Validated in our current evaluations for durable, approval-aware AI workflow patterns where replayability, retries, auditability, and long-running process control matter. Not a default for simple automations or lightweight prototypes.
Long-running agent and tool-integration workflows: retries with backoff, compensation logic, human approval pauses, signal-driven resumption, and audit trails across multi-day executions.
Self-hosted Temporal cluster (three-node), Postgres persistence, TypeScript SDK, model calls and retrieval calls modelled as activities behind an internal gateway.
- Battle-tested durable execution semantics under real failure conditions
- Excellent per-workflow visibility and replayable history for audit review
- Activities map cleanly onto model calls, tool calls, and external side effects
- Signals and timers support human approval pauses without bespoke infrastructure
- Meaningful learning curve for teams new to durable execution
- Operational responsibility for the cluster (or commitment to managed Temporal)
- Overkill for simple, short-lived automations that a queue or cron would handle
- Determinism constraints in workflow code surprise teams used to ad-hoc scripting
- Worker scaling, task-queue partitioning, and timeout design need intentional work
- Embedding non-deterministic logic inside workflow code and breaking replay
- Underestimating the workflow-design effort for compensation and human-approval paths
- Treating Temporal as a substitute for evaluation and observability of the AI steps themselves
- Adopting it for automations that do not need durability, paying the operational cost for no return
Fits as the durable substrate underneath agent graphs and tool-integration flows: model calls, retrieval calls, and external actions modelled as activities; deterministic orchestration on top; agent frameworks (e.g. LangGraph) run inside activities when the deliberation layer benefits from a graph. Pairs with team-owned tracing and evaluation rather than replacing them.