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Implement Tool-Use for Data Integration

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Linked challenge: Multi-Agent System for Internal Security Anomaly Detection

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7
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1
Linked challenge
Multi-Agent System for Internal Security Anomaly Detection

Prompt source

Original prompt text with formatting preserved for inspection.

7 lines
1 sections
No variables
1 code block
Develop custom Python tools that AutoGen agents can use to simulate access to internal logs (e.g., reading from a JSON file representing logs), fetching external news articles (e.g., a mock news API), and analyzing code (e.g., CodeRabbit-inspired static analysis for unusual commits). Ensure these tools are robust and return structured outputs that o4-mini agents can interpret. Describe how you will integrate these tools into your AutoGen agents. Consider how FLAML could optimize agent workflows that depend on tool outputs. ```python
# Example tool function
def get_access_logs(user_id: str, date_range: tuple) -> str: # Simulate fetching logs return f"Simulated logs for {user_id} on {date_range}" # How to register a tool with an agent in AutoGen
# agent_instance.register_for_llm(name="get_access_logs", description="Get access logs for a user.")(get_access_logs)
# Or for UserProxyAgent
# agent_instance.register_function(function_map={"get_access_logs": get_access_logs})
```

Adaptation plan

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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.