# Track: Mike Acton's nagent — Deep Dive on LLM Agent Architecture

**Status:** Active (spec approved 2026-06-08; revised 2026-06-08 with user-corrections)
**Initialized:** 2026-06-08
**Owner:** Tier 2 Tech Lead
**Priority:** Medium (architectural; informs future Application+Meta-Tooling decisions but is not a code refactor)

> **Revision note (2026-06-08):** This spec was revised based on direct user corrections after the first draft. Earlier versions overstated gaps in Manual Slop's "editable discussion" and "per-file memory" features; the corrections are folded into §2 and §4 below. Read the **report.md** for the actual analysis; this spec.md is the wrapper.

---

## 1. Overview

This track documents a deep-dive analysis of Mike Acton's [`macton/nagent`](https://github.com/macton/nagent) reference implementation ("nagent" = "not-an-agent") and its implications for how Manual Slop should think about LLM-driven workflows.

nagent is a 14-section, ~1,500-line Python reference that operationalizes the philosophy **"the agent is not the thing; the data is the thing."** It provides a concrete, minimal counterpoint to the standard "agent framework" model. Its central claim: **durable work matters more than durable processes; explicit artifacts beat opaque state.**

The companion doc ([report.md](./report.md)) is the deep-dive analysis itself — a 14-section comparison against Manual Slop's actual implementation, written for engineers (not marketing). This spec.md is the conductor/track wrapper: the design intent, the relationship to the Application vs Meta-Tooling split, the planned follow-up tracks, and the out-of-scope notes.

### 1.1 What this track produces

| Artifact | Purpose |
|---|---|
| `spec.md` | This file — the track design and scoping. |
| `report.md` | The 14-section deep-dive analysis. The primary deliverable. |
| `comparison_table.md` | A flat side-by-side table (one row per nagent principle) for quick reference. |
| `decisions.md` | Future-track candidates extracted from the analysis (each becomes a follow-up track if approved). |

### 1.2 Non-Goals

- **Not** rewriting Manual Slop to use nagent. The architectures serve different domains (see §2).
- **Not** replacing any existing track. This is a *reference* track — it informs future tracks but doesn't compete with them.
- **Not** a comparison of "framework vs framework." nagent is a 1,500-line reference; Manual Slop is 13,000+ lines of production code with a real GUI, real persistence, real HITL. The comparison is *philosophical*, not "which is better."

---

## 2. The Application / Meta-Tooling Distinction (load-bearing context)

Per `docs/guide_meta_boundary.md`, Manual Slop lives in two distinct architectural domains. **This distinction is critical for understanding the nagent comparison:**

| Domain | Lives at | AI / HITL Model | Tooling |
|---|---|---|---|
| **The Application** (`manual_slop`) | `gui_2.py`, `ai_client.py`, `multi_agent_conductor.py`, `dag_engine.py` | A local GUI for orchestrating AI. The "Application AI" is a long-lived assistant that the user talks to over many turns. Strict HITL: every destructive action requires a GUI modal approval. | `manual_slop.toml [agent.tools]` — strict allowlist |
| **The Meta-Tooling** (us) | `scripts/mma_exec.py`, `conductor/`, `.agents/skills/`, the MCP tools in `mcp_client.py` when used by external agents | External agents (Gemini CLI, OpenCode, Claude Code) that *build* the Application. Each invocation is a fresh sub-agent. Token-firewalled. | Full mcp_client.py toolset, including mutation tools |

**nagent lives in the Meta-Tooling domain.** nagent is a reference for how *external* agents (the ones reading this conversation, the ones writing the code) should structure their own work.

**Manual Slop's Application AI does not — and should not — look like nagent.** The Application AI is a chatty, conversational, persona-driven, RAG-augmented, curation-rich assistant with a real GUI. It's a *different kind of thing*. Conflating the two is exactly the kind of "feature bleed" `guide_meta_boundary.md` warns against.

Every recommendation in `report.md` is qualified with which domain it applies to. The Application is the production code the user cares about; the Meta-Tooling is what we (the agents) use to build it.

