Operator-ready prompt for reuse, tuning, and workspace runs.
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Inspect first, copy once, then fork into Workspace when you want variants, notes, and model settings attached to the same run.
Swap domain facts, examples, and any hard-coded entities for your own context.
Tighten the evidence or verification requirement if this is headed toward production.
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Structured source with 7 active lines to adapt.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Design the AutoGen agent team structure for the legal tech valuation challenge. Define at least three distinct agent roles (e.g., 'Market Analyst', 'Financial Reviewer', 'Legal Innovator'), their responsibilities, communication patterns, and how they will collaborate. Provide the Python code to initialize these agents and the GroupChat orchestrator. ```python
import autogen config_list = [ { "model": "gemini-3-flash", # Replace with your actual Gemini 3 Flash model identifier "api_key": autogen.Env.get("GEMINI_API_KEY"), "api_type": "google", # For Google models "api_base": "https://generativelanguage.googleapis.com/v1beta" }
] llm_config = { "timeout": 60, "cache_seed": 42, "config_list": config_list, "temperature": 0
} # Your agent definitions here
market_analyst = autogen.AssistantAgent( name="Market_Analyst", llm_config=llm_config, system_message="You are an expert market analyst specializing in the legal tech sector..."
) # ... (define other agents) # Your GroupChat and manager setup here
```Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Preserve the role framing, objective, and reporting structure so comparison runs stay coherent.
Swap in your own domain constraints, anomaly thresholds, and examples before you branch variants.
Check whether the prompt asks for the right evidence, confidence signal, and escalation path.
Copy once for a pristine source snapshot, then move the prompt into Workspace when you want variants, run history, and side-by-side tuning without losing the original.
Prompt diagnostics
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This prompt already mixes executable detail with instructions, so the safest path is to tune examples and interfaces before you rewrite the overall scaffold.
AutoGen Multi-Agent Legal Tech Valuator with Gemini 3 Flash
Develop a sophisticated multi-agent system using Microsoft's AutoGen framework to perform automated due diligence and valuation analysis for emerging AI legal software startups, inspired by Legora's recent funding. This system will leverage specialized agents to research market trends, analyze financial documents, identify legal tech innovations, and synthesize comprehensive valuation reports. The focus is on orchestrating autonomous agents that can collaboratively perform complex tasks, with human oversight at key decision points, ensuring accuracy and compliance in a high-stakes environment. Utilize Gemini 3 Flash for advanced multimodal understanding and reasoning, Upstage for data preparation, and ElevenLabs for voice-enabled executive summaries.
Use the challenge page to recover the original task boundaries before you tune the prompt. That keeps your variants grounded in the same evaluation target instead of drifting into a different problem.