AI agent orchestration: LangGraph vs custom vs workflow engines
What should you know about AI agent orchestration?
Orchestration gives agents durable state, retries and traces; use LangGraph for agent logic and a workflow engine like Temporal for long-running reliability. The two solve different problems, and a custom loop is right only when the agent is short-lived and the team already owns the retry and tracing plumbing.
AI agent orchestration is the layer that decides what runs next, remembers where a task got to, retries what failed and records what happened. A demo agent does not need it; a loop in a notebook is enough. A production agent that takes minutes or days, calls a dozen tools, waits for a human approval and must survive a deployment mid-task needs all of it. The choice is between an agent framework such as LangGraph, a general workflow engine such as Temporal, a custom loop, or a combination.
Our default is a combination: LangGraph or similar for the agent's reasoning graph, and a workflow engine for the durable outer process. This article explains why, when each alone is enough, and how to avoid paying for both without needing either.
What an agent workflow engine actually provides
Strip away the vocabulary and orchestration is four capabilities.
- Durable state. The task's progress is persisted so that a crash, a deploy or a restart resumes rather than restarts. Without this, a long agent run that fails at step nine repeats steps one to eight, with their side effects.
- Retries and timeouts. Tool calls fail. Models time out. The orchestrator retries with backoff, distinguishes retryable errors from permanent ones, and gives up cleanly.
- Waiting. Agents wait for humans (approvals), for external systems (a payment to settle) and for time (follow up in three days). Waiting without holding a process open, for hours or weeks, is a hard engineering problem that engines solve.
- Traces. Every step, input and output is recorded so that a run can be replayed, debugged and used as an evaluation case.
Agent frameworks provide the first and fourth well and the second partially. Workflow engines provide all four but know nothing about models or prompts. That asymmetry is the whole decision.
LangGraph vs Temporal vs custom: the comparison
| Concern | LangGraph (agent framework) | Temporal (workflow engine) | Custom loop |
|---|---|---|---|
| Agent logic (graph, branching, tool loops) | Native; typed state and nodes | Possible but verbose | You write it |
| Durable execution across restarts | Checkpointers persist state; you manage the runtime | Core feature; workers are stateless | You build it |
| Long waits (days) for approvals or events | Interrupts exist; long waits need external plumbing | Native signals and timers | Cron and a database |
| Retries with backoff and idempotency | Per-node retry policies | Native, per activity | You build it |
| Tracing and replay | Good via LangSmith or OpenTelemetry | Event history is the source of truth | You build it |
| Streaming tokens to a UI | Native | Not its job | Straightforward |
| Operational overhead | Low to moderate | A cluster to run or a cloud service | None until it breaks |
| Fit | Reasoning inside a task | The process around tasks | Short, single-tool agents |
When LangGraph alone is enough
If a task completes in seconds to a few minutes, does not wait on humans mid-flight, and losing a run to a restart is acceptable (the user just retries), an agent framework with a persistent checkpointer is sufficient. Most in-app copilots and support agents fit here: a conversation turn triggers a graph, the graph calls tools, a result streams back. The LangGraph documentation describes the state, node and checkpoint model clearly, and the typed-state discipline it enforces is valuable whether or not you keep the library long term.
The trap is growing past this without noticing. The moment a task needs "wait for the finance manager to approve, then continue", teams start building queues and cron jobs around the framework, and six months later they own a fragile workflow engine of their own.
When a workflow engine is the right layer
Use Temporal, or an equivalent durable-execution engine, when tasks are long, when they wait on people or external events, when the side effects are expensive to repeat, or when you need an exact history of what ran for audit. Claims processing, onboarding, collections sequences and reconciliation all look like this: a process that spans days, with agent steps inside it and human steps between them. The Temporal documentation covers durable execution, signals and timers; the mental model is that the workflow code is the source of truth and the engine replays it after any failure.
In this design, each agent step is an activity: a bounded call that runs the reasoning graph, returns a structured result, and can be retried safely because the workflow, not the agent, owns the process state. Approvals are signals. Follow-ups are timers. Nothing is lost when a worker restarts.
When a custom loop is honest
A custom loop is right for a narrow, short agent with one or two tools, owned by a team that already has retry and tracing infrastructure and does not want another dependency. It is wrong as soon as anyone says the words "we will add persistence later". The retry, idempotency and replay logic that engines provide is exactly the code teams get wrong under pressure, and rebuilding it inside a business application is rarely a good use of engineering time.
