AI tools are changing at an incredible pace, and at Corbits, we understand that staying competitive means keeping up with the latest technologies. AI orchestration is an effective way to maximize your computing infrastructure and stay ahead.
What is AI Orchestration?
Workflow engines, also known as orchestration engines, are applications that can create and automate AI workflows. These tools orchestrate groups of AI agents, wrangling unpredictable AI behavior and converting it into reliable systems.
AI agents are incredibly powerful, but in order for businesses to effectively use them, systems must produce accurate results with minimal human intervention. Agents working in tandem, coordinated by a workflow engine, can solve complex, multi-step tasks with ease.
How AI Orchestration Works
We can think of AI agent orchestration like filmmaking with a bit of improvisation from the cast. The actors represent the agents (CrewAI, LangGraph, etc.), analyzing scenes, processing lines, and improvising their own, while the workflow engine (Temporal, ApacheFlow, etc.) is the director and editor, ensuring structure, reshoots, and the final output.
The two layers of the production perform distinct tasks but interact to produce a greater end product. Agents think, and workflows guarantee execution.
Two Layers of Orchestration
┌─────────────────────────────────────────────────────┐
│ AGENT-LEVEL (AI reasoning) │
│ │
│ Frameworks: CrewAI, LangGraph, AutoGen, Swarm │
│ │
│ What Agents Manage: │
│ - Tool calling │
│ - Memory and state between steps │
│ - Multi-agent collaboration │
│ - Decision-making loops │
├─────────────────────────────────────────────────────┤
│ SYSTEM-LEVEL (How the system runs) │
│ │
│ Workflow Engines: Temporal, ApacheFlow, Prefect │
│ │
│ What Workflow Engines Manage: │
│ - Retries and failures │
│ - Scheduling │
│ - Long-running processes │
│ - External integrations │
└─────────────────────────────────────────────────────┘
When to Use a Workflow Engine to Orchestrate Agents
Workflow engines are essential when coordinating multiple AI agents that must operate together in a reliable way.
In a customer support system, agents might classify incoming requests, retrieve relevant knowledge, and generate responses. In data processing pipelines, agents may clean data, run analyses, and produce reports. All of these steps require clear task handoffs, error handling, and state tracking throughout the entire process.
Workflow engines are also useful when tasks need to be re-run, audited, or scaled across large systems. Such systems could be research or content generation pipelines. Any system with a sufficient level of complexity can make use of a workflow engine.
Orchestration Architecture Example
The diagram below illustrates a production architecture example, showing each piece of an orchestration system and the flow of data through it, from the frontend to the database layer.
Production Architecture
┌──────────────────────────┐
│ Frontend (UI) │
│ Web / Mobile / API │
└────────────┬─────────────┘
│
┌────────────▼─────────────┐
│ API Gateway │
│ Auth, Rate Limiting │
└────────────┬─────────────┘
│
┌────────────▼─────────────┐
│ Backend Service │
│ FastAPI / Node.js │
└────────────┬─────────────┘
│
┌────────────▼─────────────┐
│ Temporal Workflow │
│ (Orchestration) │
└────────────┬─────────────┘
│
┌────────────▼─────────────┐
│ Agent Execution Layer │
│ (LangGraph) │
└──────┬───────────┬───────┘
│ │
┌──────▼──────┐ ┌──▼──────────┐
│ Tool Workers│ │ Tool Workers │
│ (LangGraph) │ │ (External) │
└──────┬──────┘ └──┬──────────┘
│ │
┌──────▼───────────▼───────┐
│ Data Layer │
│ Postgres / Redis / │
│ Vector DB │
└──────────────────────────┘
Pattern: Temporal + LangGraph
In the Temporal + LangGraph pattern, a user engages the AI system and their request is sent through the chain. The Temporal workflow analyzes the request before sending it to the appropriate LangGraph agent for execution. The agent then attempts to solve the request, the state is recorded by Temporal, and the workflow decides whether the agent needs to retry, continue, or request human approval.
User Request
│
▼
Temporal Workflow
│
▼
LangGraph Agent Execution
│
▼
Tool Calls / APIs
│
▼
State persisted in Temporal
│
▼
Retry / Continue / Human Approval
Example Workflow Implementation Using Temporal and LangGraph
Workflow Engine (Temporal using Temporal Python SDK)
In this layer, Temporal is used to call the agent as an activity.
@workflow.defn
class AgentWorkflow:
@workflow.run
async def run(self, input):
result = await workflow.execute_activity(
run_agent,
input,
start_to_close_timeout=timedelta(minutes=5)
)
return result
Agent Layer (LangGraph)
In this layer, LangGraph manages the internal reasoning of the agents.
graph = StateGraph()
graph.add_node("planner", planner_agent)
graph.add_node("executor", executor_agent)
graph.add_edge("planner", "executor")
Common Orchestration Pitfalls to Avoid
Treating LLMs as deterministic
LLMs are inherently probabilistic, not deterministic, meaning that expecting consistent outputs for the same input is impractical.
Solution: Build with tolerance in mind. Validate outputs, define clearly how AI agents interact, share data, and follow workflows, and add retries or fallback logic.
Adding too much complexity too quickly
It's tempting to build complex multi-agent systems from the get-go. The problem is that this can introduce unnecessary complexity, make debugging nearly impossible, and cause performance needs and costs to explode.
Solution: Start with a single-agent setup using only the tools you need and add orchestration when you hit clear limitations.
Ignoring error handling and recovery
Systems don't know how to handle errors unless you specify the process. One failure can break your entire pipeline or cause cascading errors across multiple agents.
Solution: Add retries, timeouts, or circuit breakers and specify fallback strategies when the system makes a mistake.
Poor state and memory management
State and memory are the backbone of orchestration. Engines like Temporal natively handle these, but it's still best practice to understand what's happening under the hood. Poor state management causes context loss between steps and hallucinated continuity in systems, while poor memory management reduces learning capability and personalization potential for future sessions.
Solution: Take time to understand your system's state and memory management capabilities and parameters. Whether you're using a workflow engine that manages these automatically or building a custom solution, it's important to plan and design with state and memory in mind.
Ready to Build Your AI Orchestration System?
Now that you're more familiar with AI agent orchestration, with its different layers, frameworks, and the architecture involved, you're ready to implement a powerful tool for your business. If you're looking for high-performance, on-demand infrastructure to power your systems, speak to our team today and learn which of our products best suit your needs.