AI workflows are patterns that determine how multiple AI agents interact, coordinate, and combine their outputs. Based on established computer science paradigms for distributed computing and multi-agent systems, these workflows enable more powerful, reliable, and nuanced results than single-agent approaches.
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
Best For: Content refinement, multi-stage reviews, progressive improvement, quality assurance
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
Best For: Complex problems with distinct components, document analysis, multi-faceted research, strategic planning
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
Best For: Content creation, decision validation, risk assessment, creative projects with critical requirements
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
Best For: Classification tasks, objective decision making, option evaluation, risk mitigation in high-stakes scenarios
| Features | Sequential Refinement | Divide & Conquer | Creator & Critic | Majority Voting |
|---|---|---|---|---|
| Complexity Handling | Medium | High | Medium | Low |
| Critical Feedback | Limited | Limited | Extensive | Moderate |
| Parallelization | No | Yes | Partial | Yes |
| Quality Improvement | High | Moderate | High | Moderate |
| Processing Speed | Slow | Fast | Medium | Fast |
| Decision Confidence | Medium | Medium | Medium | High |
| Content Creation | Excellent | Good | Excellent | Poor |
AI workflows build upon decades of computer science research and distributed systems theory.
Sequential Refinement implements the classic pipeline architecture pattern, where output from one process becomes input to the next. This pattern optimizes for progressive refinement and specialization at each stage.
Key Theory: Data flow models, Unix pipe principles, functional composition
The Divide & Conquer workflow operationalizes recursive problem-solving approaches that break complex problems into simpler subproblems, solve each independently, and combine results.
Key Theory: Recursive algorithms, MapReduce, parallel processing frameworks
Creator & Critic workflows implement principles from adversarial systems where one component creates while another evaluates, similar to GAN architecture in machine learning.
Key Theory: Generative Adversarial Networks, red teaming, iterative refinement
Majority Voting leverages ensemble theory from machine learning, where multiple models contribute to a decision, often outperforming any single model.
Key Theory: Voting classifiers, wisdom of crowds, Bayesian model averaging
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