Firing Multiple Tickets at Once

Opening Scene

A kitchen running several tables’ orders doesn’t cook everything in strict, one-item-at-a-time sequence — independent dishes for different tables fire simultaneously across different stations, all coordinated to arrive together, dramatically faster than if every single dish were prepared one after another. An agent working through a multi-step plan can benefit from this same parallelism, wherever the plan’s steps are genuinely independent of each other.

In Plain English

Parallel execution runs independent subtasks in an agentic plan simultaneously, rather than strictly sequentially, when those subtasks don’t depend on each other’s results. This directly connects to the dependency identification covered in Article 5’s planning discussion: a well-structured plan explicitly identifies which steps must happen in sequence and which can genuinely run at the same time, and exploiting that distinction can meaningfully reduce a complex task’s overall completion time.

The Old Way

Before parallel execution was a recognized, deliberate optimization, agentic systems often defaulted to strictly sequential execution:

Early agentic loops typically executed a plan’s steps one at a time, in strict sequence, regardless of whether genuine dependencies actually required that ordering.
This left significant potential time savings unrealized for plans containing genuinely independent subtasks.
Identifying which steps in a plan were actually independent, rather than just listed sequentially, wasn’t yet a standard, deliberate analysis step.

Recognizing and exploiting genuine independence between plan steps emerged as agentic systems matured and latency became a genuine, practical concern.

What’s Changing (and Why AI Is the Reason)

Agent frameworks increasingly support explicit parallel execution of independent subtasks, connecting directly to the planning dependency analysis covered in Article 5.
This has become especially valuable for research and information-gathering tasks, where multiple independent searches or retrievals can run simultaneously rather than sequentially.
Parallel execution introduces its own genuine complexity — coordinating results from multiple simultaneous branches, and handling a partial failure in one branch while others succeed — making it a deliberate architectural choice, not a free, automatic improvement.

The Metaphor, Fully Extended

The Kitchen	Parallel Execution Concept
Independent dishes for different tables cooking simultaneously	Independent subtasks in a plan executing simultaneously
Coordinating everything to arrive together despite parallel preparation	Coordinating results from multiple simultaneous execution branches
A kitchen that’s identified which dishes genuinely don’t depend on each other	An agent that’s identified which subtasks genuinely don’t depend on each other
Dramatically faster service through genuine, coordinated parallelism	Dramatically faster task completion through genuine, coordinated parallel execution

For Beginners: What to Actually Do

Practice identifying genuinely independent steps within a multi-step plan, distinguishing them from steps with real, necessary sequential dependencies.
Learn the basic pattern for running parallel subtasks and then coordinating their results back into a single, combined outcome.
Get comfortable with the added complexity parallel execution introduces, particularly around handling a partial failure in one parallel branch.

For Practitioners and Leaders: The Deeper Layer

Evaluate your agentic system’s plans specifically for genuine parallelization opportunities, particularly for research or information-gathering tasks.
Weigh the latency benefits of parallel execution against its added architectural complexity and failure-handling requirements.
Build explicit handling for partial failures in parallel execution, connecting directly to the error handling covered in Article 12.

Quick Recap

Parallel execution runs independent subtasks in an agentic plan simultaneously, rather than strictly sequentially.
This can meaningfully reduce overall task completion time for plans containing genuinely independent steps.
Agent frameworks increasingly support this pattern directly, connecting to the dependency analysis in planning.
Parallel execution introduces genuine added complexity around coordination and partial failure handling.

Where This Fits in the Series

Article 11 covered running independent work simultaneously. Article 12 covers what happens when one of those tickets comes back wrong.