AI agent orchestration is the process of managing a series of automated tasks, executed by AI models and traditional software tools, to achieve a complex, long-running goal. While a single call to an LLM can generate code or summarize a document, most meaningful work—like building a feature, onboarding a customer, or processing an insurance claim—involves multiple steps, dependencies, and potential failures. Orchestration provides the backbone for these workflows, ensuring they run reliably from start to finish.

Think of it as the difference between a shell script and a robust backend application. A simple script executes commands in sequence, but an application manages state, handles errors, retries failed operations, and can be paused and resumed. Orchestration brings this level of durability and structure to AI-powered workflows.

Simple agent loops, where an AI model repeatedly calls tools until a goal is met, are powerful but brittle. They often struggle with tasks that are “bigger than one PR”—work that can’t be completed in a single, short-lived process. Orchestration is crucial for these scenarios because it addresses the core challenges of long-running, stateful operations.

These challenges include:

• Persistence: The system must remember the state of a workflow even if the server restarts or the process crashes.
• Error Handling: When an API call fails or an LLM returns an unexpected result, the workflow needs a strategy to retry the step or escalate the issue.
• Compensation: If a step in the middle of a workflow fails, you might need to “undo” the previous successful steps (e.g., cancel a flight booking if the subsequent hotel booking fails). This is often called the Saga pattern.
• Human-in-the-Loop: Many automated processes require human judgment for approval, ambiguity resolution, or final review. The orchestrator must be able to pause the workflow and wait for external human input.

Without a dedicated orchestration layer, you’d be forced to build this complex, failure-prone infrastructure yourself.

Regardless of the specific framework you choose, successful orchestration systems implement a few key patterns to ensure reliability and manage complexity.

• Stateful Execution: The orchestrator maintains the current state of the workflow, including the results of completed steps and what needs to happen next. This state is durably persisted, so the workflow can be resumed after interruptions.
• Idempotent Activities: Each individual task (or “activity”) in a workflow should be designed to be idempotent. This means it can be executed multiple times with the same input and produce the same result without causing unwanted side effects. This is critical for safely retrying failed steps.
• Asynchronous Handoffs: For tasks that require waiting—for an API call to complete, for a scheduled time to pass, or for a human to click a button—the orchestrator should not block resources. It should pause the workflow efficiently and resume it only when the external event occurs.
• Observability: Complex, multi-step processes can be difficult to debug. Good orchestration includes detailed logging, tracing, and visualization, allowing you to see the execution history of a workflow, inspect its state, and understand why it made certain decisions.

These patterns provide the foundation for building resilient, scalable, and manageable AI-driven systems.

Several tools have emerged to help developers implement these patterns. While they share common goals, they are optimized for different use cases. We’ll explore three popular options: Temporal, Dagster, and LangGraph.

• Temporal is a general-purpose, durable execution system designed for mission-critical, long-running workflows. It guarantees that your workflow code will execute to completion, regardless of infrastructure failures.
• Dagster is a data orchestrator that excels at building, testing, and monitoring data pipelines and machine learning systems. It treats the data assets produced by your AI agents as first-class citizens.
• LangGraph is a lightweight library built on LangChain specifically for creating stateful, multi-actor agent applications. It’s particularly well-suited for building cyclical graphs where the flow is not known in advance.

Temporal is designed to make your code fault-tolerant by abstracting away the complexity of state management, retries, and timers. It uses a “workflow-as-code” model where your business logic is written in a standard programming language, and the Temporal service ensures its durable execution.

A Temporal application consists of a client, the Temporal service, and a worker. The client starts a workflow, the service records its entire execution history, and the worker executes the individual steps, called Activities. If the worker crashes, the service will requeue the task and another worker will pick it up, using the execution history to replay the workflow to its last known state before running the failed activity.

This example shows a Python workflow that researches a topic and then writes an article. Each step is a durable Activity.

# activities.py
import httpx

# An Activity is a simple function that does the work.
async def research_topic(topic: str) -> str:
    # In a real app, this would call a search API.
    print(f"Researching: {topic}")
    return f"Detailed research notes about {topic}..."

async def write_article(notes: str) -> str:
    # This would call an LLM to write the article.
    print(f"Writing article based on: {notes[:20]}...")
    return "This is a generated article about the topic."

# workflow.py
from temporalio import workflow
from temporalio.common import RetryPolicy
import asyncio

with workflow.unsafe.imports_passed_through():
    from activities import research_topic, write_article

@workflow.defn
class ResearchWorkflow:
    @workflow.run
    async def run(self, topic: str) -> str:
        # Activities are executed with retries.
        notes = await workflow.execute_activity(
            research_topic,
            topic,
            start_to_close_timeout=asyncio.timedelta(seconds=60),
            retry_policy=RetryPolicy(maximum_attempts=3),
        )

        # The workflow can wait for human approval here if needed.
        # await workflow.wait_for_condition(lambda: self.is_approved)

        article = await workflow.execute_activity(
            write_article,
            notes,
            start_to_close_timeout=asyncio.timedelta(minutes=5),
        )
        return article

This workflow is durable. If the worker crashes while write_article is running, Temporal will restart it on another worker without re-running the research_topic activity.

