Role-Based Cooperation

Agents with distinct, fixed roles collaborate in structured rounds. Each sees the others' outputs and refines its own contribution.

Best for: C-suite simulations, cross-functional team alignment, product design councils.
LLM calls: N agents × R rounds.

---

Sequence Diagram

sequenceDiagram
    participant CEO
    participant CTO
    participant CFO

    rect rgb(230, 245, 255)
        Note over CEO,CFO: Round 1 — Independent views
        CEO->>CEO: "Strategy: focus on AI products"
        CTO->>CTO: "Architecture: cloud-native microservices"
        CFO->>CFO: "Budget: $2M available for Q1"
    end

    rect rgb(255, 245, 230)
        Note over CEO,CFO: Round 2 — With shared context
        CEO->>CEO: "Adjusted: AI products within $2M budget"
        CTO->>CTO: "Revised: prioritise ML infra over new services"
        CFO->>CFO: "Updated: reallocate 40% of engineering budget to AI"
    end

---

Use Case 1 — Product Strategy Council (Anthropic)

import asyncio
from pyagent_patterns.base import Agent
from pyagent_patterns.structural import RoleBased
from pyagent_providers import AnthropicLLM

pattern = RoleBased(
    agents=[
        Agent(
            "CEO",
            AnthropicLLM("claude-sonnet-4-20250514"),
            system_prompt="You are the CEO. Think about: market positioning, competitive advantage, "
                          "customer value, and long-term strategic direction. "
                          "Be decisive. After reviewing other executives' input, "
                          "update your position if the evidence warrants it.",
        ),
        Agent(
            "CTO",
            AnthropicLLM("claude-sonnet-4-20250514"),
            system_prompt="You are the CTO. Think about: technical feasibility, build vs buy, "
                          "architecture trade-offs, engineering capacity, and technical debt. "
                          "Be honest about what is and isn't possible in the proposed timeline.",
        ),
        Agent(
            "CPO",
            AnthropicLLM("claude-sonnet-4-20250514"),
            system_prompt="You are the CPO. Think about: user needs, product-market fit, "
                          "feature prioritisation, and the roadmap impact of each strategic choice. "
                          "Represent the voice of the customer.",
        ),
        Agent(
            "CFO",
            AnthropicLLM("claude-haiku-3-5-20241022"),
            system_prompt="You are the CFO. Think about: budget constraints, ROI, payback period, "
                          "financial risk, and capital allocation trade-offs. "
                          "Challenge assumptions about projected returns.",
        ),
    ],
    rounds=2,
    shared_context=True,
)

result = asyncio.run(pattern.run(
    "Should we pivot our B2B SaaS product to add an AI-native layer? "
    "Investment required: ~$3M over 12 months. "
    "Competitor just launched a similar feature. "
    "Current ARR: $8M, growth rate: 35% YoY."
))
print(result.output)
print(f"Roles: {result.metadata['roles']}, Rounds: {result.metadata['rounds']}")
print(f"Cost: ${result.cost_estimate:.4f}")

---

Use Case 2 — Engineering Design Review (OpenAI + Anthropic)

from pyagent_providers import OpenAILLM

design_review = RoleBased(
    agents=[
        Agent(
            "architect",
            OpenAILLM("gpt-4o"),
            system_prompt="You are the principal architect. Evaluate system design for: "
                          "scalability, maintainability, security, and alignment with architectural principles. "
                          "Propose specific changes with rationale.",
        ),
        Agent(
            "security_engineer",
            AnthropicLLM("claude-sonnet-4-20250514"),
            system_prompt="You are the security engineer. Review for: "
                          "threat model coverage, authentication/authorisation design, "
                          "data protection, and compliance requirements (GDPR, SOC2). "
                          "Flag every security concern, even theoretical ones.",
        ),
        Agent(
            "staff_engineer",
            OpenAILLM("gpt-4o-mini"),
            system_prompt="You are a staff engineer responsible for implementation. "
                          "Evaluate: implementation complexity, team capability fit, "
                          "testing strategy, and operational concerns. "
                          "Flag anything that will be painful to maintain.",
        ),
    ],
    rounds=2,
    shared_context=True,
)

result = asyncio.run(design_review.run(open("system_design_doc.md").read()))

---

Use Case 3 — Marketing Campaign Review (LiteLLM)

from pyagent_providers import LiteLLM

campaign_team = RoleBased(
    agents=[
        Agent(
            "brand_strategist",
            LiteLLM("anthropic/claude-sonnet-4-20250514"),
            system_prompt="You are the brand strategist. Evaluate for brand alignment, "
                          "tone consistency, and positioning versus competitors.",
        ),
        Agent(
            "performance_marketer",
            LiteLLM("gpt-4o"),
            system_prompt="You are the performance marketer. Evaluate for conversion potential, "
                          "measurability, CAC efficiency, and channel fit.",
        ),
        Agent(
            "legal_reviewer",
            LiteLLM("anthropic/claude-haiku-3.5"),
            system_prompt="You are the legal reviewer. Check for: misleading claims, "
                          "required disclosures, trademark issues, and regulatory compliance.",
        ),
    ],
    rounds=2,
    shared_context=True,
)

result = asyncio.run(campaign_team.run(
    "Review this campaign concept: 'The AI that replaces your entire data team.'"
))

---

OTel Trace Output

Trace: pyagent.pattern.role_based (8.4s, $0.024)
├── Round 1
│   ├── pyagent.agent.CEO (2.1s, claude-sonnet-4-20250514)
│   ├── pyagent.agent.CTO (1.9s, claude-sonnet-4-20250514)
│   ├── pyagent.agent.CPO (2.2s, claude-sonnet-4-20250514)
│   └── pyagent.agent.CFO (0.9s, claude-haiku-3-5-20241022)
└── Round 2 (with shared context)
    ├── pyagent.agent.CEO (1.8s)
    ├── pyagent.agent.CTO (1.6s)
    ├── pyagent.agent.CPO (1.7s)
    └── pyagent.agent.CFO (0.7s)

---

When to Use

Condition	Recommendation
Different stakeholder perspectives genuinely conflict	✅ Use Role-Based
Roles must see and respond to each other's inputs	✅ Use Role-Based
Roles can work entirely independently	❌ Use Fan-Out/Fan-In
You want adversarial positions with a judge	❌ Use Debate
Roles have a strict delegation hierarchy	❌ Use Hierarchical

---

See Also

• Debate — two sides argue assigned positions rather than natural roles
• Hierarchical — structured delegation hierarchy
• Swarm — roles emerge dynamically from neighbour interaction