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Add possible constrained-agent approaches document with GLIA
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6-Documentation/famm/POSSIBLE_CONSTRAINED_AGENT_APPROACHES.md
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6-Documentation/famm/POSSIBLE_CONSTRAINED_AGENT_APPROACHES.md
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# Possible Constrained-Agent Approaches
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## Purpose
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Consolidate the project's prior SmallCode-like approaches and add GLIA as a persistent-memory substrate.
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This document is intentionally named **possible approaches**. It is a routing map, not a final architecture claim. Each approach is a candidate route for shrinking the active computational field while preserving receipts, memory, verification, and Warden boundaries.
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```text
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small/local model or constrained agent
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→ memory recall
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→ context-budget projection
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→ TODO/plan decomposition
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→ patch/logogram mutation
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→ verifier/governor check
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→ adversarial dual tests
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→ FAMM scar/coarsening or promotion
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→ memory update receipt
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```
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## External anchors
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### SmallCode
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```text
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Repository: https://github.com/Doorman11991/smallcode
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Role: constrained local coding agent / execution field shrinker
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```
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SmallCode is a terminal-native coding agent optimized for small local LLMs, especially 7B-20B models. It uses budget-managed context, TODO-file decomposition, patch-first editing, forgiving tool parsing, working memory, verifier/governor logic, early-stop detection, and optional escalation.
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### GLIA
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```text
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Repository: https://github.com/Eshaan-Nair/Glia-AI
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Role: local-first cross-tool persistent memory / recall substrate
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```
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GLIA describes itself as a local-first memory layer that captures AI conversations, builds a searchable knowledge graph, and injects relevant context into new prompts. It has two shared-memory interfaces: a browser extension for chat websites and an MCP server for coding tools. Both read/write the same backend memory store.
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GLIA features especially relevant to this stack:
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```text
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browser extension + MCP server
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shared local memory store
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hybrid retrieval: sentence vectors + chunk vectors + FTS5
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knowledge graph extraction
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HyDE retrieval
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small-to-big retrieval
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surgical sentence trimming
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background indexing
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project isolation
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prune_memory for outdated facts
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SQLite/WAL local mode
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```
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## Consolidated approach map
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| Approach | Role | Existing project analogue | External analogue |
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|---|---|---|---|
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| Hermes field-operator bridge | controlled workflow operator | Hermes / Warden receipts | GLIA MCP + SmallCode skills |
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| FastPatchCheck | fast viability check | local patch smoke test | SmallCode verifier |
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| StructuralAdmissibilityCheck | invariant legitimacy check | Judge structural gate | SmallCode governor |
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| BridgeModel_GlobalGate | hard execution choke point | guarded transition | SmallCode tool routing / GLIA injection guard |
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| BridgeModel_Linter | architecture safety scanner | Warden pre-runtime alert | SmallCode parser repair / GLIA sanitization |
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| GCL Combined Coding Surface | typed coding substrate | encode/mutate/repair/gate/receipt | coding tools + MCP surfaces |
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| Logogram Chirality Route Gate | semantic kernel routing | glyph/code kernel phase | BoneScript / semantic code packets |
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| TraceInvariant direction | session continuity / drift prevention | trace budget + failure ledger | GLIA memory + SmallCode TODO/session state |
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| Anti-FAMM | witness blind-spot adversary | projection-nullspace attack | memory/summary hidden-failure tests |
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| Anti-BraidStorm | hostile crossing adversary | false survivor detection | multi-agent patch convergence tests |
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## Approach A — Memory-first agent substrate
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Use GLIA as the memory substrate.
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```text
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conversation / coding session
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→ store_memory / Save Chat
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→ embeddings + knowledge graph triples
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→ recall_context / auto-injection
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→ project-scoped memory packet
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```
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Project mapping:
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```math
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\Gamma_{\mathrm{GliaMemory}}
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=
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(
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X_{\mathrm{history}},
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\pi_{\mathrm{chunk}},
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W_{\mathrm{recall}},
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R_{\mathrm{inject}},
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I_{\mathrm{decision}},
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G_{\mathrm{project}},
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K,
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\epsilon
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)
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```
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| Packet term | Meaning |
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|---|---|
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| `X_history` | full conversation / project history |
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| `pi_chunk` | chunking, embedding, graph extraction |
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| `W_recall` | retrieved context / graph facts |
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| `R_inject` | prompt or MCP context injection |
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| `I_decision` | remembered project decisions and constraints |
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| `G_project` | project/session isolation guard |
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| `K` | retrieval, storage, and prompt budget cost |
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| `epsilon` | stale, irrelevant, or missing memory residual |
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Warden checks:
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```text
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stale memory
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wrong project memory
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cross-project leakage
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prompt injection in retrieved chunks
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PII leakage
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over-injection / context noise
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memory facts treated as proof
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```
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## Approach B — Execution-first constrained coding agent
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Use SmallCode-like architecture as the execution substrate.
