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

Short answer

These three are not competitors — they are different layers. LangGraph defines agent control flow (graph nodes and edges). Temporal makes long-running execution durable and crash-safe (workflows and activities). Cordum governs what an agent is allowed to do: its Safety Kernel returns an ALLOW, DENY, REQUIRE_APPROVAL, or ALLOW_WITH_CONSTRAINTS decision before any side effect runs, then records an audit trail. For production agents that touch real systems, use LangGraph for reasoning, Temporal for durability, and Cordum for the policy gate.

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

The Safety Kernel checks policy before dispatch, routes REQUIRE_APPROVAL jobs to an approval queue, and records a decision + snapshot audit trail.

In Cordum the gate is not in your workflow code — it is a separate service. The scheduler calls the Safety Kernel over gRPC before it routes a job onto the NATS bus, so the policy decision happens whether the request came from a LangGraph agent, a Temporal activity, or a direct API call. Keep policy decisions outside agent prompt logic: prompt changes should not silently change risk posture. For the broader picture of where this gate sits in an enterprise agent estate, see the AI agent governance solution.

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

# Input policy rules, evaluated by the Safety Kernel
# before the scheduler dispatches a job. Schema:
# core/infra/config/safety_policy.go (rules[].match / decision / constraints)
rules:
  - id: deny-destructive-shell-prod
    match:
      topics: ["job.exec.shell"]      # glob via path.Match, e.g. job.*
      labels:
        env: prod
        command_class: destructive
    decision: deny

  - id: approval-required-prod-remediation
    match:
      topics: ["job.incident.remediate"]
      risk_tags: ["high"]
      labels:
        env: prod
    decision: require_approval         # binds approval_ref to the job_id

  - id: constrained-medium-remediation
    match:
      topics: ["job.incident.remediate"]
      risk_tags: ["medium"]
    decision: allow_with_constraints
    constraints:
      budgets:
        max_runtime_ms: 180000
        max_retries: 1
      sandbox:
        isolated: true
        network_allowlist: ["api.internal.example.com"]
      toolchain:
        allowed_commands: ["kubectl rollout restart"]

Each rule resolves to one of the Safety Kernel’s normalized decisions — allow, deny, require_approval, throttle, or allow_with_constraints. Rules match on topics (glob patterns like job.*), risk_tags, labels, MCP server/tool, and more. Policy bundles are hot-reloaded and snapshot-hashed, so every decision is traceable to the exact policy version that produced it.

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. Cordum's Safety Kernel adds that decision: the scheduler calls it before every dispatch and receives an ALLOW, DENY, REQUIRE_APPROVAL, or ALLOW_WITH_CONSTRAINTS result.

How does Cordum's policy gate actually work?

Cordum runs a dedicated Safety Kernel as the policy decision point. Before the scheduler dispatches a job over the NATS bus, it calls the kernel (gRPC Check), which evaluates the request against versioned policy bundles. When approval is required, the approval is bound to the policy snapshot and job hash, so the decision cannot be silently re-interpreted later. Decisions and snapshots are persisted as an audit trail in Redis.

Can LangGraph interrupts replace approval systems?

Interrupts can pause flow, but production approval systems also need identity, policy-snapshot binding, and audit history that persists across teams. In Cordum, REQUIRE_APPROVAL is a first-class job state (APPROVAL_REQUIRED) and the approval is cryptographically bound to the snapshot that produced it.

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. Cordum also adds post-execution output safety, which can ALLOW, REDACT, or QUARANTINE a result before it leaves the platform.