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| ``` | |
| (/research -> agent -> research_doc -> (parsing to struct -> agent_run -> serialized_json) -> human_in_loop_verify) | |
| | | |
| | ^ | |
| research.md | |
| # Task: Make a sandwich | | |
| | | |
| ## User Story | | |
| ... | | |
| ## Instructions | | |
| ... | | |
| ## Progress Log | | |
| ... | | |
| ## Review Decision | | |
| ... | | |
| struct: { | |
| "gap": [Gap] | |
| "technical_approach_solving": [Approach] | |
| } | |
| Gap analyzer Agent | |
| System Prompt: "you are an expert at analyzing gaps in the code base, | |
| extract all the gaps from research doc and add | |
| implementation ideas in above format: .... Output | |
| in structured JSON. Present to human for verification | |
| focusing on " | |
| ``` |
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In this toy example — just research — here the idea you start with a very customized doc, e.g. research.md. Then the Gap analyzer Agent which is essentially like your very familiar, sub agent, creates structured data from the parsed document. Why? Because you need to not give the next step — a human — the entire document, just the important stuff. The presumption here ofc is that presenting gaps is the timely and appropriate thing to do, its a design decision by the person making these complex beasts.
These steps can be more complex and repeated multiple times a day, e.g. an AI could arbitrate things and ask more humans about the gaps, because everyone is too busy to look at it, multiple rounds of feedback from humans etc.