This case study explores how Temporal provides durable execution infrastructure for building reliable, long-running AI agents in production environments. The problem addressed is that traditional approaches to building production systems—whether through manual retry logic, event-driven architectures, or checkpoint-based solutions—require significant engineering effort to handle failures common in cloud environments and agentic workflows. Temporal solves this through a deterministic execution model that separates business logic from reliability concerns, allowing developers to write regular code in their preferred language while automatically handling crashes, retries, and state management. The solution has been adopted by companies like OpenAI (Codex on the web), Replit, and Lovable, with integrations across major AI frameworks including OpenAI Agents SDK, Pydantic AI, Vercel AI SDK, BrainTrust, and LangFuse, enabling developers to build production-grade agentic systems with significantly reduced complexity.
Temporal provides durable execution infrastructure specifically designed to address the reliability and operational challenges of running LLM-based agents and agentic systems in production. The platform is being adopted by major AI companies including OpenAI for Codex on the web, Replit, and Lovable, demonstrating its applicability to real-world production scenarios where AI agents need to operate reliably at scale.
The fundamental challenge Temporal addresses is that modern agentic systems often need to run for extended periods, interact with unreliable external services, handle failures gracefully, and maintain state across restarts—all while remaining easy for developers to build and maintain. Traditional approaches to solving these problems include writing extensive retry logic, implementing event-driven architectures with message queues and dead-letter queues, or using checkpoint-based solutions that require developers to manually serialize and restore program state.
Temporal’s approach is to provide a programming abstraction that makes software “crash-proof” through deterministic execution. The key insight is separating reliability concerns from business logic: developers write code in their preferred programming language using normal constructs, while Temporal handles all the complexity of distributed systems, failures, and state management. This is achieved through a programming model consisting of workflows (deterministic code that defines control flow) and activities (arbitrary code that performs I/O operations like calling LLMs or external APIs).
The system consists of three core components. The Temporal server maintains durable state backed by databases like Cassandra or PostgreSQL, or runs as Temporal Cloud with multi-region replication. Workers are deployed in the user’s environment (VPC, Kubernetes, or containers) where actual code execution happens. Clients initiate workflows and can interact with running workflows through signals, updates, and queries.
The deterministic execution model works by ensuring workflow code, given the same inputs, always produces the same outputs. This allows Temporal to replay workflow execution from history when failures occur. Activities, which perform non-deterministic operations like LLM calls or API requests, have their results captured and stored durably in the Temporal server. If a worker crashes mid-execution, Temporal can spin up computation elsewhere and replay the workflow using the saved activity results, continuing exactly where it left off.
A critical feature for production AI systems is that all data sent to Temporal server can be encrypted before transmission, with encryption keys held only by the customer. This means the Temporal server tracks state and orchestrates execution without ever seeing actual application data, addressing data privacy and security concerns essential for enterprise AI deployments.
Temporal has developed integrations with major AI agent frameworks specifically to make it easy to add durability and reliability to agentic systems. Key integrations include OpenAI Agents SDK, Pydantic AI, Vercel AI SDK, BrainTrust for observability, and LangFuse for monitoring. The strategy is to give users choice in their AI stack while Temporal handles the reliability layer.
A common misconception addressed in the discussion is whether LLMs’ non-deterministic nature conflicts with Temporal’s deterministic execution model. The clarification is that the agentic loop itself—the code that calls the LLM, processes responses, and decides which tools to call—is deterministic. The non-deterministic LLM outputs are treated as activity results that get captured and stored, making the overall execution reproducible even though individual LLM calls may vary.
For practical implementation, developers can use a “Temporal skill” that ensures LLM coding agents correctly understand how to build applications with Temporal. This reflects the emerging pattern of using AI assistants to learn and implement new frameworks, where the challenge becomes knowing what features to ask for rather than writing boilerplate code manually.
