Temporal Workflow Engineering
Durable execution infrastructure for long-running agent workflows, retry logic, and stateful orchestration. We build Temporal systems that survive failures and scale to millions of concurrent executions.
What you get back
- 1. Diagnosis What works, what is blocked, and why.
- 2. Recommendation Audit, advisory, sprint, or pause.
- 3. Scope Next action, boundaries, and timing.
Durable Execution for Agent Systems
We engineer Temporal workflows for AI agent systems that require guaranteed completion, failure recovery, and long-running orchestration — from content pipelines to multi-step approval workflows spanning hours or days.
Typical engagement starts when
- agent workflows fail silently because retry logic and state recovery were bolted on rather than designed in
- long-running processes (approval chains, multi-step generation, external API orchestration) need execution guarantees the current stack cannot provide
- the team is evaluating Temporal vs. LangGraph checkpointing and needs a decision grounded in operational trade-offs
- existing workflow infrastructure (Airflow, Celery, custom queues) is straining under reliability requirements it was never designed for
What We Build
| Capability | What We Deliver |
|---|---|
| Workflow design | Temporal workflow and activity patterns for AI agent orchestration, HITL approvals, and long-running tasks |
| Activity implementation | Idempotent activities with heartbeating, timeout configuration, and retry policies for external API calls |
| Failure handling | Compensation workflows, saga patterns, and dead-letter handling for graceful degradation |
| Observability | Temporal Web UI integration, custom search attributes, and workflow tracing for debugging production executions |
Engineering Standards
- Workflow versioning with deterministic replay: safe deployment of workflow changes without breaking running executions
- Activity heartbeats for long-running operations: detect stuck workers before timeout expiration
- Search attributes for operational queries: filter workflows by customer, status, or business domain in production
- Namespace isolation for multi-tenant deployments: separate workflow execution contexts by environment or team
- Retry policies matched to failure modes: immediate retry for transient errors, exponential backoff for rate limits, no retry for validation failures
When to Use This
| If Your Situation Is | Then We Recommend |
|---|---|
| Agent workflows need guaranteed completion across restarts, deploys, and failures | Temporal workflows with durable execution and automatic retry |
| HITL approval steps span hours or days, not seconds | Temporal signals and queries for human interaction patterns |
| Current retry logic is fragile (lost state, duplicate execution, silent failures) | Temporal activity patterns with idempotency keys and compensation |
| Multi-step workflows coordinate external APIs with varying reliability | Activity-level retry policies and circuit breaker patterns |
| LangGraph checkpointing is sufficient and you do not need cross-service orchestration | LangGraph Engineering — lighter-weight state management |
| Workflow is simple and does not need durable execution guarantees | Direct implementation without orchestration overhead |
Temporal vs. LangGraph Checkpointing
| Aspect | Temporal | LangGraph Checkpointing |
|---|---|---|
| Execution guarantee | Durable across process restarts, deploys, infrastructure failures | Checkpoint persistence to Redis/Postgres; requires manual recovery logic |
| Scope | Cross-service orchestration, external API coordination, saga patterns | Single agent workflow state, tool call sequences |
| Deployment | Temporal Cluster (self-hosted or Temporal Cloud) | Application-level, no additional infrastructure |
| Best for | Long-running workflows (hours/days), multi-service coordination, strict SLAs | Agent state within a single execution context, rapid iteration |
Use Temporal when workflows span multiple services, require compensation logic, or have SLAs that cannot tolerate silent failures. Use LangGraph checkpointing when agent state is the primary concern and cross-service orchestration is minimal.
Common failure patterns we fix
- retry logic implemented per-activity with inconsistent policies, causing unpredictable failure behavior
- workflow state reconstructed from database rather than replayed, breaking Temporal’s determinism guarantees
- heartbeating omitted for long-running activities, causing premature timeouts and duplicate execution
- workflow versioning skipped during deployments, corrupting in-flight workflow state
- search attributes not designed upfront, making production debugging and operational queries impossible
What you leave with
- Temporal workflows deployed with proper versioning, retry policies, and activity patterns
- Operational runbooks for deployment, debugging, and failure recovery
- Search attributes and observability configured for production querying
- Architecture documentation for extending workflows without violating determinism constraints
Best Fit
- Team has long-running workflows that must survive infrastructure failures
- Organization operates multi-step processes spanning external APIs and human approvals
- Engineering team needs execution guarantees beyond “retry and hope”
- Product requires audit trails and replay capability for compliance
Depth of Practice
We operate Temporal workflows for autonomous content engines, multi-step approval pipelines, and cross-service orchestration. Production deployments handle millions of workflow executions with sub-second activity scheduling and zero lost state across infrastructure changes.
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