Multi-agent workflows involve the orchestration and coordination of tasks or processes carried out by multiple autonomous agents. These agents can be software applications, artificial intelligence models, bots, or even human participants. Multi-agent workflows are often used in scenarios where tasks are distributed, asynchronous, or require collaboration among diverse systems or entities.

Multi-agent workflows also share some similar characteristics, even though the use cases may vary widely (conversational AI, data pipelines, autonomous vehicles, similarity search and recommendation engines, etc could all be a multi-agent workflow). Those characteristics are:

Decentralization: Tasks are distributed among agents that operate independently. No single agent has complete control; instead, a coordinating system ensures tasks are executed as intended. Coordination and Communication: Agents need to exchange data or signals to complete their individual tasks. Orchestration frameworks often act as the "manager," ensuring proper communication and timing. Autonomy: Each agent can perform its tasks independently based on its programming or training. Agents can make decisions locally (e.g., retrying a failed task or escalating an issue). Interdependency: Tasks performed by one agent often depend on the outputs of another, requiring seamless handoffs and state management. State Management: Maintaining workflow state across agents is crucial, especially in long-running workflows.

Temporal is well-suited to support multi-agent workflows because it handles the orchestration, state management, and coordination across different agents (AI or otherwise).

In addition to this, Temporal doesn’t need you to build specific code for each agentic framework.

As Anton Tsitou, CTO at Spiral Scout, noted:

"Most 'agentic' frameworks require you to build code for the framework specifically, but Temporal doesn't. It already provides the service mesh, so you don’t have to reinvent it."

This distinction not only streamlines implementation but also reinforces Temporal’s role as a general-purpose backbone for agentic systems.

Here’s how Temporal can specifically support such a use case:

1. Orchestrating Multi-Agent Interactions#

Think of Temporal as the "conductor," managing interactions among multiple agents by setting up workflows where agents communicate and pass information as tasks are completed.

In a multi-agent workflow, each agent may handle a specific part of a task — say, one agent for data processing, another for analyzing results, and a third for final reporting. Temporal ensures these tasks happen in the correct order and within defined parameters.

2. Stateful Coordination Across Agents#

Agents in a multi-agent system may need to maintain context or state throughout a workflow, especially when they need to reference prior steps, adapt based on changing information, or retry parts of the workflow.

Temporal’s state management capabilities allow it to store and retrieve data across these interactions, ensuring each agent has the correct context at every stage. Temporal can also time out, retry, or roll back actions if an agent fails, giving resilience to the overall system.

I recently spoke with the Twilio AI Assistants team to hear about their learnings on multi-agent workflows. One of the biggest challenges they’ve faced is enabling shared user context across multiple agents, channels, and conversations — a crucial element in delivering seamless, personalized experiences.

“As part of addressing this challenge, Twilio has invested in our Customer Memory capability which is powered by Twilio Segment,” shared the team. “Customer Memory enables each agent to access the businesses’ context on any user into every conversation and to store learnings from each interaction back to the shared context so that they can improve future interactions.”

This ability to maintain shared context is essential in complex workflows, ensuring that agents can pick up where the previous one left off, making the system more effective and reliable for the user.

3. Reliable Async Communication#

Multi-agent systems often require asynchronous communication since agents can take variable time to complete tasks or may have dependencies on each other. Temporal’s ability to manage async and sync processes smoothly means that agents can be orchestrated effectively regardless of individual response times.

Temporal’s timers, retries, and error handling ensures that if one agent fails or takes too long, the entire workflow doesn’t collapse. Instead, it can adapt or notify other agents as needed.

4. Parallel Processing for High Efficiency#

For multi-agent systems that involve parallel tasks, Temporal’s parallelism and concurrency control enable multiple agents to work simultaneously without blocking each other. This is ideal when agents operate independently on sub-tasks that later need to converge.

For example, in a recommendation system, separate agents might handle user data processing, item analysis, and scoring. Temporal can coordinate the simultaneous execution of these tasks and merge results efficiently.

5. Long-Running Workflows with Multi-Step Processes#

In use cases where agents contribute to a long-running process — like generating and refining a report over several steps — Temporal’s ability to maintain workflows over hours or even days is invaluable. Each agent can do its part, and Temporal keeps the process stable over time.

Temporal’s workflows can sleep, wait for new data, or pause until a signal (such as an update from an agent) arrives, allowing for workflows that evolve as agents contribute data at different times.

6. Event-Driven Triggers and Signals#

Multi-agent systems often operate in response to events (like incoming data or status updates from other agents). Temporal supports signals and queries that let agents or external triggers start, modify, or stop workflows in real time.

This approach not only enables flexible workflows, but it also integrates seamlessly into a unified hub for activities within a product. This means that anything that Temporal interacts with inside the system can be used as part of the agentic workflow, without the need to build separate agentic functions.

For example, a monitoring agent could signal other agents to update their analysis if new data arrives or a condition changes, ensuring that all activities across the system can be orchestrated as part of a single, cohesive workflow.

Practical Example: A Multi-Agent Workflow for Customer Support#

Imagine a customer support system where Temporal coordinates multiple agents:

Agent A: Analyzes incoming customer requests and classifies them by their level of urgency. Agent B: Looks up customer history and finds relevant data. Agent C: Routes the request to the appropriate support team or initiates an automated response.

Temporal would:

Trigger Agent A upon receiving a request. Wait for Agent A’s result, then pass the data to Agent B. Manage async calls to Agent C for routing and initiate follow-up workflows based on responses.

Temporal’s orchestration here allows each agent to function independently yet stay coordinated, enabling seamless and efficient customer support automation.

In summary#

Temporal’s orchestration, durability, and ability to manage complex stateful and stateless interactions make it a perfect foundation for multi-agent workflows in AI applications. Temporal ensures that these interactions are reliable, efficient, and resilient, even as workflows grow in complexity or scale.

Learn more about Temporal and get started with a free trial + $1,000 in credits today.