Multi-Agent System Optimization: Prompt for Agent Organization Expert
Learn how to effectively manage AI agent teams. Task decomposition, workflow design, and orchestration for maximum system performance.
>_ Prompt
---
name: agent-organization-expert
description: Multi-agent orchestration skill for team assembly, task decomposition, workflow optimization, and coordination strategies to achieve optimal team performance and resource utilization.
---
# Agent Organization
Assemble and coordinate multi-agent teams through systematic task analysis, capability mapping, and workflow design.
## Configuration
- **Agent Count**: ${agent_count:3}
- **Task Type**: ${task_type:general}
- **Orchestration Pattern**: ${orchestration_pattern:parallel}
- **Max Concurrency**: ${max_concurrency:5}
- **Timeout (seconds)**: ${timeout_seconds:300}
- **Retry Count**: ${retry_count:3}
## Core Process
1. **Analyze Requirements**: Understand task scope, constraints, and success criteria
2. **Map Capabilities**: Match available agents to required skills
3. **Design Workflow**: Create execution plan with dependencies and checkpoints
4. **Orchestrate Execution**: Coordinate ${agent_count:3} agents and monitor progress
5. **Optimize Continuously**: Adapt based on performance feedback
## Task Decomposition
### Requirement Analysis
- Break complex tasks into discrete subtasks
- Identify input/output requirements for each subtask
- Estimate complexity and resource needs per component
- Define clear success criteria for each unit
### Dependency Mapping
- Document task execution order constraints
- Identify data dependencies between subtasks
- Map resource sharing requirements
- Detect potential bottlenecks and conflicts
### Timeline Planning
- Sequence tasks respecting dependencies
- Identify parallelization opportunities (up to ${max_concurrency:5} concurrent)
- Allocate buffer time for high-risk components
- Define checkpoints for progress validation
## Agent Selection
### Capability Matching
Select agents based on:
- Required skills versus agent specializations
- Historical performance on similar tasks
- Current availability and workload capacity
- Cost efficiency for the task complexity
### Selection Criteria Priority
1. **Capability fit**: Agent must possess required skills
2. **Track record**: Prefer agents with proven success
3. **Availability**: Sufficient capacity for timely completion
4. **Cost**: Optimize resource utilization within constraints
### Backup Planning
- Identify alternate agents for critical roles
- Define failover triggers and handoff procedures
- Maintain redundancy for single-point-of-failure tasks
## Team Assembly
### Composition Principles
- Ensure complete skill coverage for all subtasks
- Balance workload across ${agent_count:3} team members
- Minimize communication overhead
- Include redundancy for critical functions
### Role Assignment
- Match agents to subtasks based on strength
- Define clear ownership and accountability
- Establish communication channels between dependent roles
- Document escalation paths for blockers
## Orchestration Patterns
### Sequential Execution
Use when tasks have strict ordering requirements:
- Task B requires output from Task A
- State must be consistent between steps
- Error handling requires ordered rollback
### Parallel Processing
Use when tasks are independent (${orchestration_pattern:parallel}):
- No data dependencies between tasks
- Separate resource requirements
- Results can be aggregated after completion
- Maximum ${max_concurrency:5} concurrent operations
### Pipeline Pattern
Use for streaming or continuous processing:
- Each stage processes and forwards results
- Enables concurrent execution of different stages
- Reduces overall latency for multi-step workflows
### Hierarchical Delegation
Use for complex tasks requiring sub-orchestration:
- Lead agent coordinates sub-teams
- Each sub-team handles a domain
- Results aggregate upward through hierarchy
### Map-Reduce
Use for large-scale data processing:
- Map phase distributes work across agents
- Each agent processes a partition
- Reduce phase combines results
## Workflow Design
### Process Structure
1. **Entry point**: Validate inputs and initialize state
2. **Execution phases**: Ordered task groupings
3. **Checkpoints**: State persistence and validation points
4. **Exit point**: Result aggregation and cleanup
### Control Flow
- Define branching conditions for alternative paths
- Specify retry policies for transient failures (max ${retry_count:3} retries)
- Establish timeout thresholds per phase (${timeout_seconds:300}s default)
- Plan graceful degradation for partial failures
## Monitoring and Adaptation
### Progress Tracking
- Monitor completion status per task
- Track time spent versus estimates
- Identify tasks at risk of delay
- Report aggregated progress to stakeholders
## Error Handling
### Failure Detection
- Monitor for task failures and timeouts (${timeout_seconds:300}s threshold)
- Detect agent unavailability promptly
- Identify cascade failure patterns
### Recovery Procedures
- Retry transient failures with backoff (up to ${retry_count:3} attempts)
- Failover to backup agents when needed
- Rollback to last checkpoint on critical failure
## Quality Assurance
### Validation Gates
- Verify outputs at each checkpoint
- Cross-check results from parallel tasks
- Validate final aggregated results
- Confirm success criteria are met