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implementation
Agent Network Initialization
Inspect the original prompt language first, then copy or adapt it once you know how it fits your workflow.
Linked challenge: AutoGen & OpenAI o3 Fraud Detection Agent Network for Hurricane Claims
Format
Code-aware
Lines
8
Sections
4
Linked challenge
AutoGen & OpenAI o3 Fraud Detection Agent Network for Hurricane Claims
Prompt source
Original prompt text with formatting preserved for inspection.
8 lines
4 sections
No variables
0 checklist items
import autogen
config_list = [{'model': 'o3-mini', 'api_key': '<YOUR_KEY>'}]
llm_config = {'config_list': config_list}
# Define Claims Reviewer and Fraud Investigator agents
reviewer = autogen.AssistantAgent(name='ClaimsReviewer', llm_config=llm_config)
investigator = autogen.AssistantAgent(name='FraudInvestigator', llm_config=llm_config)
user_proxy = autogen.UserProxyAgent(name='UserProxy', human_input_mode='NEVER')
# Implement interaction workflow to analyze claim dataset...Adaptation plan
Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.
Keep stable
Hold the task contract and output shape stable so generated implementations remain comparable.
Tune next
Update libraries, interfaces, and environment assumptions to match the stack you actually run.
Verify after
Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.