Pattern Based Agent
10 multi-agent orchestration patterns including Two-Agent Chat, Auto Pattern, Round Robin, and more.
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Pattern Based Agent · Agent Mesh
Task Analysis
Analyze incoming task requirements and determine optimal pattern.
Key Features
Two-Agent Chat
Direct communication pattern between two specialized agents for focused tasks.
Auto Pattern
Dynamic agent selection based on task requirements and available capabilities.
Round Robin
Sequential task distribution across multiple agents for balanced workload.
Supervisor Pattern
Central coordinator managing task delegation and quality control.
Hierarchical Pattern
Multi-level agent organization with clear reporting and escalation paths.
Swarm Pattern
Decentralized agent collaboration for complex, parallelizable tasks.
How It Works
Task Analysis
Analyze incoming task requirements and determine optimal pattern.
Agent Selection
Select and configure agents based on task needs.
Task Distribution
Distribute tasks across selected agents according to pattern.
Parallel Execution
Agents execute tasks in parallel according to orchestration pattern.
Result Aggregation
Collect and merge results from all participating agents.
Quality Evaluation
Evaluate output quality and determine if reprocessing is needed.
Response Delivery
Deliver final aggregated response to the caller.
Learning & Optimization
Learn from execution to optimize future pattern selections.
Multi-Agent Architecture
Orchestrator
Central coordination agent that manages task distribution and agent lifecycle.
- Task distribution
- Agent management
- Quality control
- Error handling
- Performance monitoring
Worker Agent
Specialized agents that execute specific tasks within the orchestration pattern.
- Task execution
- Result generation
- Status reporting
- Error recovery
- Resource management
Evaluator Agent
Assesses quality of outputs and provides feedback for continuous improvement.
- Quality assessment
- Feedback generation
- Pattern optimization
- Performance tracking
- Recommendation
Router Agent
Intelligently routes tasks to appropriate agents based on capabilities and load.
- Task analysis
- Agent matching
- Load balancing
- Priority handling
- Fallback management
Monitor Agent
Tracks system health, performance metrics, and generates alerts.
- Health monitoring
- Metrics collection
- Alert generation
- Capacity planning
- Optimization
Use Cases
Complex Reasoning
Use multi-agent collaboration for complex reasoning tasks.
Content Generation
Generate high-quality content through specialized agent teams.
Data Processing
Process large datasets in parallel with distributed agent workloads.
Code Review
Multi-perspective code review with specialized review agents.
Research Synthesis
Synthesize information from multiple sources using research agents.
Decision Support
Support complex decisions with multiple expert agent perspectives.