Temporal provides several capabilities critical for production LLMOps scenarios. Long-running workflows are a major use case, where agents may need to run for days, weeks, or indefinitely—monitoring systems, processing incoming data on schedules, or managing ongoing business processes. The system automatically handles sleep/wake cycles, scaling resources to zero when agents are idle and rehydrating state when they need to resume execution.
Human-in-the-loop workflows are supported natively, where an agent can pause to wait for human approval, document signatures, or other external inputs. During these pauses, compute resources are reclaimed, and the workflow can resume from the exact point it stopped when the external input arrives. Timeouts and reminders can be set using simple timer primitives, and workflows can be interrupted or modified through signals and updates even while running.
The development experience includes handling failures gracefully during development. If code crashes due to a bug—even hours into execution—developers can fix the error, restart the worker, and Temporal will continue execution from where it failed using the new code version. This dramatically reduces development cycle time compared to traditional approaches where failures mean restarting entire processes from scratch.
Worker versioning enables sophisticated deployment strategies like blue-green deployments, allowing gradual rollout of new agent behaviors without disrupting running workflows. This is particularly valuable when updating agent logic or tool implementations, as existing long-running workflows can continue on the old version while new workflows start on the new version.
Workflow pause provides the ability to stop execution at specific points for inspection before allowing progress, useful for debugging or adding approval gates. Streaming capabilities are being enhanced as first-class features, important for agentic applications that need to provide real-time updates during tool execution or stream responses back to users.
Large payload storage addresses scenarios common in AI workflows where agents process media files or large documents. Instead of passing these through Temporal server, the system can store them in the customer’s blob storage (S3, etc.) while maintaining metadata and tracking in Temporal, optimizing for both performance and cost.
The temporal UI provides observability into running workflows, showing workflow execution, activity calls with arguments and return values, retry attempts, and failures. This visibility is essential for debugging and monitoring production AI agents, allowing operators to understand what agents are doing and diagnose issues when they occur.
The discussion provides valuable context on how Temporal differs from other solutions. Compared to Airflow, which is domain-specific for ETL and data movement, Temporal is a general-purpose programming model suitable for any long-running process. While DAG executors can be built on top of Temporal, the programming model allows expressing more complex logic than static DAGs, particularly important for agentic systems where control flow depends on LLM decisions.
Checkpoint-based solutions, increasingly common in agent frameworks, are characterized as coarse-grained. Checkpoints typically require developers to write serialization logic and may only capture state at specific points in code. Temporal’s approach is more fine-grained, incrementally capturing state changes as they occur through activity results, providing better recovery points without developer-written checkpoint logic.
Compared to event-driven architectures with message queues, which can technically achieve reliability but require significant complexity in handling dead-letter queues, compensation logic, and ensuring idempotent operations, Temporal abstracts away this complexity. Developers ensure activities are idempotent (typically through unique keys in databases), but don’t need to manually manage queues and retry logic.
The discussion touches on emerging patterns around agent memory. File systems have become the default memory repository for many agents, particularly coding agents that generate and manipulate files. However, Temporal’s state management can serve as agent memory for many use cases, particularly episodic memory related to specific sessions or tasks. State is isolated by default to individual workflow instances, providing natural separation between different agent sessions.
Cross-session memory and team collaboration patterns are acknowledged as areas of active exploration across the ecosystem. The parallels with database access control and permissions models are noted—concepts like hierarchical permissions, delegation between agents, and data lineage tracking for security (e.g., detecting prompt injection) are areas where the industry is still developing best practices.
The philosophical connection between durable execution and serverless is explored in depth. Both concepts fundamentally address the promise that developers should be able to write code and have it “just work” without worrying about infrastructure complexity. Temporal’s model aligns with serverless principles: scale to zero when idle, scale up when needed, and abstract away server management.
Workers can be deployed on Kubernetes with autoscaling based on signals from Temporal server about pending work. The trajectory points toward more serverless-like experiences where compute automatically scales without manual configuration. The emphasis is that compute is ephemeral and fungible—what matters is program state, which Temporal makes durable.
