Workflow from spec

You are designing a complete AG2 multi-agent workflow from a user's specification.

Instructions

1. Ask the user to describe their workflow goal in plain language. Then analyze:

   • How many distinct roles/specializations are needed?
   • Do agents need to collaborate (group chat) or work in sequence (pipeline)?
   • Does any agent need external tools or API access?
   • What's the expected input and output?

2. Select the orchestration pattern:

Pattern Selection Guide

Scenario	Pattern	Why
Agents need to discuss and build on each other's ideas	Group Chat (auto)	LLM picks best next speaker
Fixed processing pipeline (A -> B -> C)	Sequential	Predictable flow
Each agent works independently, results merged	Fan-out/Fan-in	Parallel processing
One coordinator delegates to specialists	Hub-and-spoke	Central routing
Iterative refinement (draft -> review -> revise)	Two-agent loop	Focused improvement

3. Generate the full implementation:

Two-Agent Loop (Iterative Refinement)

from autogen import ConversableAgent

creator = ConversableAgent(
    name="Creator",
    system_message="You draft content based on requirements. Incorporate feedback from the Reviewer.",
    llm_config={"model": "gpt-4o-mini"},
)

reviewer = ConversableAgent(
    name="Reviewer",
    system_message="""You review drafts critically.
- If the draft meets requirements, respond with: APPROVE
- Otherwise, provide specific, actionable feedback""",
    llm_config={"model": "gpt-4o-mini"},
)

result = creator.initiate_chat(
    reviewer,
    message="Create a [specification here]",
    max_turns=4,  # Max 2 revision cycles
)

Hub-and-Spoke (Coordinator + Specialists)

from autogen import ConversableAgent

coordinator = ConversableAgent(
    name="Coordinator",
    system_message="""You coordinate work between specialists.
- Analyze the request and determine which specialist(s) to engage
- Synthesize responses from specialists into a final answer
- Available specialists: [list them]""",
    llm_config={"model": "gpt-4o-mini"},
)

specialist_a = ConversableAgent(
    name="SpecialistA",
    system_message="You handle [domain A]. Respond only about your expertise.",
    llm_config={"model": "gpt-4o-mini"},
)

specialist_b = ConversableAgent(
    name="SpecialistB",
    system_message="You handle [domain B]. Respond only about your expertise.",
    llm_config={"model": "gpt-4o-mini"},
)

# Use nested chats for hub-and-spoke
coordinator.register_nested_chats(
    [
        {"recipient": specialist_a, "max_turns": 1, "summary_method": "last_msg"},
        {"recipient": specialist_b, "max_turns": 1, "summary_method": "last_msg"},
    ],
    trigger=lambda sender: True,  # Always consult specialists
)

Key Rules

• Start with the simplest pattern that solves the problem
• Prefer 2-3 agents over 5+ agents -- complexity grows fast
• Every agent must have a clear termination condition
• Test with a simple input before adding complexity
• Document the workflow with a diagram in comments

4. After generating, walk through the data flow to verify correctness.