Durable agent execution: the combined pattern
The pattern we ship for anything that spans systems or time is layered. The workflow engine owns the process: the steps, the waits, the retries, the history. Each step that needs reasoning runs an agent graph as an activity, with its own tool loop and step budget. The graph returns a typed result to the workflow. Approvals, limits and gates are enforced at the workflow layer, where they are durable and auditable, rather than inside the graph.
This keeps agent logic testable in isolation, because each graph is a function from input to structured output, and it keeps the process reliable because the engine has solved the hard distributed-systems problems already. The planner, worker and reviewer roles from Multi-agent systems explained map cleanly onto this: the planner's output becomes the workflow's step list; each worker is an activity; the reviewer is a step before any external action.
Idempotency: the detail that decides whether retries are safe
Retrying an agent step that already issued a refund issues it twice. Every tool that changes state must accept an idempotency key derived from the workflow and step identifiers, so a retried call is recognised and returns the original result. This is a tool-contract requirement, not an orchestration feature, and it is the first thing we check when reviewing an existing agent. The tool-side design is covered in MCP explained.
A worked example
A last-mile logistics operator needed dispatch decisions that involved reading orders, checking driver availability, proposing assignments, waiting for a dispatcher to confirm exceptions, and then pushing routes to an offline-first driver app that might not sync for an hour. The first prototype was a single agent loop that held everything in memory; a deploy in the afternoon lost every in-flight assignment.
The rebuilt system put the process in a workflow engine: one workflow per dispatch window, with agent activities for assignment proposals, a signal for dispatcher approval, timers for sync confirmation and retries with idempotency keys on every push. The agent graphs stayed small and were evaluated on their own. The dispatch platform case study describes the wider build.
Team and timeline
Choosing and standing up the orchestration layer is a one-to-two-week architecture task at the start of an agent build, done by a backend engineer with the AI engineer. Running a workflow engine adds operational scope: either a managed service or a small cluster your platform team owns, which is part of the discussion on the private agentic AI page for teams that must self-host. Orchestration work is included in the multi-agent systems service from $24,500 or ₹16 lakh; a ten-day Sprint Zero settles the framework-versus-engine question with a written recommendation before you commit. See the pricing page for programme and service prices.
Before you start: a checklist
- Measure how long a task really takes end to end, including human waits
- List every step that changes external state and confirm it can be idempotent
- Decide whether losing an in-flight run on deploy is acceptable
- Separate agent logic (graph) from process logic (workflow) on paper first
- Choose where approvals and limits are enforced; make it the durable layer
- Agree who operates the engine, or pick a managed service
- Plan tracing so a run can be replayed into an evaluation case
- Set step budgets and timeouts per activity, not just per task
Glossary
- Durable execution: process state persisted so work resumes after failure
- Activity: a bounded, retryable unit of work inside a workflow
- Signal: an external event, such as an approval, delivered to a running workflow
- Checkpointer: the component that persists an agent graph's state between steps
- Idempotency key: an identifier that makes a retried call return the original result
- Replay: reconstructing a run from its event history to debug or test it
Related reading
How to build an AI agent that is safe to run unattended covers the limits and gates that sit at the workflow layer, Shadow mode covers the approval signals in practice, and What is an AI agent? is the non-technical introduction.
Put reasoning in the graph, the process in the engine, and idempotency in every tool, and an agent that runs for days becomes as boring to operate as any other service.
Frequently asked questions
Should we use LangGraph or Temporal for AI agents?
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Usually both, for different jobs. LangGraph handles the reasoning graph inside a task; Temporal handles the durable process around tasks, including long waits, retries and history. LangGraph alone is enough for short tasks that do not wait on humans.
What is durable agent execution?
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Persisting an agent's progress so that a crash, deploy or restart resumes the task instead of repeating it. It requires persisted state, retry policies and idempotent tools, so that a retried step does not repeat a side effect such as a payment.
Is a custom orchestration loop ever the right choice?
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For a short, single-tool agent owned by a team that already has retry and tracing infrastructure, yes. As soon as the agent needs persistence, long waits or replay, a framework or engine is cheaper than building those from scratch.