Multi-region reliability is demonstrated by the capability to move running workflows between cloud regions during outages. When a region fails, workflows can be transferred to another region and continue execution from the last saved state, a level of reliability that traditional databases don’t provide even for running queries.
Platform engineering teams commonly adopt Temporal as foundational infrastructure, recognizing it addresses both reliability requirements and developer productivity goals. By providing Temporal as shared infrastructure, platform teams enable application developers across the organization to build faster with standardized reliability patterns.
The ability to use regular programming languages and constructs rather than domain-specific languages is highlighted as a key advantage. Developers can work in Python, TypeScript, or other languages they already know, with type annotations and familiar patterns, rather than learning database-specific procedural languages or workflow-specific DSLs.
For developers new to Temporal, the recommendation is to either run the Temporal CLI locally or start with Temporal Cloud, then use coding agents with the Temporal skill to generate initial projects. The skill ensures consistency and helps LLMs understand Temporal’s patterns correctly. The documentation and tutorials are comprehensive, but the emerging pattern is to use AI assistants to learn the framework, building mental models through conversation before diving into code.
The key mental model to understand involves distinguishing between workflows (restricted, deterministic code) and activities (arbitrary code with I/O), and understanding the three-component architecture of server, workers, and clients. A simple heuristic for structuring code is: if it does I/O, it should be an activity; otherwise, it can be workflow code. Sample applications and an AI cookbook provide starting points for common patterns.
While the case study presents Temporal as solving fundamental problems in production AI systems, it’s important to note this is essentially a technical explanation and product positioning rather than an independent evaluation. The discussion doesn’t deeply explore potential drawbacks such as operational complexity of running the infrastructure, learning curve for the programming model, or performance overhead of the execution model.
The claimed benefits around developer productivity and reliability need to be evaluated in context. While abstracting away retry logic and state management is valuable, developers must still understand Temporal’s programming model and restrictions (particularly around determinism in workflows). The assertion that “coding agents are pretty good” at generating Temporal code should be viewed with some skepticism until validated more broadly.
The integrations with AI frameworks are relatively new (many released recently), so production experience at scale across diverse use cases is still being accumulated. The checkpoint comparison somewhat simplifies the tradeoffs—checkpoint-based systems may be simpler for certain use cases despite being “coarse-grained.”
That said, the fundamental technical approach is sound: separating reliability concerns from business logic through deterministic execution is a well-established pattern, and applying it to AI agents addresses real problems. The adoption by sophisticated technical organizations like OpenAI, Replit, and Lovable suggests the approach has merit for production agentic systems. The open-source nature (MIT license) and ability to self-host on Cassandra or PostgreSQL reduces vendor lock-in concerns compared to proprietary solutions.
Snorkel developed a specialized benchmark dataset for evaluating AI agents in insurance underwriting, leveraging their expert network of Chartered Property and Casualty Underwriters (CPCUs). The benchmark simulates an AI copilot that assists junior underwriters by reasoning over proprietary knowledge, using multiple tools including databases and underwriting guidelines, and engaging in multi-turn conversations. The evaluation revealed significant performance variations across frontier models (single digits to ~80% accuracy), with notable error modes including tool use failures (36% of conversations) and hallucinations from pretrained domain knowledge, particularly from OpenAI models which hallucinated non-existent insurance products 15-45% of the time.
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This podcast discussion between Galileo and Crew AI leadership explores the challenges and solutions for deploying AI agents in production environments at enterprise scale. The conversation covers the technical complexities of multi-agent systems, the need for robust evaluation and observability frameworks, and the emergence of new LLMOps practices specifically designed for non-deterministic agent workflows. Key topics include authentication protocols, custom evaluation metrics, governance frameworks for regulated industries, and the democratization of agent development through no-code platforms.
