Temporal Workflow Engineering
Durable execution infrastructure for long-running agent workflows, retry logic, and stateful orchestration. We build Temporal systems that survive common failure modes and make recovery behavior explicit.
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 durable execution, failure recovery, and long-running orchestration: from content pipelines to multi-step approval workflows spanning hours or days.
Typical engagement starts when
| Signal | Why Temporal Fits |
|---|---|
| Agent workflows fail silently | Retry logic and state recovery were bolted on instead of designed in |
| Approval chains, generation flows, or external API orchestration run for a long time | The workflow needs durable execution beyond a single request lifecycle |
| Team is evaluating Temporal vs. LangGraph checkpointing | The decision needs operational trade-offs, not framework preference |
| Existing workflow infrastructure is straining | The reliability requirement may exceed what Airflow, Celery, or custom queues were meant to carry |
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
| Standard | What It Protects |
|---|---|
| Workflow versioning with deterministic replay | Running executions are protected during workflow changes |
| Activity heartbeats for long-running operations | Stuck workers are visible before timeout behavior surprises the team |
| Search attributes for operational queries | Operators can filter workflows by customer, status, or business domain |
| Namespace isolation | Execution contexts stay separated by environment, team, or tenant boundary |
| Retry policies matched to failure modes | Transient errors, rate limits, and validation failures get different handling |
When to Use This
| If Your Situation Is | Then We Recommend |
|---|---|
| Agent workflows need durable recovery across restarts, deploys, and failures | Temporal workflows with durable execution and explicit retry behavior |
| 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 recovery behavior | Direct implementation without orchestration overhead |
Temporal vs. LangGraph Checkpointing
| Aspect | Temporal | LangGraph Checkpointing |
|---|---|---|
| Durability model | Durable across process restarts, deploys, and infrastructure failures | Checkpoint persistence to Redis/Postgres; recovery logic remains application-owned |
| 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 reliability expectations 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
| Pattern | Engineering Fix |
|---|---|
| Retry logic implemented per activity with inconsistent policies | Define retry behavior by failure mode |
| Workflow state reconstructed from a database rather than replayed | Preserve deterministic workflow behavior |
| Heartbeating omitted for long-running activities | Make stuck workers visible before duplicate execution risk grows |
| Workflow versioning skipped during deployments | Protect in-flight workflows during changes |
| Search attributes not designed upfront | Make production debugging and operational queries possible |
What you leave with
| Output | Decision It Supports |
|---|---|
| Temporal workflows with versioning, retry policies, and activity patterns | The system has an explicit recovery model |
| Operational runbooks | Deployment, debugging, and failure recovery become repeatable |
| Search attributes and observability | Operators can query production workflow state |
| Architecture documentation | Teams can extend workflows without violating determinism constraints |
Best Fit
- Team has long-running workflows that must recover cleanly from infrastructure failures
- Organization operates multi-step processes spanning external APIs and human approvals
- Engineering team needs a stronger recovery model than “retry and hope”
- Product requires audit trails and replay capability for regulated review
Depth of Practice
We operate Temporal workflows for content engines, multi-step approval pipelines, and cross-service orchestration where recovery behavior, replay, and operational visibility matter.
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