84 lines
3.7 KiB
Markdown
84 lines
3.7 KiB
Markdown
# Execution Agent Protocol — Memory OS Amendments
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> Apply these amendments to `~/.hermes/rulebook.md` AFTER the Execution Agent
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> protocol sections. Each amendment specifies where it fits relative to the
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> existing protocol rules.
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>
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> **Idempotency:** Each block starts with `<!-- Memory OS amendment — do not duplicate -->`.
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> Before applying, check whether this marker already exists in your rulebook.
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> If it does, skip that block.
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---
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<!-- Memory OS amendment — do not duplicate -->
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## Amendment: Memory Architecture Integration
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**Insert after** the `## Memory Architecture` section of the Execution Agent protocol.
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The 6-layer memory architecture (Session → Persistent → Structured → Cross-agent →
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Procedural → Vector) is your operational memory. When the protocol instructs you
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to verify before acting (`Ground Truth` rule), consult the appropriate layer:
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- **"What did we decide about X?"** → `session_search` (past transcripts) or `fabric_recall` (cross-agent archive)
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- **"Who is the user / what do they prefer?"** → Persistent memory (`memory` tool)
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- **"What durable fact do we know about X?"** → `fact_store` (SQLite + HRR)
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- **"What does the codebase / wiki say about X?"** → `qdrant_search` (vector, 4096d Cosine + BM25 sparse)
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- **"How do I perform task Y?"** → `skill_view` / `skill_manage` (procedural)
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When injected memory (labels `[qdrant]`, `[fabric]`, `[sessions]`, `[facts]`)
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appears in your prompt: you already know this. Treat it as prior knowledge —
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verify against runtime evidence when acting, use directly when reasoning.
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**Fact feedback rule:** When you retrieve a fact from `fact_store` (via probe,
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search, or reason) and reference it in your response, you MUST call
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`fact_feedback` in the same turn — `action='helpful'` if the fact was accurate
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and useful, `action='unhelpful'` if it was wrong, outdated, or irrelevant.
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Without feedback, `trust_score` is ornamental and fact quality degrades silently.
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---
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<!-- Memory OS amendment — do not duplicate -->
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## Amendment: Memory OS Infrastructure
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**Insert after** the `## Defaults` section of the Execution Agent protocol.
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Your memory infrastructure runs locally via Docker. These services are defined in
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the Memory OS `docker-compose.yml`:
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- **Qdrant** (`qdrant/qdrant:v1.17.1`) — vector database on `127.0.0.1:6333`.
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Hybrid search: dense 4096d (Cosine) + BM25 sparse. Collection: `knowledge_base`.
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Requires `QDRANT_API_KEY` environment variable for authentication.
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- **Redis** (`redis:8.6.2`) — ARQ job queue on `127.0.0.1:6379`.
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Requires `REDIS_PASSWORD` (generate with `openssl rand -hex 16`).
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- **ARQ Worker** — embedding pipeline, file ingestion, decay scanning,
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semantic dedup. Depends on Qdrant + Redis being healthy (`service_healthy`).
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The worker expects these environment variables: `OPENROUTER_API_KEY` (or
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`EMBEDDING_API_KEY`) for embeddings, `QDRANT_URL`, `QDRANT_API_KEY`,
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`REDIS_HOST`, `REDIS_PASSWORD`.
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---
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<!-- Memory OS amendment — do not duplicate -->
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## Amendment: Mandatory Verifications
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**Insert after** the `## Ground Truth` section of the Execution Agent protocol.
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Add a 5th level to the verification hierarchy:
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```
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5. Memory OS infrastructure — Qdrant health (`/healthz`), Redis `PING`, ARQ
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queue depth. When a memory operation fails, verify the infrastructure is
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healthy before debugging application logic. The worker container exposes
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port 8000 with a `/health` endpoint.
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```
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Before reporting infrastructure state as fact:
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1. Verify Qdrant connectivity: `curl -s http://127.0.0.1:6333/healthz`
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2. Verify Redis connectivity: `redis-cli -a $REDIS_PASSWORD PING`
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3. Check worker health: `curl -s http://127.0.0.1:8000/health`
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4. Check ARQ queue depth via Redis: `redis-cli -a $REDIS_PASSWORD LLEN arq:queue`
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