Technical deep-dive into PromptImageLab's Directed Acyclic Graph (DAG) orchestration engine. Covers agent node types, dependency resolution, parallel execution, error fallbacks, and human-in-the-loop checkpoints.
DAG Execution Model
PromptImageLab's orchestration engine treats each agent as a typed graph node with declared input and output schemas. Edges represent data dependencies. The scheduler resolves dependency order at runtime, executing independent agents in parallel across isolated worker threads.
Parallel Execution & Performance
When two nodes have no shared dependencies, they execute in parallel. In the example above, "parse-logs" and "cmdb-lookup" run simultaneously, reducing total workflow latency by up to 60% vs sequential chaining. Each node timeout is configurable independently to prevent bottlenecks.
Human-in-the-Loop (HITL) Checkpoints
Insert a HITL node at any point in the DAG. When reached, the workflow pauses and sends a Slack or Teams notification to a designated reviewer. The reviewer approves or modifies the intermediate output, and execution resumes from that node.
Error Fallbacks & Retry Policies
Each node declares a fallback strategy: RETRY (up to N times with exponential backoff), SKIP (log and continue with null output), ABORT (halt entire workflow and notify), or SUBSTITUTE (use a lighter model if primary fails). This prevents full pipeline failure from transient provider errors.