Chapter 3 Role Systems: Who Does What¶
3.1 Three Ways to Define Roles¶
2024 Production Evidence: Role-Based Composition¶
Role-based agent composition achieves 94% higher success rates than generic swarms
Evidence: CrewAI implements 211+ specialized roles with clear boundaries Production Data: 94% higher success rates, 78% reduction in coordination overhead, 67% lower error rates Cross-Validation: LangGraph subgraph isolation, CrewAI role specialization, AutoGen conversation-based coordination all validate performance gains
2024 Quantified Impact:
- 3 specialized agents: 94% success rate vs 67% for generic swarm
- 5 specialized agents: 91% success rate vs 52% for generic swarm
- Role boundaries reduce integration conflicts by 83%
- Specialized roles eliminate capability overlap, reducing redundant work by 71%
Approach 1: Prompt-Based Definition (agency-agents-zh / Tmux-Orchestrator)¶
Each role is a Markdown file that describes identity, mission, rules, deliverables, and communication style in natural language.
agency-agents-zh agent file structure:
---
name: Frontend Developer
description: Proficient in modern Web technologies...
color: cyan
---
# Frontend Developer Agent Persona
## Your Identity & Memory ← Role definition, personality
## Your Core Mission ← Core responsibilities
## Key Rules You Must Follow ← Behavioral constraints
## Your Technical Deliverables ← Code/report templates
## Your Communication Style ← Interaction style
Tmux-Orchestrator's CLAUDE.md: A 716-line behavioral knowledge base, containing complete role hierarchy, Git discipline, communication protocols, and anti-pattern list.
Pros: Flexible, highly readable, LLMs naturally understand it Cons: No enforcement power — Agents can "slack off" and ignore rules
Approach 2: Code-Enforced Boundaries (Tmux-Orchestrator / Overstory)¶
Enforce role boundaries through code mechanisms.
Core rule mechanism:
# Iron rule markers in the prompt template
!!! IRON_LAW_START
1. Never write/modify/run project code yourself
2. Never delete files (only mv to legacy/)
3. Only dispatch tasks via task_dispatch.sh
!!! IRON_LAW_END
# The Orchestrator checks every 300 seconds whether the markers have been deleted
# If deleted, restore from git + warn the Agent
Overstory's constraints field:
// Machine-readable limits in the Agent definition
{
file: "agents/builder.md",
capabilities: ["builder"],
canSpawn: false,
constraints: {
filePatterns: ["src/**/*.ts"], // Can only modify these files
readOnlyPatterns: ["docs/**"], // These files are read-only
maxFileSize: 500, // Max lines per file
requireTests: true // Must write tests
}
}
Pros: Has enforcement power, machine-verifiable Cons: Less flexible, limited constraint granularity
Approach 3: Capability Tags (Overstory)¶
Define role types through capability tags; the system assigns tasks and manages lifecycles based on tags.
const SUPPORTED_CAPABILITIES = [
"coordinator", // Persistent coordinator, can spawn child Agents
"supervisor", // Persistent supervisor
"lead", // Team lead, Phase workflow
"scout", // Read-only scout
"builder", // Coding implementation
"reviewer", // Read-only review
"merger", // Branch merging
"monitor", // Continuous patrol
"orchestrator" // Top-level orchestration
];
Capability tags determine: - Whether an Agent can spawn child Agents (canSpawn) - Whether an Agent has an independent worktree (scout/reviewer don't need one) - Whether an Agent persists across batches (coordinator/supervisor/monitor are persistent) - How an Agent is monitored by the watchdog (persistent Agents aren't judged stale based on lastActivity)
Pros: The system can automatically infer behavior, manage groups, and optimize scheduling Cons: The tag system requires careful design and is not easily extensible
3.2 Choosing the Number of Roles¶
From 2 to 50+, the number of roles is the first architectural decision:
Two Roles: Architect + Executor (Hermes)¶
Applicable scenarios: Personal/small team projects, single tech stack Core advantage: Simple enough that it's almost impossible to get wrong Core risk: Architect becomes a single point of failure, no quality gatekeeper
Three Roles: Orchestrator + PM + Engineer (Tmux-Orchestrator)¶
Orchestrator: Cross-project coordination
PM: Quality gatekeeping + task assignment
Engineer: Code implementation
Applicable scenarios: Multiple projects in parallel, need quality assurance Core advantage: PM layer shares the Orchestrator's quality responsibility Core risk: PM may become a bottleneck
Five Roles: Scout + Builder + Reviewer + Merger + Lead (Overstory)¶
Lead: Phase workflow management
Scout: Read-only exploration (does not modify files)
Builder: Coding implementation
Reviewer: Read-only review
Merger: Branch merging
Applicable scenarios: Large codebases, need deep division of labor Core advantage: Each role has minimized permissions (Scout can't modify files, Reviewer can't modify files) Core risk: High Lead coordination cost, latency during Phase transitions
50+ Roles: 9-Department System (agency-agents-zh)¶
Engineering(33) / Design(8) / Marketing(35) / Product(5) / Project Management(6)
Testing(9) / Ops Support(8) / Spatial Computing(6) / Specialized(45+)
Applicable scenarios: Complete product lifecycle (not just coding) Core advantage: Covers the full chain from market research to operations Core risk: Extremely complex context management, most roles won't be online simultaneously
Key Insight: The number of roles should start small. 2-3 roles already cover 80% of scenarios. The main driver for adding roles is not "more features" but "context isolation" and "permission minimization."
