The governed
agentic runtime.

fabric_kb is the durable ground truth; the RAG index is a derived overlay rebuilt from it. The store is chosen once at deployment and validated by deployment_config (it accepts only (local, local) or (hybrid, postgres) — anything else fails closed). Local mode keeps everything on-device: a JSONL store, on-device nomic-embed-text embeddings, brute-force cosine, and atomic index promotion (build to a temp file, then swap) so a half-built index is never read — it never auto-syncs to a hosted store. Hybrid mode stores vectors in PostgreSQL + pgvector under an EmbeddingContract that pins model/dimensions/normalization, with every query carrying a QueryScope under row-level security so a workspace only ever sees its own rows — fail-closed, with tombstoned revocation and a sampled re-embed audit. The caller never knows which store answered. See the RAG-modes diagram and ADR-0001.

Keep canonical sources current, classify them into the correct workspace/domain, avoid duplicate snapshots, and let Fabric select context. Do not paste secrets, entire repositories or every skill into a request. More context is not automatically more intelligence: relevant, diverse and sufficiently complete evidence produces better results than maximum volume.

An explicit valid capability pin wins. Otherwise Fabric considers the requested lane, model kind, provider readiness, policy eligibility, benchmark results and recorded reliability. In the default hybrid profile, local handles low-latency/private work while the configured cloud specialist handles eligible reasoning and coding. If the chosen model fails, Fabric retries within its governed limits and may walk policy-allowed, capability-equivalent fallbacks; deterministic validators and human approvals remain authoritative regardless of which model answered.

FABRIC_AI_EXECUTION_POLICY supplies the startup default. Administrators can enforce it with FABRIC_AI_EXECUTION_POLICY_LOCK=1, which prevents the Settings UI from changing the mode.

gemma3:12b (fast) · qwen2.5:72b (reason) · qwen3-coder-next (code) · qwen2.5vl:32b (vision) · nomic-embed-text (embed, pinned & required). The cloud specialist covers reason · code · vision. Every model is a plugin behind the same capability contract — see the model-routing diagram.

They're complementary halves of the same brain. RAG knowledge is reference: indexed documents, sheets, wikis and code that Fabric retrieves and cites at reasoning time to ground a plan (“what does the Proving Grounds wiki say the steps are?”). It's untrusted context — it informs a decision, it never becomes an authoritative production value on its own, and a gap is reported rather than invented. Training / learned memory is experience: structured facts Fabric records about what actually happened and what you decided (archetype choices, gate approvals, success rates, corrections, and the notes you teach it), which drive prediction, confidence and calibration. Put simply — RAG answers “what do we know about X?”; learned memory answers “what happened last time we did X, and what did we decide?”. The Train Fabric panel feeds both at once: a document lands in RAG for retrieval, while a note also lands in memory as a durable teaching fact.

Skip secrets, tokens and credentials (Fabric indexes text — treat everything you add as readable). Skip transient chatter, one-off Slack threads, and duplicates of what's already indexed — they add noise the retriever has to fight through. Skip anything you're not allowed to store. If a source is authoritative but sensitive, prefer a link over pasting its contents.