Agentic workflow architecture breaks down into modular blocks for reasoning, planning, action, and reflection. The structure relies on specialized agents handling specific tasks, while orchestration logic manages the flow between them. Registries store agents and enterprise data. Operator-based abstractions map retrieval, reasoning, and memory to execution patterns. Structural and semantic rules verify compatibility. Memory layers keep state persistent. Dynamic schedulers optimize routing based on query needs. The control plane acts as a tool, encapsulating complex routing behind standardized interfaces to support scalability.
For AI agents, the operation is an iterative execution loop. The agent evaluates a prompt, runs tools, and feeds results back into the model until it generates a final response. This follows three steps. First, the model assesses the current state and returns text or tool call requests. Second, the framework runs the requested tools and captures the output. Third, results feed back into the model. The loop repeats until no further tool calls are needed.
Modern implementations add specific features to this loop. WorkflowAgent from the AI SDK adds durability and resumability. Mastra’s agentic-loop uses a `do-while` structure to manage iterations and persistent snapshots.
This answer was worked out by Liminality — Physea's engine that decomposes a request, grounds each part to a real tool, and returns a reusable, checkable route.
Connect it over MCP: https://mcp.physea.ai/mcp · physea.ai