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```text
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raw coding request
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→ budgeted context summary
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→ TODO decomposition
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→ patch-first edit
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→ compile/lint/test verifier
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→ Warden decision
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→ escalation only on hard fail
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```
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Project mapping:
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```math
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\Gamma_{\mathrm{SmallCode}}
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=
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(
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X_{\mathrm{task}},
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\pi_{\mathrm{summary}},
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W_{\mathrm{todo}},
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R_{\mathrm{patch}},
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I_{\mathrm{compile}},
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G_{\mathrm{local}},
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K,
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\epsilon
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)
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```
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Useful when:
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```text
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local models are weaker than frontier models
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context is limited
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whole-file rewrites are risky
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patches can be locally verified
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session state must survive across turns
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```
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Warden checks:
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```text
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patch compiles but violates global invariant
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summary hid relevant code path
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tool parser repaired into wrong command
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TODO plan says done while tests fail
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local loop / repetition detected
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unbounded cloud escalation
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```
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## Approach C — GLIA + SmallCode combined route
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This is the strongest practical integration.
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```text
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GLIA recalls durable project memory
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→ SmallCode executes constrained patch plan
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→ verifier emits receipt
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→ GLIA stores final decision and scars
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```
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Pipeline:
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```text
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1. identify_active_project / project selection
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2. recall_context for relevant prior decisions
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3. SmallCode builds TODO plan
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4. execute patch-first edit
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5. run verifier / tests / lint
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6. Anti-FAMM checks summary/memory blind spots
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7. Anti-BraidStorm checks false convergence across candidate patches
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8. store_memory with final decision, failure, or scar
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9. NUVMAP Delta-DAG records the route
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```
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This route turns memory and execution into a closed loop:
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```text
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memory informs action
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action emits receipt
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receipt updates memory
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future recall sees the scar or promotion
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```
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## Approach D — Hermes-style authority bridge
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Hermes remains the authority/workflow bridge.
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```text
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tool or agent may execute/suggest/schedule
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but may not promote without receipts
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```
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Use Hermes when the route needs explicit permissioning:
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```text
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skill execution
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scheduled audit
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automation
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cross-tool workflow
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write authority
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promotion-state changes
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```
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Project mapping:
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```text
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Hermes = authority substrate
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GLIA = memory substrate
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SmallCode = execution substrate
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FAMM = scar/residual substrate
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BJW = decision substrate
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NUVMAP = route-memory substrate
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```
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## Approach E — FastPatch + StructuralAdmissibility route
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This is the local-to-global verifier route.
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```text
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FastPatchCheck
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→ local viability
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→ StructuralAdmissibilityCheck
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→ invariant legitimacy
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→ escalationNeeded or promotion
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```
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Use it as the default verification stack for coding agents:
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```text
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patch is syntactically valid
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patch compiles/tests locally
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patch preserves architectural invariant
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patch has no hidden route leak
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patch emits receipt
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```
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## Approach F — Logogram/code-kernel route
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Use semantic/logogram kernels to reduce active tool calls.
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```text
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many low-level tool calls
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→ one high-level semantic kernel
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→ deterministic expansion
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→ compile/check receipt
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```
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External analogue:
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```text
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BoneScript in SmallCode
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```
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Project analogue:
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```text
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LOGOGRAM_RADIX_FIELD
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GCCL_COMPLEX_PHASE_CODEC
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CODE_LOGOGRAM_KERNEL
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```
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Rule:
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```text
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internal bases may be weird;
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external recovery must be boring.
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```
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So every code-logogram must expand back to ordinary files, tests, bytes, or standard source code with receipts.
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## Approach G — GCCL complex phase action routing
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Use GCCL complex phase routing to classify agent actions:
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```text
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phase 1 → survivor action / apply patch
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phase i → witness action / inspect, recall, summarize
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phase -1 → cancellation action / revert, remove, undo
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phase -i → adversarial action / probe, fuzz, Warden test
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```
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With omnidirectional GCCL:
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```math
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\theta=e^{i\phi}
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```
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becomes a continuous routing field rather than a four-state switch.