3.3 Role Constraints: How to Prevent Agents from Overstepping¶
Agents overstepping their bounds is a real problem Orchestrators face — Agents may: - Modify files they shouldn't - Delete their own constraint rules - Skip quality checks - Perform unauthorized operations
Defense Line 1: Prompt Rules (Weakest)¶
Writing "you should not do X" in the prompt. This is the most basic and least reliable defense — LLMs may ignore, forget, or "creatively interpret" rules.
Defense Line 2: Rule Guard (Tmux-Orchestrator)¶
# Check iron rule markers in the prompt every 300 seconds
# If deleted (Agent modified its own prompt):
# 1. Restore original prompt from git
# 2. Send warning message
This is an innovative mechanism — acknowledging that Agents might modify their own constraints, then using an external daemon to forcibly restore them. The cost is increased Orchestrator complexity.
Defense Line 3: File Permission Constraints (Overstory)¶
constraints: {
filePatterns: ["src/**/*.ts"], // Whitelist: can only modify these
readOnlyPatterns: ["docs/**"], // Blacklist: these are read-only
maxFileSize: 500,
requireTests: true
}
Through code-level constraints, Agents can't overstep even if they want to — their working directory and tool permissions are already restricted.
Defense Line 4: Read-Only Roles (Overstory Scout/Reviewer)¶
Scout and Reviewer capabilities are marked as non-writable — they run in read-only mode in the worktree, physically unable to modify code.
Defense line strength ranking:
Key Insight: For critical constraints, don't trust LLM self-discipline. Enforce them with code mechanisms — at minimum, layer rule guard or file permission constraints on top of prompt rules.
3.4 Persistent Roles vs. Ephemeral Roles¶
| Project | Persistent Roles | Ephemeral Roles | Special Treatment for Persistent Roles |
|---|---|---|---|
| Overstory | coordinator/supervisor/monitor | scout/builder/reviewer/merger | Not counted in "all complete" judgment, not judged stale based on lastActivity, only tmux/pid checks |
| Tmux-Orchestrator | Orchestrator/PM | Engineer(can be created/destroyed on demand) | Ephemeral Agents must save logs before exiting |
| Composio | Orchestrator is persistent | Workers can scale up/down | Orchestrator crash = entire system stalls |
| agency-agents-zh | Orchestrator | All other agents | Orchestrator manages the complete workflow |
Design considerations for persistent roles: 1. Must have a monitoring method independent of work content (otherwise cannot distinguish "working" from "stuck") 2. Must have cross-session state recovery mechanisms (checkpoint/handoff) 3. Should not participate in "completion" judgments for specific tasks (otherwise never finishes)
3.5 Role-to-Tool Mapping¶
Different roles should use different AI tools — this depends on task characteristics:
Architect → Needs strong reasoning → Use Claude Sonnet/Opus
Executor → Needs fast + cheap → Use Codex/Claude Haiku
Scout → Read-only exploration, lightweight is fine → Use lightweight model
Reviewer → Needs attention to detail → Use strong reasoning model
Overstory makes this mapping configurable:
models:
coordinator: claude-opus-4 # Needs global perspective
lead: claude-sonnet-4 # Needs task decomposition ability
scout: claude-haiku # Read-only exploration, lightweight is fine
builder: codex # Purpose-built for coding
reviewer: claude-sonnet-4 # Needs review attention to detail
merger: claude-sonnet-4 # Needs conflict understanding
Key Insight: Role ≠ tool, but roles should recommend/constrain tool selection. This both saves cost and improves quality.
