AI Workflows
Definition
An AI workflow is a multi-step automation that chains AI recipes together with control flow logic — conditions, loops, parallel branches, human approval gates, LLM prompts, eval quality gates, routers, and aggregators. Workflows support sequential or DAG execution and transform isolated AI tasks into end-to-end business processes that run with minimal human intervention.
How AI Workflows Work
A workflow defines a sequence of steps, each of which can be a recipe step (runs an AI recipe), a condition step (branches based on values), a loop step (iterates over a collection), a parallel step (runs branches concurrently), or an approval step (pauses for human review). Data flows between steps automatically — each step receives outputs from previous steps as inputs. Workflows can be triggered manually, on a schedule, or by webhook events.
Nine Step Types
Recipe steps execute an AI recipe with automatic input mapping and retry logic. Condition steps evaluate expressions to branch execution. Loop steps iterate over collections. Parallel steps run branches concurrently. Approval steps pause for human review. LLM steps issue direct prompts without a recipe wrapper. Eval steps score outputs against quality criteria and can trigger convergence loops for iterative refinement. Router steps classify inputs and branch to specialist routes. Aggregator steps combine outputs from parallel branches via merge, best-of, or synthesize strategies. These nine primitives can model virtually any business process.
Workflows vs. Simple Automations
Traditional automation tools (Zapier, Make) focus on trigger-action patterns: when X happens, do Y. AI workflows go further by adding reasoning at every step. A workflow does not just move data — it reads documents, scores leads, drafts content, makes decisions, and requests human approval. The combination of AI reasoning and real workflow primitives (branching, loops, parallelism) is what distinguishes AI workflows from simple if-this-then-that automations.
Scheduling and Triggers
Workflows can run on demand, on a cron schedule (daily, weekly, custom), or triggered by webhook events from external systems. Scheduled workflows are ideal for recurring tasks like weekly report generation, daily ticket triage, or monthly board prep. Webhook triggers connect workflows to your existing tools — when a new lead arrives in your CRM, when a support ticket is created, or when a document is uploaded.
Monitoring and Observability
Every workflow run is tracked with full execution history: which steps ran, what inputs and outputs they produced, how long each step took, and how many tokens were consumed. Failed steps show error details and can be retried. Stalled workflows are detected by an automated watchdog. This visibility ensures you can debug issues, optimize costs, and maintain reliability as your automation scales.
Related Terms
AI Recipes
Learn what AI recipes are and how they work in JieGou. Recipes are reusable, single-operation AI building blocks with structured inputs and outputs.
BYOK (Bring Your Own Key)
Learn what BYOK means for AI automation. Bring Your Own Key lets you connect your own LLM API keys to JieGou for full cost control and data privacy.
MCP (Model Context Protocol)
Learn what MCP (Model Context Protocol) is and how JieGou uses it. MCP enables AI to interact with external tools, browsers, and services through a standardized protocol.