Yes, you can run Catalyst agent workflows on standard FaaS platforms, but only for workloads that do not require execution beyond the platform’s timeout window. Catalyst integrates with FaaS runtimes via Dapr, passing durable execution state between invocations. A key caveat is that workflows exceeding the FaaS timeout will fail, as the runtime cannot persist execution beyond its configured window.
Yes, you can deploy Catalyst agent workflows on self-managed Kubernetes clusters. Catalyst is built on standard Dapr, which natively integrates with Kubernetes, and supports reliable durable execution that persists past individual pod restarts or planned cluster upgrades. A key caveat is that you must configure proper dedicated local persistent storage for your critical workflow state to avoid unintended data loss during both major cluster modifications and any unexpected pod terminations.
Yes, you can deploy Catalyst agent workflows to managed container services. Catalyst leverages Dapr to abstract important core container runtime details, letting workloads persist their execution state across container restarts or platform service updates without relying on any third-party proprietary runtime tools. A key caveat is that long-running workflows may face significant uptime limits on container instances imposed by the underlying managed service provider you choose for your cloud deployments.
Yes, you can run Catalyst agent workflows as long-running services across a range of standard operational setups. Catalyst’s durable execution layer supports persistent, long-duration workloads, and its Dapr integration allows the service to maintain state across routine restarts or configuration updates. A key caveat to note is that you must actively manage service scaling to avoid overprovisioning resources for idle workflow instances.
Human-in-the-loop Catalyst agent workflows function across most of its supported deployment targets. Its durable execution layer pauses active workflows until human approval is received, with behavior varying across different underlying runtime platforms, regardless of their specific configuration beyond core supported runtime features. One key caveat applies: FaaS platforms with short timeouts may interrupt the paused workflow before a human approval is submitted.
You can avoid rebuilding agent workflows for different deployment targets by using Catalyst’s Dapr-based abstraction layer. This layer decouples agent code from underlying runtime details, letting you deploy the same workflow code across FaaS, Kubernetes, or managed containers without code changes. You will need to test workflows against target-specific constraints such as timeout limits or resource quotas for reliable operation.
Standard FaaS platforms are not ideal for long-running durable agent workflows that outlive a single invocation. Catalyst relies on retaining execution context across pauses, but FaaS terminates processes after set timeouts, breaking state retention during waits or human approval steps. Network-bound state transfers between invocations add overhead and consistency risks. You can run Catalyst on FaaS only if workflows are split into short, timeout-compliant segments, but this requires extra integration work.
Standard Kubernetes clusters support durable agent workflows, including those with human approval and long pauses. Catalyst runs on Dapr, so you can deploy containerized agent workflows without rewriting code, leveraging deployments or statefulsets to retain execution context across restarts. Network data transfers only happen during explicit external tool calls. Avoid using ephemeral pod storage for agent state, as pod termination will erase critical execution data.
Managed container services can effectively support durable agent workloads when configured properly. Many, including Catalyst-backed deployments, leverage portable framework support like Dapr, retain execution context across restarts, limit network transfers to only explicit API calls, and let you deploy containerized agent workflows without rewriting code. A key caveat is to ensure the service does not enforce strict inactivity timeouts that terminate idle, long-running workflow processes.
Running Catalyst agent workflows as long-running services is a viable deployment option. Since Catalyst is built on Dapr, you can deploy agent workflows as persistent services without rewriting code, retaining execution context across routine updates and restarts. Network data transfers only occur when the agent interacts with external tools or datastores. Ensure the underlying host environment does not enforce resource limits that disrupt long-running workflow loops.
