AI workflow automation platforms with record-and-replay and task orchestration capabilities address the challenges of building, running, and governing stateful, multi-step AI applications. This topic covers systems that capture interactions (record), recreate execution for debugging or compliance (replay), and coordinate tasks, agents, and data flows across services and models (orchestration). Relevance in 2026 stems from the rise of agentic applications and retrieval-augmented workflows that require repeatability, observability, and governance. Stateful orchestration (e.g., LangChain’s engineering stack and LangGraph) and developer platforms that support event chaining, lifecycle management, and memory (GPTConsole) are designed to move AI from experimentation to production. Enterprise-focused platforms (Kore.ai, Yellow.ai) emphasize no-code-to-pro-code multi-agent workflow composition, channel integration, and governance, while infrastructure offerings (Xilos) aim to provide end-to-end visibility into agent activity. Developer-first tooling for document agents and RAG (LlamaIndex) and web-grounded answer engines and APIs (Perplexity) show how data retrieval and grounded responses integrate into orchestrated flows. Operational needs—reproducible testing, automated code quality and SDLC governance (Qodo), developer IDEs with built-in agents and hosting (Replit)—are increasingly central as organizations scale AI automation. Record-and-replay supports incident investigation, model auditing, and deterministic testing of complex flows; orchestration tools handle retries, branching, error handling, and observability needed for compliance and reliability. Collectively, these categories—AI automation platforms and low-code workflow platforms—form the stack enterprises use to build, govern, and maintain reliable agentic systems in production.