Dagster is primarily known as a data pipeline orchestrator, but its core concepts are incredibly well-suited for AI agent workflows that produce data artifacts. Dagster focuses on “software-defined assets”—declarative definitions of the assets you want to create and the functions that create them.

In Dagster, you define a graph of assets, where each asset is a persistent object like a file, database table, or ML model. An asset is generated by a function (an “op”). Dagster understands the dependencies between these assets and orchestrates their creation, allowing you to version, test, and materialize them on a schedule or via a trigger.

Here, each step of the AI workflow produces a tangible data asset that Dagster manages.

from dagster import asset
import requests

# This asset represents the raw research notes.
@asset
def research_notes() -> str:
    topic = "multi-step AI agents"
    # This would call a research tool in a real application.
    notes = f"Here are detailed notes on {topic}."
    with open("research_notes.txt", "w") as f:
        f.write(notes)
    return notes

# This asset depends on the research_notes asset.
# Dagster automatically passes the output of the upstream asset.
@asset
def generated_article(research_notes: str) -> None:
    # This would be an LLM call.
    prompt = f"Write an article based on these notes: {research_notes}"
    article_text = f"This is a generated article based on the notes."
    with open("article.md", "w") as f:
        f.write(article_text)

# The human approval step is also modeled as an asset.
@asset(deps=[generated_article])
def reviewed_article():
    # This could trigger a notification in a UI or Slack,
    # waiting for a human to review `article.md` and
    # place the final version at `reviewed_article.md`.
    print("Please review article.md and save the final version.")

Dagster provides a rich UI for visualizing this asset graph, tracking its history, and re-running parts of the pipeline.

LangGraph is a part of the LangChain ecosystem designed to build complex, stateful agent runtimes with cycles. While Temporal and Dagster are general-purpose orchestrators, LangGraph is purpose-built for the dynamic, often unpredictable control flow of AI agents.

You define an agent as a state machine. The graph consists of nodes (functions or tools) and edges (logic that directs the flow from one node to another). The state is passed between nodes, and each node can modify it. Crucially, edges can be conditional, allowing the graph to loop and route logic based on the current state, which is perfect for agentic behavior.

This example shows a simple agent that can decide whether to use a search tool or respond directly to the user.

from langgraph.graph import StateGraph, END
from typing import TypedDict, Annotated
import operator

# Define the state for our graph.
class AgentState(TypedDict):
    messages: Annotated[list, operator.add]

def call_model(state):
    # A simplified "LLM" that decides if a tool is needed.
    last_message = state["messages"][-1]
    if "search" in last_message.lower():
        # Representing a tool call.
        return {"messages": ["Tool call: search('AI orchestration')"]}
    else:
        return {"messages": ["Final Answer: Here is the information."]}

def call_tool(state):
    # Simplified tool execution.
    tool_output = "Temporal, Dagster, and LangGraph are orchestration tools."
    return {"messages": [tool_output]}

# The conditional edge decides where to go next.
def should_continue(state):
    if "Tool call" in state["messages"][-1]:
        return "continue"
    else:
        return "end"

# Build the graph
workflow = StateGraph(AgentState)
workflow.add_node("agent", call_model)
workflow.add_node("tool", call_tool)

workflow.add_conditional_edges(
    "agent",
    should_continue,
    {"continue": "tool", "end": END}
)

workflow.add_edge("tool", "agent")
workflow.set_entry_point("agent")

app = workflow.compile()

# Run it
inputs = {"messages": ["Can you search for AI orchestration tools?"]}
for output in app.stream(inputs):
    print(output)

LangGraph is excellent for modeling the conversation and decision-making loop of an agent, but it doesn’t provide the infrastructure-level durability of Temporal or the data-lineage focus of Dagster.

When an AI agent performs a task, it often does so on behalf of a specific user. This introduces critical security and authorization requirements that orchestration frameworks alone don’t solve. An agent workflow that interacts with user data or third-party APIs needs to know who the user is and what they are permitted to do. This is where Kinde provides essential capabilities.

Imagine a workflow where an AI agent drafts a contract, sends it for internal legal review, and then submits it to a customer via DocuSign.

• User-Specific Permissions: The agent needs to access resources in your cloud, like a specific customer’s data. Kinde’s permissions and roles ensure the agent, acting on behalf of the user who initiated the workflow, only has access to the data it’s supposed to. You can define a “Contract Manager” role in Kinde and check for that role before allowing the workflow to proceed.
• Secure API Access: The workflow needs to call external APIs like DocuSign. The agent must do so within the context of the user’s account. Kinde can manage the OAuth 2.0 flow, providing the secure access tokens the agent needs to authenticate with these services on behalf of the user. This avoids storing sensitive user credentials directly in the workflow’s state.
• Human-in-the-Loop Authentication: The “legal review” step is a human handoff. When the lawyer receives a notification to review the contract, they must log in. Kinde provides the secure authentication to verify the lawyer’s identity before they are allowed to approve or reject the draft, ensuring the approval comes from an authorized individual.

By integrating Kinde, you can build powerful, secure, and auditable AI-driven workflows where every action is tied to an authenticated user with the correct permissions.

For more information on implementing these security patterns, explore the Kinde documentation:

• Manage user permissions
• Protecting your API
• Authenticate with Kinde using OAuth 2.0 or OpenID Connect