LangGraph vs Temporal vs Cordum (2026): Agent Logic, Durable Execution, and Governance

The production problem

“LangGraph vs Temporal” looks like a fair comparison until an incident hits production. Then the missing layer appears: who decides whether the agent is allowed to run the action at all.

LangGraph gives expressive agent flow. Temporal gives reliability under failure. Neither is a policy decision point by default. If your agent modifies customer-facing systems, this gap becomes the incident.

One-line rule

If your approval model is “someone will notice in Slack,” you do not have an approval model.

What top sources cover vs miss

Source	Strong coverage	Missing piece
LangGraph Durable Execution Docs	Clear requirements for checkpointers, thread IDs, determinism, and idempotent task boundaries during replay.	Does not define policy-gated execution for high-risk external actions across organizational boundaries.
LangGraph Persistence Docs	Detailed model for threads, checkpoints, super-steps, and production checkpointer backends.	No guidance for pre-dispatch approval workflows or immutable multi-system governance audit requirements.
Temporal Durable Execution Technical Guide	Strong explanation of completion guarantees, retries, signals/queries, and long-running workflow behavior.	No built-in AI-specific policy decision model (`ALLOW`, `DENY`, `REQUIRE_APPROVAL`) before tool or API side effects.

This guide fills that gap with a layer model, explicit ownership boundaries, and code-level integration points.

Three layers, three jobs

Layer	Owner	Responsibility	Common failure
Reasoning layer	Applied AI team	Prompting, tool selection, agent graph behavior	Agent loops or brittle branch logic
Execution layer	Platform team	Retries, resumability, timeouts, idempotent activity calls	Stuck workflows and duplicate side effects
Governance layer	Security + platform	Policy checks, approval gates, auditability, output safety	Unapproved prod actions and weak incident forensics

Side-by-side comparison

Dimension	LangGraph	Temporal	Cordum
Primary concern	Agent control flow and state transitions	Durable execution and failure recovery	Governance, policy, and approvals
Unit of orchestration	Graph nodes and edges	Workflow + activities	Jobs + policy checks + workflow steps
Long-running reliability	Depends on persistence setup	Core guarantee	Job state, scheduler reconciliation, DLQ
Pre-execution policy	Custom	Custom	Built-in Safety Kernel decisions
Human approval routing	Custom interrupt handling	Custom signal + workflow logic	First-class `REQUIRE_APPROVAL` and approval state
Audit trail	State checkpoints	Workflow event history	Policy snapshot + decision timeline + job history
Best fit	Rapid agent behavior development	Business-critical process durability	Regulated or high-impact agent actions

Reference architecture

LangGraph

Computes diagnosis and proposes actions. No side effects yet.

Temporal

Runs durable workflow, retries transient failures, and resumes after outages.

Cordum

Checks policy before execution, routes approvals, and records audit timeline.

Keep policy decisions outside agent prompt logic. Prompt changes should not silently change risk posture.

Working code patterns

Pattern: graph proposes, durable workflow orchestrates, policy gate decides, then side effects execute.

langgraph-flow.py

Python

from typing import TypedDict
from langgraph.graph import StateGraph, START, END
from langgraph.checkpoint.postgres import PostgresSaver

class AgentState(TypedDict):
    ticket_id: str
    summary: str
    proposed_action: str


def analyze(state: AgentState):
    # LLM call or retrieval logic
    return {"summary": "Root cause likely config drift"}


def propose(state: AgentState):
    return {"proposed_action": "restart_service:payments-api"}


builder = StateGraph(AgentState)
builder.add_node("analyze", analyze)
builder.add_node("propose", propose)
builder.add_edge(START, "analyze")
builder.add_edge("analyze", "propose")
builder.add_edge("propose", END)

with PostgresSaver.from_conn_string(DB_URI) as checkpointer:
    graph = builder.compile(checkpointer=checkpointer)
    result = graph.invoke(
        {"ticket_id": "INC-441", "summary": "", "proposed_action": ""},
        config={"configurable": {"thread_id": "inc-441-thread"}},
    )

incident-workflow.ts

TypeScript

import { proxyActivities } from "@temporalio/workflow";

const { runLangGraphStep, cordumPolicyCheck, executeRemediation, verifyRemediation } =
  proxyActivities<{
    runLangGraphStep(input: unknown): Promise<unknown>;
    cordumPolicyCheck(input: unknown): Promise<{ decision: string }>;
    executeRemediation(input: unknown): Promise<void>;
    verifyRemediation(input: unknown): Promise<{ passed: boolean }>;
  }>({ startToCloseTimeout: "2 minute" });

export async function IncidentWorkflow(input: { incidentId: string }) {
  const proposal = await runLangGraphStep(input);
  const policy = await cordumPolicyCheck(proposal);

  if (policy.decision === "DENY") {
    return { status: "blocked_by_policy" };
  }

  if (policy.decision === "REQUIRE_APPROVAL") {
    // wait for external approval signal handled by workflow code
    // omitted here for brevity
  }

  await executeRemediation(proposal);
  const verification = await verifyRemediation({ incidentId: input.incidentId });

  if (!verification.passed) {
    throw new Error("verification_failed");
  }

  return { status: "resolved" };
}

safety-policy.yaml

YAML

version: v1
rules:
  - id: deny-destructive-shell-prod
    match:
      topic: "job.exec.shell"
      labels:
        env: prod
        command_class: destructive
    decision: DENY

  - id: approval-required-prod-remediation
    match:
      topic: "job.incident.remediate"
      labels:
        env: prod
        risk_tier: high
    decision: REQUIRE_APPROVAL

  - id: constrained-medium-remediation
    match:
      topic: "job.incident.remediate"
      labels:
        risk_tier: medium
    decision: ALLOW_WITH_CONSTRAINTS
    constraints:
      max_runtime_sec: 180
      allowed_namespaces: ["prod-edge", "staging"]

For a deeper production checklist, pair this with the AI agent deployment checklist.

Limitations and tradeoffs

More moving parts

Three layers add operational complexity. You need clear ownership, not shared ambiguity.

Replay discipline required

Durable systems require determinism boundaries. Side effects must be isolated to avoid duplicate impact.

Approval latency

Strong governance can slow urgent changes if risk tiers are too broad. Review queue metrics every week.

Frequently Asked Questions

Do I need both LangGraph and Temporal?

If workflows are short-lived and low-impact, LangGraph alone can be enough. If failures, retries, and multi-hour waits matter, add Temporal for durability.

Where should governance live?

Outside prompts and outside ad-hoc workflow code. Governance should be a dedicated decision layer with explicit policy outputs and audit history.

Can Temporal replace governance?

No. Temporal handles execution reliability. It does not natively decide if an action is allowed by organizational policy.

Can LangGraph interrupts replace approval systems?

Interrupts can pause flow, but production approval systems also need identity, policy snapshot binding, and audit requirements that persist across teams.

What is the minimum safe stack for production agent actions?

A reasoning framework, a durable execution runtime, and a policy gate before side effects. Missing any one of the three increases incident cost.