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Agentic AI
Planning & Control Flow
LLMCompiler
Take ReWOO's plan-as-DAG and run independent steps in parallel through a task-fetching dispatcher.
Intent & Description
🎯 Intent
Take ReWOO’s plan-as-DAG and run independent steps in parallel through a task-fetching dispatcher.
📋 Context
A team runs an agent whose work consists of many tool calls — fetching prices for nine tickers, summarising five documents, querying three APIs — and most of those calls are independent of each other. The deployment is latency-sensitive. The team is already using a plan-then-execute style such as ReWOO, where the planner emits a dependency DAG before any tool runs.
💡 Solution
- Planner builds the full dependency DAG of tool calls before any tool fires. - Task-Fetching Unit dispatches each step as soon as all its inputs are available, with bounded concurrency. - Joiner assembles the final answer from the resolved DAG once all steps complete.
Real-world Use Case
- Latency-sensitive agents waste time waiting on independent tool calls in series.
- A planner can build a dependency DAG up front for the workload.
- Bounded concurrency and a join step are acceptable engineering investments.
Source
Advantages
- End-to-end latency drops to the longest dependency chain.
- Cost remains roughly the same as ReWOO.
Disadvantages
- Concurrency adds operational complexity (rate limits, partial failures).
- Planner mistakes are amplified by parallel execution.