Parallel Agents
Parallel agents are multiple AI agents that execute concurrently on independent tasks, combining their results to complete complex workflows faster than sequential single-agent processing would allow.
Understanding Parallel Agents
Sequential AI execution — one task at a time, each waiting for the previous to complete — creates a latency ceiling. For complex requests that involve multiple independent information-gathering tasks, sequential processing is unnecessarily slow. Parallel agents break this ceiling by running independent work concurrently.
The key requirement for parallelization is task independence. Tasks that depend on each other's outputs must remain sequential. Tasks that don't — like querying your email, calendar, and task manager simultaneously — can run in parallel, reducing total completion time to approximately the duration of the longest individual task rather than the sum of all tasks.
Parallel agent architectures require orchestration: something must determine which tasks can run in parallel, dispatch them concurrently, and wait for all results before proceeding. LangGraph supports parallel node execution natively through branching and joining patterns in the graph structure.
Parallel agents also improve quality by allowing specialized agents to work simultaneously. A research task might dispatch one agent to gather current data, another to analyze historical context, and a third to check for recent updates — synthesizing all three views into a comprehensive response.
How GAIA Uses Parallel Agents
GAIA uses parallel agents for multi-source information gathering. When preparing a meeting brief, summarizing your week, or generating a project status update, GAIA dispatches multiple agents simultaneously to different tool integrations — reducing response time dramatically compared to querying each tool sequentially.
Related Concepts
Supervisor Agent
A supervisor agent is the AI agent in charge of a multi-agent system. It receives the request, splits it into subtasks, hands each one to a specialized worker agent (a subagent) with the right tools, checks what comes back, and combines the results into one answer. The workers do the execution; the supervisor decides who does what and when the job is done.
Subagent
A subagent is an AI agent that another agent starts to handle one part of a bigger task. The parent agent (often called the supervisor or orchestrator) gives it a written task, a focused set of tools and a fresh context of its own; the subagent works on that piece independently and returns only its result. Splitting work this way lets several subagents run in parallel and keeps the main agent's context clean.
Agentic AI
Agentic AI describes artificial intelligence systems designed to operate autonomously, making decisions and executing multi-step tasks with minimal human oversight.
Agent Loop
An agent loop is the iterative execution cycle of an AI agent in which it reasons about the current state, selects and executes an action (often a tool call), observes the result, and repeats until the task is complete or a stopping condition is reached.
AI Orchestration
AI orchestration is the coordination of multiple AI agents, models, and tools to work together in completing complex, multi-step tasks that no single component could handle alone.
