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Technical Explanation

1. Agent Workflow

Describe step-by-step how your agent processes an input:

  1. Receive user input
  2. (Optional) Retrieve relevant memory
  3. Plan sub-tasks (e.g., using ReAct / BabyAGI pattern)
  4. Call tools or APIs as needed
  5. Summarize and return final output

2. Key Modules

  • Planner (planner.py): …
  • Executor (executor.py): …
  • Memory Store (memory.py): …

3. Tool Integration

List each external tool or API and how you call it:

  • Search API: function search(query)
  • Calculator: LLM function calling

4. Observability & Testing

Explain your logging and how judges can trace decisions:

  • Logs saved in logs/ directory
  • TEST.sh exercises main path

5. Known Limitations

Be honest about edge cases or performance bottlenecks:

  • Long-running API calls
  • Handling of ambiguous user inputs