---

## 3. Summary of the 14-Section Comparison

The full table is in `comparison_table.md`. Verdict summary:

| nagent Principle | Manual Slop Equivalent | Verdict |
|---|---|---|
| 1. Durable work, disposable workers | AppState snapshots + history branching (Takes); MMA workers are real subprocesses | **PARTIAL** — different domains; MMA has it, App doesn't need it |
| 2. Text in, text out | `ai_client.send()` returns `str`; `mcp_client.dispatch` returns `str` | **PARITY** |
| 3. Conversations are editable state | Discussion takes + branching + edit-in-place + UISnapshot history; `ContextPreset` for per-file view-mode memory | **PARITY (DIFFERENT FOCUS)** — Manual Slop has this; focuses on *editable UI state* (per Take) and *editable per-file curation* (per FileItem), not editable conversation logs |
| 4. Visible output protocol | Uses provider-native function calling; the protocol is opaque to humans | **ARCHITECTURAL DIFFERENCE** — Application-side; correct trade-off |
| 5. The loop (append, call, parse, act, repeat) | `ai_client._send_*` tool-call loop, MMA `ConductorEngine.run`, `WorkflowSimulator.run_discussion_turn_async` | **PARITY** — but the loop is in multiple files, not as a single small function |
| 6. Per-file memory (curation, not conversation log) | `FileItem` (path + view_mode + ast_mask + custom_slices); `ContextPreset` (saved set of FileItems); Fuzzy Anchor slices | **MANUAL SLOP IS STRONGER IN THE CURATION DIMENSION**; nagent's "file-edit conversation" pattern (one conversation log per file) is not present |
| 7. Repository history as data | `_reread_file_items` mtime-based diff injection; `git_commit_file_patch` per-file history summaries; no explicit "neighborhood" computation | **PARITY (PARTIAL)** — diff injection is similar; the "neighborhood" computation is missing |
| 8. Historical coupling & artifact neighborhoods | n/a (no equivalent) | **GAP** — could be added as a new tool |
| 9. Disposable sub-conversations | MMA `mma_exec.py` Tier 3 workers are real subprocesses; **non-MMA 1:1 discussions do NOT have disposable sub-conversations yet** (per user) | **GAP (Application) — useful for 1:1 discussions; **PARITY for MMA** |
| 10. Controlled writes | MCP 3-layer security + Execution Clutch + Allowlist Construction + Path Validation + Resolution Gate | **PARITY (STRONGER)** — Manual Slop's 3-layer is more thorough than nagent's tmpdir check |
| 11. Large files as explicit artifacts (split/patch) | `nagent-file-split`/`nagent-file-patch`/`nagent-file-summarize` with `index.json` + segment files + source hash validation; 32 KB target size; per-language natural splitters (no tree-sitter) | **PARITY (DIFFERENT MECHANISM)** — both have the insight; nagent uses per-language scoring functions + subprocess isolation, Manual Slop uses tree-sitter + in-process `summarize.py` |
| 12. Tool discovery (self-describing executables) | Hard-coded `dispatch` if/elif chain in `mcp_client.py` | **GAP (Application) — could be added; useful for the Meta-Tooling domain** |
| 13. Differences from frameworks | The philosophical frame | n/a |
| 14. Build your own | The reference's "minimal" claim is wrong for the Application | n/a for Application |

The full 14-row analysis with 6 (revised from 8) specific Manual Slop pitfalls is in `report.md`.

---

## 4. The Revised 6 Pitfalls (corrected)

Earlier versions of this list contained two errors that user-corrections caught:

- **REMOVED** pitfall #3 (per "Conversation state is buried in module-level globals" was over-stated) — Manual Slop has *some* editable-state infrastructure (`HistoryManager` with UISnapshot, discussion Takes/branching, `ContextPreset` save/load) but the actual *raw conversation transcript* is in `ai_client._provider_specific_history` globals. The truth is: **Manual Slop has editable UI state, not editable conversation transcripts.** That distinction is now captured honestly in §3 of the report.

- **REVISED** pitfall #6 (per "Per-file memory") — Manual Slop *does* have a per-file memory concept (`FileItem` + `ContextPreset` + `custom_slices` + `ast_mask`), but it's *curation memory*, not nagent's *conversation-log memory*. Manual Slop's concept is *richer in the curation dimension* but *absent in the conversation-log dimension*. That's a useful distinction.

