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Sequential Pipeline Agents(SPA)
Specialized agents process tasks in a linear pipeline where each agent's output becomes the next agent's input
🎯 30-Second Overview
Pattern: Specialized agents process tasks sequentially where each agent's output feeds the next agent's input
Why: Enables expertise specialization, quality control gates, independent optimization, and clear error isolation per stage
Key Insight: Linear agent pipeline with validation checkpoints - each stage optimized for specific capabilities
⚡ Quick Implementation
📋 Do's & Don'ts
🚦 When to Use
Use When
- • Multi-step workflows with distinct phases
- • Tasks requiring specialized expertise per stage
- • Quality-critical processes needing validation gates
- • Workflows where stages can be independently optimized
- • Complex transformations with clear dependencies
Avoid When
- • Simple single-step tasks
- • Highly interactive or conversational flows
- • Real-time systems requiring low latency
- • Tasks requiring dynamic branching or loops
- • When stage dependencies are unclear
📊 Key Metrics
💡 Top Use Cases
References & Further Reading
Deepen your understanding with these curated resources
Academic Papers
Multiagent Systems: A Survey from a Machine Learning Perspective (Stone & Veloso, 2000)
Multiagent Cooperation and Competition with Deep Reinforcement Learning (Tampuu et al., 2017)
Sequential Decision Making in Multi-Agent Systems (Bernstein et al., 2002)
A Review of Cooperative Multi-Agent Deep Reinforcement Learning (OroojlooyJadid & Hajinezhad, 2019)
Contribute to this collection
Know a great resource? Submit a pull request to add it.
Sequential Pipeline Agents(SPA)
Specialized agents process tasks in a linear pipeline where each agent's output becomes the next agent's input
🎯 30-Second Overview
Pattern: Specialized agents process tasks sequentially where each agent's output feeds the next agent's input
Why: Enables expertise specialization, quality control gates, independent optimization, and clear error isolation per stage
Key Insight: Linear agent pipeline with validation checkpoints - each stage optimized for specific capabilities
⚡ Quick Implementation
📋 Do's & Don'ts
🚦 When to Use
Use When
- • Multi-step workflows with distinct phases
- • Tasks requiring specialized expertise per stage
- • Quality-critical processes needing validation gates
- • Workflows where stages can be independently optimized
- • Complex transformations with clear dependencies
Avoid When
- • Simple single-step tasks
- • Highly interactive or conversational flows
- • Real-time systems requiring low latency
- • Tasks requiring dynamic branching or loops
- • When stage dependencies are unclear
📊 Key Metrics
💡 Top Use Cases
References & Further Reading
Deepen your understanding with these curated resources
Academic Papers
Multiagent Systems: A Survey from a Machine Learning Perspective (Stone & Veloso, 2000)
Multiagent Cooperation and Competition with Deep Reinforcement Learning (Tampuu et al., 2017)
Sequential Decision Making in Multi-Agent Systems (Bernstein et al., 2002)
A Review of Cooperative Multi-Agent Deep Reinforcement Learning (OroojlooyJadid & Hajinezhad, 2019)
Contribute to this collection
Know a great resource? Submit a pull request to add it.