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Use this when actions need to carry:
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```text
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chirality
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witness direction
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cancellation pressure
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scar burden
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receipt confidence
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adversarial pressure
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```
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## Approach H — Adversarial duals as mandatory hardening
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Before promotion, run:
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```text
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Anti-FAMM
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→ searches for invisible residuals and false scars
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Anti-BraidStorm
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→ searches for false convergence, aliasing, wrong-handed recombination, receipt drift
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```
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For memory-agent systems, this becomes:
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```text
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Anti-FAMM memory test:
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Does retrieved context omit a fact that changes the decision?
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Anti-BraidStorm patch test:
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Do several agents converge on the same wrong edit because they share a poisoned memory or false summary?
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```
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## Approach I — NUVMAP Delta-DAG execution receipts
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Every memory/action/check route becomes a Delta-DAG edge:
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```text
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state_t
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→ recalled context
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→ patch proposal
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→ verifier result
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→ adversarial result
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→ state_t+1
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```
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Receipt packet:
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```text
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memory_hash
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context_hash
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patch_hash
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test_hash
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scar_hash
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promotion_state
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```
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This gives the project replay, provenance, and failure reuse.
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## Recommended practical architecture
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```text
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GLIA persistent memory
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→ SmallCode constrained execution
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→ FastPatchCheck / StructuralAdmissibilityCheck
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→ Anti-FAMM / Anti-BraidStorm adversarial probes
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→ FAMM scar/coarsening ledger
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→ NUVMAP Delta-DAG route receipt
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→ GLIA store_memory / project summary update
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```
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Short form:
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```text
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GLIA remembers.
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SmallCode acts.
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FAMM scars.
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BJW decides.
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NUVMAP receipts.
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Hermes governs.
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```
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## Possible implementation tiers
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### Tier 0 — Manual doctrine
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Use the document as workflow guidance only.
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```text
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recall manually
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patch manually
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verify manually
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store decision manually
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```
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### Tier 1 — Local memory + constrained execution
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Use GLIA for memory and SmallCode for coding.
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```text
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GLIA recall_context
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→ SmallCode plan/patch
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→ tests
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→ GLIA store_memory
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```
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### Tier 2 — Receipt-bearing workflow
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Add FAMM-style receipts.
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```text
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memory hash
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patch hash
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test hash
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scar class
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promotion decision
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```
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### Tier 3 — Adversarial hardening
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Add Anti-FAMM and Anti-BraidStorm.
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```text
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memory blind-spot probes
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summary-loss checks
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false-convergence checks
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patch alias checks
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```
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### Tier 4 — GCCL phase-routed agents
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Use complex phase routing to classify actions and automate Warden behavior.
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```text
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apply / witness / cancel / adversarial probe
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```
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## Warden boundaries
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Allowed claim:
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```text
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These approaches give the project several concrete routes for constrained local-agent execution, persistent memory, patch-first coding, logogram-code kernels, and adversarial verification.
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```
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Disallowed claim:
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```text
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SmallCode, GLIA, or any local-agent stack guarantees correctness without tests, receipts, project isolation, and Warden checks.
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```
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Hard rules:
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```text
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memory is evidence, not proof
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summaries are lossy
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retrieval can be stale
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patches must be verified
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agent convergence can be false
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cloud escalation must be bounded
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project isolation must be checked
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receipts dominate model confidence
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```
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## Project sentence
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The possible constrained-agent approach is to pair GLIA-style durable memory with SmallCode-style constrained execution: recall the right project context, decompose into atomic TODOs, mutate by patch/logogram kernels, verify through fast and structural gates, attack with Anti-FAMM and Anti-BraidStorm, then store the resulting receipt, scar, or promotion back into the memory graph for the next run.
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## Citations
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```bibtex
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@online{doorman11991_smallcode,
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title = {SmallCode},
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author = {{Doorman11991}},
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organization = {GitHub},
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url = {https://github.com/Doorman11991/smallcode},
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urldate = {2026-05-18},
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note = {Terminal-native coding agent optimized for small local LLMs; README branch master.}
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}
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@online{nair_glia_ai,
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title = {GLIA — Persistent Memory for AI Coding Tools},
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author = {Nair, Eshaan},
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organization = {GitHub},
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url = {https://github.com/Eshaan-Nair/Glia-AI},
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urldate = {2026-05-18},
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note = {Local-first memory layer with browser extension and MCP server sharing one memory store.}
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}
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```
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