Why long-running agents need a durable runtime
A long-running agent repeats tool calls and model inference over minutes to days. When a process restarts, a call times out, or a deploy swaps the worker, the in-flight research loop is lost entirely. A durable execution runtime persists workflow state outside the runtime, so execution resumes from its last point after a failure. At Replay 2026, Temporal announced Serverless Workers, Standalone Activities, and Workflow Streams alongside integrations with the OpenAI Agents SDK and Google ADK, and OpenAI stated that Temporal's durable orchestration is central to large agentic workflows. AWS Lambda Durable Functions (December 2025) and Microsoft Durable Task (April 2026) joined the same pattern.
Checkpoints, replay recovery, and idempotency
Temporal recovers by replaying event history, not by loading a snapshot. Every decision and activity result the workflow produced is written to history; when a worker dies, another worker replays that history to restore the exact state. Workflow code must therefore be deterministic, and any side-effecting step — a model call, a search, a file write — is isolated as an activity so its result is recorded. On replay, activities are not re-executed but reuse their recorded results, so the same LLM call is recovered without double billing. For recovery to be safe, each activity must be idempotent.
Detailed guide: from planning to operations
In planning, define recovery success as numbers. For an auto-research loop, target a single-run completion rate of at least 99%, a post-failure resume success rate of at least 99.5%, zero duplicate LLM calls from replay, and a p95 recovery-resume latency under 30 seconds. Model one research query as one workflow, decompose search, summarization, and verification into activities, and give each activity an explicit timeout (120s for model calls, 30s for external search) and a retry ceiling.
Branch recovery differently by failure type. Transient failures retry with exponential backoff, capped at five attempts. A determinism violation cannot be resolved by retrying, so version the workflow to branch the code path. When the model repeats ungrounded answers or the verification pass rate falls below threshold, escalate to human review; when a budget or time limit is exceeded, safely truncate to a partial result; and when one tool returns the same error three times in a row, circuit-break that branch.
In operations, fix a standard log schema. Record workflow and run IDs, activity name, attempt count, retry reason, and recovery flag as structured fields. To keep user input from flowing into history verbatim, apply PII masking to emails, phone numbers, and identifiers at the activity boundary. Before deploying, verify with a replay test that the code change does not break determinism.
The improvement loop reviews recovery metrics weekly. Aggregate the activities that retry most, the human-review escalation rate, and how often circuit-breaks fire, then feed recurring failures back into timeout, backoff, and idempotency-key design. Any non-zero duplicate-call count signals a side effect leaking outside an activity and is fixed immediately.
Bottom line
Long-running agents need an execution model that persists state outside the runtime. Temporal recovers from the last point via history replay, and using it safely requires deterministic workflows and idempotent activities. Branch failures into retry, human review, safe truncation, and abort, and feed metrics such as a 99% completion rate and zero duplicate calls back weekly, and even a multi-day research loop finishes without incident.