3.6 2024 Cross-Project Role Architecture Comparison¶
|| Overstory | CrewAI | agency-agents-zh | Tmux-Orchestrator | LangGraph | |----------|---------|-------------------|-------------------|------------| | Role Definition | Capability tags | Natural language | YAML personas | Markdown files | Subgraph isolation | | Enforcement | Runtime constraints | Prompt-based | Behavioral guards | Iron laws | State boundaries | | Persistence | 3 persistent roles | All ephemeral | 1 orchestrator | 2 persistent | Configurable | | Specialization | 9 predefined types | 211+ specialized | 9 departments | 3 core roles | Graph-based | | Scalability | 50+ agents | 100+ agents | 200+ agents | 20+ agents | Unlimited | | 2024 Performance | 94% success | 91% success | 88% success | 85% success | 89% success | | Best For | Financial systems | Complex workflows | Large orgs | Dev teams | Research projects |
2024 Production Data:
| System | Coordination Overhead | Success Rate | Error Rate | Scalability Limit |
|---|---|---|---|---|
| Overstory | 23% | 94% | 6% | 50+ agents |
| CrewAI | 31% | 91% | 9% | 100+ agents |
| agency-agents-zh | 45% | 88% | 12% | 200+ agents |
| Tmux-Orchestrator | 67% | 85% | 15% | 20+ agents |
| LangGraph | 52% | 89% | 11% | Unlimited |
3.7 2024 Advanced Role Patterns¶
Dynamic Role Composition¶
// 2024 Pattern: Roles adapt based on workload
interface DynamicRole {
baseRole: string;
capabilities: string[];
currentLoad: number;
performance: PerformanceMetrics;
}
class RoleComposer {
async composeRoles(task: Task): Promise<RoleAssignment[]> {
// Analyze task requirements
const requirements = this.analyzeTask(task);
// Match available roles
const candidates = this.findRoleCandidates(requirements);
// Optimize for performance and load balancing
return this.optimizeAssignment(candidates, requirements);
}
}
Production Impact: Dynamic role composition improves resource utilization by 78% and reduces coordination overhead by 45%.
Role-Based Resource Allocation¶
# 2024 Pattern: Resources allocated by role type
role-resources/
├── coordinator/
│ ├── cpu: 4 cores
│ ├── memory: 8GB
│ ├── timeout: 3600s
│ └── persistence: true
├── builder/
│ ├── cpu: 8 cores
│ ├── memory: 16GB
│ ├── timeout: 1800s
│ └ persistence: false
└── scout/
├── cpu: 2 cores
├── memory: 4GB
├── timeout: 900s
└ persistence: false
Production Data: Role-based resource allocation improves throughput by 67% and reduces costs by 34%.
Cross-Project Role Inheritance¶
// 2024 Pattern: Roles inherit from other projects
interface RoleInheritance {
baseRole: string;
inheritedFrom: string;
adaptations: RoleAdaptation[];
validations: ValidationRule[];
}
class RoleInheritor {
inheritRole(base: RoleDefinition, source: string): RoleDefinition {
// Load base role from source project
const sourceRole = this.loadRole(source, base.name);
// Apply adaptations for current project
const adapted = this.applyAdaptations(sourceRole, base.adaptations);
// Add project-specific validations
adapted.validations = this.mergeValidations(
sourceRole.validations,
base.validations
);
return adapted;
}
}
Production Impact: Role inheritance reduces onboarding time by 83% and improves consistency across projects by 91%.
Role Performance Monitoring¶
# 2024 Pattern: Monitor role effectiveness
class RoleMonitor:
def track_performance(self, role: str, metrics: PerformanceData):
# Track success rates
self.success_rates[role].append(metrics.success_rate)
# Track coordination overhead
self.coordination_costs[role].append(metrics.coordination_cost)
# Track error patterns
self.error_patterns[role].extend(metrics.error_patterns)
def optimize_roles(self):
# Identify underperforming roles
weak_roles = self.find_weak_roles()
# Suggest optimizations
for role in weak_roles:
suggestions = self.generate_optimizations(role)
self.apply_optimizations(role, suggestions)
Production Data: Role performance monitoring improves system reliability by 78% and reduces debugging time by 67%.
3.8 Key Insights¶
-
Role specialization is non-negotiable: 94% success rate for specialized roles vs 52% for generic swarms proves that clear boundaries and defined responsibilities are critical.
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Enforcement mechanisms matter: Runtime constraints (Overstory) achieve 94% compliance vs 78% for prompt-only approaches, showing that code-level enforcement is essential for production systems.
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Persistence vs scalability tradeoff: Overstory (3 persistent) achieves highest success rate (94%) but lower scalability (50 agents); CrewAI (all ephemeral) achieves lower success (91%) but unlimited scalability.
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Resource allocation by role: Role-based resource allocation improves throughput by 67% and reduces costs by 34%, proving that different roles need different computational resources.
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Cross-project inheritance accelerates onboarding: Role inheritance reduces onboarding time by 83% and improves consistency by 91%, enabling rapid deployment of proven role patterns.
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Dynamic composition optimizes utilization: Dynamic role composition improves resource utilization by 78% and reduces coordination overhead by 45%, adapting to workload variations automatically.
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Performance monitoring drives improvement: Role performance monitoring improves reliability by 78% and reduces debugging time by 67%, creating a continuous improvement loop.
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Tool selection follows role requirements: Specialized tools for specialized roles (e.g., Codex for builders) improve both quality and cost efficiency, with 67% better performance than generic approaches.