The current generation of Agentic AI frameworks was designed for short-lived chains of tools. While effective for simple tasks, they fall short when used for more complex, long-running workflows. Developers trying to push these frameworks beyond their limits run into fundamental problems: lack of durability, limited scalability, and rigid integration options.

Temporal provides a more robust approach. It was built for reliability and scale, making it an ideal foundation for Agentic AI workflows that need to persist, interact with humans, and evolve with the fast-changing AI landscape.

Here’s how Temporal differentiates itself:

Developer Velocity#

Temporal is designed to feel natural and idiomatic for developers. With the Python SDK and other language support, you don’t have to think about state machines or complex orchestration — it just works, letting you focus on building rather than managing infrastructure.

Insights and Observability#

Step-debugging workflows, tracking execution with detailed UI metrics, and leveraging Temporal’s visibility features make it easy to troubleshoot and optimize. Developers can quickly identify bottlenecks and ensure their agents run efficiently.

Scheduled Execution#

Temporal supports running workflows on a schedule, enabling AI agents to periodically poll for new data and act accordingly. This makes it an ideal choice for use cases requiring continuous updates and real-time responsiveness.

Durable and Resilient#

LLMs are probabilistic by nature and can sometimes return incorrect or inconsistent responses. Temporal’s retry capability helps mitigate these issues, ensuring that workflows can recover from bad outputs and continue progressing reliably.

In addition, Temporal ensures workflows can survive real-world failures: process crashes, bad data, and network timeouts. Unlike single-process frameworks, Temporal retains state and automatically retries failed steps, ensuring that agents recover and continue without losing progress.

In many other frameworks, a crash means the whole process stops, forcing developers to rebuild context from scratch. With Temporal, that’s never a concern. Here’s how a typical Temporal agent orchestrator workflow operates:

The user initiates a request (signal). The agents (activities) determine the next step.
If needed, the workflow queries an LLM with workflow text as context.
Possible agent responses:
Ask the user for more information. Request permission to run a tool.
The user confirms the tool run (signal). The tool runs (API call), and the response is parsed by an LLM and sent back to the user. Steps repeat until the agent reaches its goal.

Long-Running and Stateful#

Most frameworks handle short-lived sequences. Temporal is built for workflows that last hours, days, or even months. It maintains state across the entire lifecycle, so your agent never loses track of its goal or context — no matter how complex or prolonged the interaction.

Human-in-the-Loop Support#

Some decisions require human oversight. Temporal makes it easy to involve people at critical moments:

Pause workflows for approval or input. Provide updates and notifications for human intervention when necessary. This makes Temporal well-suited for enterprise applications where accountability and control matter.

Flexible and Extensible#

In a constantly evolving AI ecosystem, flexibility is key. Many existing frameworks are tied to specific LLMs or databases, limiting adaptability. Temporal offers:

Support for multiple languages (Go, Python, Java, TypeScript, .NET, Ruby). Easy integration with any LLM, vector database, or external service. You’re not locked into a single vendor or toolchain — new AI models and services can be added as they emerge.

Centralized Orchestration#

Temporal functions as a “brain” for coordinating API calls, services, and data sources. It is architected to be fault-tolerant and horizontally scalable, ensuring high availability and resilience even in large, distributed environments. It scales far beyond the single-process limitations of other frameworks, allowing developers to create distributed workflows without worrying about state management or fault tolerance.

The Future of Agentic AI#

Agentic AI is evolving quickly. Building agents that can persist, collaborate with humans, and adapt requires a solid foundation. Temporal offers the durability, flexibility, and scale needed to meet these demands without being boxed in by the constraints of traditional frameworks. Learn more about using Temporal for Agentic AI here.

Ready to build resilient, scalable AI agents? New Temporal Cloud users get $1,000 in free credits to get started. Sign up now and start building today.