m3taversal
681afad506
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Sources merged: - teleo-codex/ops/pipeline-v2/ (11 newer lib files, 5 new lib modules) - teleo-codex/ops/ (agent-state, diagnostics expansion, systemd units, ops scripts) - VPS /opt/teleo-eval/telegram/ (10 new bot files, agent configs) - VPS /opt/teleo-eval/pipeline/ops/ (vector-gc, backfill-descriptions) - VPS /opt/teleo-eval/sync-mirror.sh (Bug 2 + Step 2.5 fixes) Non-trivial merges: - connect.py: kept codex threshold (0.65) + added infra domain parameter - watchdog.py: kept infra version (stale_pr integration, superset of codex) - deploy.sh: codex rsync version (interim, until VPS git clone migration) - diagnostics/app.py: codex decomposed dashboard (14 new route modules) 81 files changed, +17105/-200 lines Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
25 lines
1.3 KiB
Markdown
25 lines
1.3 KiB
Markdown
# Personality layer may need separation from knowledge base
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**Date:** 2026-03-05
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**Status:** noted
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## The Seam
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`core/collective-agent-core.md` and the Personality sections in `agents/{name}/identity.md` are oriented toward the **product experience** — how the agent talks to users, what voice it has, what it says when challenged.
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The rest of teleo-codex is oriented toward the **operational loop** — how agents propose/evaluate claims, the schema structure, the PR workflow.
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Right now both coexist in the same repo. Fine for v1 where Pentagon agents do both jobs (interact AND maintain the knowledge base).
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## When This Becomes a Problem
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When the product separates the chat interface from the knowledge maintenance:
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- The **product prompt** loads personality + searches the knowledge base at runtime
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- The **operational agent** runs the extraction/evaluation loop against the repo
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- These are different contexts with different performance requirements
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At that point, personality documents should live closer to the product (loaded into system prompt), and the knowledge base should be searched (RAG), not loaded wholesale.
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## Not Blocking
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v1 works fine with both in one repo. Flag this when building the product API layer or when the knowledge base grows large enough that loading it all into context is impractical.
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