Durable Execution: Temporal Patterns for AI Agents

What Durable Execution Solves

The core problem with long-running agent tasks is that process lifetime is shorter than task lifetime. A container may be evicted after 15 minutes. A serverless function times out after 5 minutes. A deployment restarts all processes. Rate limit backoff pauses execution for minutes. Without durable execution, any of these events terminates the agent and loses all progress.

Durable execution separates the workflow definition (what the agent does) from the workflow execution (which process runs it). The workflow is persisted in the framework's database, not in the process memory. If the process dies, the framework assigns the workflow to a new process that picks up where the old one left off. The agent developer writes sequential code as if the process will never die, and the framework handles the durability.

This is the same pattern that database transaction logs, message queues, and orchestration systems have used for decades. Durable execution frameworks package it into a developer-friendly API that looks like normal function calls but has persistence built in.

How It Works with AI Agents

An AI agent workflow in Temporal looks like a sequence of activities (steps). Each activity is a function call: query an API, call an LLM, read a file, write a result. Temporal persists the input and output of each activity. If the worker dies between activity 7 and activity 8, Temporal restarts the workflow on a new worker, replays activities 1 through 7 using cached outputs (without re-executing them), and continues with activity 8.

For LLM-based agents, the activities map to the agent loop: plan (LLM call), execute tool (API or function call), evaluate result (LLM call), decide next step (LLM call). Each of these is a separate Temporal activity with persisted output. The agent is guaranteed to complete all steps even if the worker process is replaced multiple times during execution.

The important nuance is that LLM calls are non-deterministic: the same input may produce different outputs. Temporal handles this correctly because it persists the actual output of each activity. When replaying after a restart, it uses the persisted LLM output rather than calling the LLM again. This means the agent's reasoning path is preserved exactly, even across restarts.

Temporal Workflow Pattern for Agents

A typical agent workflow in Temporal has four components: the workflow definition (the high-level task logic), the activities (individual steps), the worker (the process that runs the workflow), and the client (what starts the workflow).

The workflow defines the agent loop: retrieve memory, generate a plan, execute each plan step, store findings, return results. Each step is an activity call that Temporal persists. The workflow can run for minutes, hours, or days, with automatic handling of worker failures, timeouts, and retries.

Activities handle the actual work: calling LLMs, executing tools, reading and writing memory. Each activity has retry policies (how many times to retry on failure), timeout settings, and heartbeat intervals (for long-running activities that need to report progress). LLM calls should have retry policies that handle rate limits and transient errors gracefully.

Alternatives to Temporal for AI Agents

Temporal is the most mature durable execution framework, but several alternatives target similar problems with different trade-offs. Inngest is a serverless-native event-driven workflow engine that runs as a managed service. It is the easiest durable execution option to adopt because there is no infrastructure to deploy: you define step functions in your application code, deploy to your existing hosting (Vercel, AWS Lambda, any Node.js server), and Inngest handles persistence, retries, and scheduling via webhooks. For teams building agents on serverless platforms, Inngest eliminates the Temporal server dependency entirely. The limitation is that Inngest is a managed service, so teams that need on-premise deployment or full infrastructure control need to look elsewhere.

Restate is an open-source durable execution engine that embeds persistence into the RPC layer rather than requiring a workflow definition language. Functions call each other normally, and Restate transparently journals every call so execution can resume from any point after a failure. This is the most ergonomic option for developers who find Temporal's workflow and activity separation cumbersome, because Restate preserves the appearance of normal function calls while adding the durability guarantees underneath. The project is newer than Temporal and has a smaller ecosystem, but its developer experience is strong.

Hatchet is designed specifically for AI workloads, including LLM orchestration, agent loops, and data pipelines. It provides durable execution with built-in concurrency control, rate limiting, and queue management tuned for the bursty, high-latency pattern of LLM API calls. For teams building agents and looking for a framework that understands AI workload patterns natively rather than adapting a general-purpose workflow engine, Hatchet is worth evaluating. It is open-source with a managed cloud option.

Production Configuration for AI Agent Workflows

Getting durable execution right for AI agents requires attention to configuration details that differ from typical workflow automation. LLM API calls are slow (1 to 30 seconds), expensive ($0.001 to $0.10 per call), and subject to rate limits and transient failures. The activity retry policy needs to account for all three: use exponential backoff with a maximum of 3 to 5 retries for transient failures, set heartbeat timeouts long enough to accommodate slow model responses (60 to 120 seconds for complex reasoning), and configure a schedule-to-close timeout that covers the maximum reasonable duration for the entire workflow.

Idempotency is critical for LLM activities because retries must not produce side effects. If an activity sends an email based on an LLM-generated response, and the activity is retried because the worker died after the email was sent but before the result was persisted, the email will be sent again. Design activities so that retries are safe: check whether the side effect has already occurred before executing it, or structure the workflow so side-effecting operations are in their own activities with idempotency keys.

Workflow history size is a practical concern for long-running agent loops. Temporal stores the full event history of each workflow, and an agent that runs thousands of activities (one per LLM call in a long research task) can produce a history that impacts performance. The standard mitigation is continue-as-new: when the history grows past a threshold (typically 10,000 events), the workflow completes and starts a new workflow execution that inherits the state. This resets the history while preserving the logical continuity of the agent's task.

Limitations and Trade-offs

Durable execution adds infrastructure complexity. Temporal requires a server cluster (or Temporal Cloud) to manage workflow state. The workflow definition must follow Temporal's determinism constraints: no random numbers, no time-dependent logic, no direct I/O in the workflow function. All side effects must be in activities. This is a natural fit for well-structured agent loops but requires refactoring for agents that interleave reasoning and tool use in a single function call.

The replay mechanism has latency implications. When a workflow restarts, replaying 50 completed activities takes a few hundred milliseconds (just reading cached outputs), which is fast. But the restart itself (assigning the workflow to a new worker, loading the workflow definition, beginning replay) can take 1 to 5 seconds. For agents where every second matters (real-time customer interactions), this latency may be unacceptable. For background tasks (research, analysis, monitoring), it is negligible.

Durable execution handles execution persistence but not knowledge persistence. It ensures that the agent's task completes, but it does not make the agent smarter over time. A Temporal-based agent that restarts from a checkpoint continues its current task perfectly but does not benefit from knowledge accumulated in previous tasks unless it has a separate long-term memory system. This is the fundamental distinction between workflow durability (keeping the current task alive) and agent memory (learning across tasks), and production agents need both.

Cost is the final trade-off. Temporal Cloud pricing is based on actions (roughly one action per activity invocation), and an agent that runs 50 LLM calls per task at 1,000 tasks per day generates 50,000 actions per day. At Temporal Cloud's pricing, this is manageable for most teams, but it adds to the already significant LLM API costs. Self-hosted Temporal eliminates the per-action cost but requires maintaining the Temporal server cluster, which is operational overhead that smaller teams may prefer to avoid.

Combining Durable Execution with Memory

The most robust architecture combines durable execution for task reliability with persistent memory for knowledge accumulation. Temporal ensures the agent completes its current task even through interruptions. A memory API (like Adaptive Recall) ensures the agent benefits from what it learned in all previous tasks.

In this architecture, the agent's workflow includes memory operations as Temporal activities: retrieve relevant memories at the start of the task, store findings after each significant discovery, and store task outcomes when the task completes. These memory operations are themselves durable (Temporal ensures they execute) and persistent (the memory API stores them across sessions). The result is an agent that is both reliable (never loses work) and intelligent (accumulates knowledge over time).