What does a minimal but realistic durable agent look like when I first run Catalyst locally?A minimal durable agent starts as a single workflow with two to three steps, such as an LLM call followed by a conditional API action.How do I iterate quickly on durable agent logic without deploying to a shared cluster?Catalyst runs fully on your machine via a local Dapr sidecar, allowing you to execute and debug agent workflows without remote infrastructure.My agent is already built with LangChain/LlamaIndex. Can I add durable execution without a rewrite?You can adopt Catalyst incrementally by wrapping an existing agent’s top-level invocation in a durable workflow.Which agent workflows should I migrate to Catalyst first to gain the most value?Start with long-running, multi-step workflows that have costly failures today, such as document processing pipelines or approval chains.After migrating an agent to Catalyst, how do I confirm its behavior hasn't changed?Replay historical inputs through the new durable workflow and compare all external outputs and side effects against the original execution logs.What is a safe rollback strategy if a migrated durable agent behaves unexpectedly in production?Keep the original agent deployment live and use a feature flag to instantly switch traffic back.What kind of durable agent should I build first to understand Catalyst’s execution model?Start with an agent that has a few deterministic steps plus a single LLM call, so you can observe how Catalyst replays and resumes.How can I quickly test and debug Catalyst durable agents locally before pushing to a shared environment?Use Catalyst’s local development runtime to iterate rapidly on agent logic, with support for step-by-step replay inspection.How do I add Catalyst durable execution to an existing LLM framework agent without a full rewrite?Wrap the agent’s decision function into a Catalyst workflow definition, keeping the original framework code largely intact.What factors help decide which agent workflows to migrate to Catalyst first?Prioritize workflows with high business impact that currently suffer from unreliable execution or complex manual recovery.How can I validate that a migrated agent’s behavior hasn’t changed after adopting Catalyst?Run side-by-side comparison of outputs between the original and migrated agent using a representative test suite, with special attention to error paths and retry scenarios.What’s the rollback approach if a migrated durable agent shows unexpected behavior in production?Keep the original agent path deployed in parallel, using feature flags to switch traffic back quickly.When building my first durable agent locally, how can I step through a workflow execution to understand how Catalyst persists state between steps?Use your local debugger to set breakpoints inside durable step functions and inspect state snapshots Catalyst creates.How do I design the local development environment to simulate realistic partial failures, so I can see durable execution recovering state?Introduce an in-process fault injector that raises exceptions at specific step boundaries or causes transient I/O errors.What pattern should I follow to wrap existing agent logic in durable steps without altering the core business logic or its unit tests?Use an adapter function that receives the existing agent’s input, calls the original logic, and returns its output, while Catalyst manages the durable lifecycle around it.When migrating interdependent agent services, how do I sequence the adoption of durable execution to avoid ordering issues during intermediate states?Begin with the most downstream service that has the fewest incoming dependencies, making it durable first.After migration, how can I use parallel runs or shadowing to compare the new durable agent's decisions against the old agent's without affecting production?Run both old and newly durable agents side by side with the same production inputs for a representative period.If a rolled-back durable agent has left partially completed steps, what manual cleanup approaches are safe to use without corrupting future durable execution state?Use Catalyst’s built-in graceful shutdown combined with a compensating action pattern to revert completed side effects before tearing down state.How do I structure my first Catalyst durable agent project so that the business logic remains testable independently of the durable execution runtime?Separate agent logic into plain functions and orchestrate them with Catalyst workflows to keep unit testing lightweight.When I roll back a migrated durable agent, what verification steps confirm that no in-flight executions are still partially persisted in the new state store before I fully decommission the old system?Query the Catalyst state store for outstanding workflow instances after disabling new invocations, ensuring no partial executions remain before decommissioning.