges that can distract from the core business logic. Temporal’s mission is to “outsource the reliability and scalability parts of building a complex distributed application seamlessly,” allowing developers to focus on the unique aspects of their agents.
The Challenge with AI Agents
Developing agentic AI applications involves orchestrating multiple processes, managing state over long periods, handling human-in-the-loop interactions, and calling external tools and LLMs. This complexity introduces significant reliability issues. LLM calls are not 100% successful, and the entire toolchain can be unreliable. Debugging and testing these interconnected systems in pre-production is incredibly difficult, leading to a lack of visibility into what is actually happening during execution. If an agent isn’t reliable, users won’t trust it.
The Temporal Solution: Reliability Through Abstraction
Temporal addresses these problems, which are common in distributed systems, by providing a battle-tested platform built around a workflow-as-code abstraction. Instead of developers manually coding for retries, state management, and error handling, they write their agent’s logic as a durable workflow using language-idiomatic SDKs (with Python recently becoming the most used).
Key Temporal concepts include:
- Workflows: The core of the application, a workflow defines the overall flow and orchestration logic as code. It can manage interactive loops, handle inputs via
signals, and be inspected usingqueries. The workflow code is free of “plumbing code”; developers don’t writeif something fails, keep retrying it, as Temporal handles this automatically. - Activities: External interactions, such as calling an LLM, querying a database, or using a tool, are wrapped in
activities. Temporal makes these activity executions durable, managing retries and failures transparently. - Durable Execution History: Temporal records the complete history of every workflow execution. This provides deep visibility for debugging and tracing, showing exactly how an agent navigated a complex series of interactions. This history can also be exported for compliance or offline analysis.
Architecture and Developer Experience
With Temporal, the architecture is simplified. The developer’s code, called a Worker, runs in their own environment and contains the business logic for the workflows and activities. The Temporal Cloud service handles the heavy lifting of orchestration, maintaining the execution state, managing timers, and ensuring durability and scalability. This model allows developers to use their existing CI/CD practices and focus on feature velocity.
This approach has a significant impact on engineering teams:
- Accelerated Development: Customers have reported speeding up feature delivery by over 6x by eliminating the need to build and maintain complex reliability infrastructure.
- Greater Scale: The platform is designed to handle massive scale without requiring developers to write specific scaling logic.
- Proven in Production: Temporal is used for mission-critical applications by companies like Dust, Gorgeous (for production AI customer service agents), and in high-stakes domains like payments, demonstrating its robustness.
Ultimately, by abstracting the complexity of building reliable distributed systems, Temporal enables developers to build and deploy sophisticated, trustworthy AI agents faster and with more confidence.