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 happens after you submit specs
1. Context
We inspect the system, constraints, and where delivery or architecture risk is most likely to surface.
2. Recommendation
You get a direct recommendation: audit, advisory track, scoped build, or a clear signal that the work is not ready yet.
3. Next Step
If there is a fit, we define the shortest path to a useful engagement and a production-ready outcome.
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.
Related articles
When Your AI Pipeline Needs Temporal and When It Does Not: The Complexity Threshold
A decision framework for choosing Temporal over cron, Celery, or Airflow — based on durability requirements, not hype.
AI EngineeringTemporal Observability for AI Workflows: What to Instrument Beyond Workflow Status
Temporal workflow status tells you if a workflow completed. It does not tell you if it produced correct results, stayed within cost budget, or met latency SLAs.
AI EngineeringBuilding Durable RAG Pipelines with Temporal: Ingestion, Embedding, and Index Management
How to use Temporal workflows to build fault-tolerant RAG ingestion pipelines with reliable embedding, partial-update handling, and index consistency.
Discuss your Temporal Workflow Engineering path
Submit system context, constraints, and delivery pressure. A Principal Engineer reviews every submission and recommends the right next step.
1. Context
We review the system, constraints, and where risk is most likely to surface.
2. Recommendation
You get a direct recommendation: audit, advisory, sprint, or pause.
3. Next Step
If there is a fit, we define the shortest useful engagement.
No SDRs. A Principal Engineer reviews every submission.