Multi-Agent Workflows

Four orchestration patterns that route your prompt through multiple AI agents. Each workflow applies a different reasoning structure — sequential refinement, adversarial critique, parallel decomposition, or independent consensus.

01 Sequential Refinement

Passes output through a series of specialized agents, each refining and improving upon the previous agent's work. Based on pipeline architecture patterns in computer science.

CS Theory Foundation

Sequential and pipeline processing models, waterfall development methodology, functional composition

Sequential Refinement

Each model refines the previous output

READYSTEP 0/3

02 Creator & Critic

Employs a two-stage approach where one agent generates content and others provide critical feedback to identify weaknesses, inconsistencies, and areas for improvement.

CS Theory Foundation

Adversarial models, red team/blue team security techniques, iterative design patterns

Creator – Critic

Generate → dual critic feedback → refine

READYITER 0

03 Divide & Conquer

Breaks complex problems into smaller, more manageable sub-problems that are solved independently before being recombined into a comprehensive solution.

CS Theory Foundation

Recursive algorithms, MapReduce paradigm, parallel computing, decomposition techniques

Divide & Conquer

Chunk task → distribute → parallel process → merge

READYPHASE: IDLE

04 Majority Voting

Multiple agents independently evaluate options or generate answers, with the final output determined by consensus or weighted agreement mechanisms.

CS Theory Foundation

Ensemble methods, Byzantine fault tolerance, voting algorithms, consensus protocols

Majority Voting

Independent agents vote on best output

READYVOTES: 0/3

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