The remaining 6 pitfalls, after corrections:

1. **No structured output protocol** in the Application AI (uses opaque function calling; nagent's regex tag protocol is the alternative for the Meta-Tooling). **Domain: Application can stay opaque; Meta-Tooling should learn.**
2. **Provider-specific history is in process globals** (5 separate per-provider lists with their own locks; switching providers mid-session loses history). **Domain: Application. Future-track candidate.**
3. **RAG is not "history as data"** — RAG retrieval is fuzzy and not auditable. nagent's git-history-driven context is exact and inspectable. RAG is useful but should be additive, not a replacement. **Domain: Application. Coexists with nagent-style history.**
4. **The AI client is a stateful singleton with module-level globals** (2,685-line `ai_client.py` is unparseable without state). A future refactor toward a stateless `LLMClient` class with explicit `Conversation` objects would let the App save/load/replay conversations as files. **Domain: Application. Future-track candidate.**
5. **No non-MMA disposable sub-conversations** — only MMA workers are real subprocesses; the user explicitly noted that 1:1 discussions don't have sub-agents. nagent's `<nagent-conversation>` pattern (a sub-agent for bounded investigation) would be valuable for the Application. **Domain: Application. Future-track candidate (user-flagged as a want).**
6. **Hard-coded tool discovery** — the 45 MCP tools are in a flat if/elif chain in `dispatch`. nagent's `--description` self-describing executables pattern is more extensible. **Domain: both. Low priority.**

Plus 2 domain-domain recommendations that are not pitfalls per se:

- **Personas are config bundling** (per user: "just bundles preparatory cruft — vendor/model, tools/permissions, and system prompts"). The user noted that you can *completely opt out* by just using AI settings directly. **Domain: Application. Keep as-is; not a pitfall.**
- **RAG is opt-in** (per user: "doesn't have to be used"). Worth considering: a sub-agent that *prepares RAG chunks* before a run. **Domain: Application. Future-track candidate.**

---

## 5. What This Track Read (in full, before writing)

To avoid hand-waved claims, the report and this spec were written after reading all of:

### nagent source (read in full)

- `README.md` (~1,500 lines) — the 14-section "teaching document"
- `bin/nagent` (~700 lines) — the main loop, tag parser, sub-conversation runner, git history + co-edit + summary integration
- `bin/helpers/nagent_llm.py` (~300 lines) — provider dispatch, token accounting
- `bin/helpers/nagent_cli.py` (~80 lines) — `--description` self-describing executable pattern, `WaitSpinner`
- `bin/helpers/nagent_file_edit_lib.py` (~170 lines) — file-index by `st_dev:st_ino`, `resolve_file_edit_conversation`, `is_split_segment_for_source`
- `bin/helpers/nagent_file_split_lib.py` (~400 lines) — `SPLIT_TYPES` (11 langs), per-language `SCORE_BY_TYPE` (no tree-sitter; regex + line counts + brace/JSON/XML depth), 32 KB default, source SHA-256 hashing
- `bin/helpers/nagent_file_patch_lib.py` (~130 lines) — strict hash validation, `make_unified_patch` via `difflib.unified_diff`, `apply_segment_patches` writes the source
- `bin/helpers/nagent_file_summarize_lib.py` (~110 lines) — per-segment LLM calls + retry-with-smaller-prompt (max 2 attempts), `--limit-word-count` validation, `combined_summary_from_index`
- `bin/nagent-file-edit` (~120 lines) — per-file subprocess wrapper, `default_pid = BASHPID or os.getppid()`
- `bin/nagent-file-split` (~170 lines) — main executable, `--refresh INDEX` mode for re-splitting without losing segment paths
- `bin/nagent-file-summarize` (~100 lines) — main executable, cascades to `nagent-file-split --summarize` for files > 64 KB; uses `positive_int` CLI type (rejects 0)

### Manual Slop docs (read in full)

- `docs/Readme.md` (434 lines) — docs index
- `docs/guide_architecture.md` (989 lines) — threading model, cross-thread data structures
- `docs/guide_ai_client.md` (424 lines) — multi-provider LLM client
- `docs/guide_mma.md` (564 lines) — 4-tier MMA orchestration
- `docs/guide_tools.md` (506 lines) — MCP tool inventory + Hook API
- `docs/guide_mcp_client.md` (410 lines) — 45 tools + 3-layer security
- `docs/guide_app_controller.md` (447 lines) — headless controller
- `docs/guide_meta_boundary.md` (57 lines) — Application vs Meta-Tooling split
- `docs/guide_context_curation.md` (303 lines) — Granular AST Control + Fuzzy Anchor Slices + AST Inspector
- `docs/guide_personas.md` (307 lines) — Unified agent profile model
- `docs/guide_rag.md` (411 lines) — RAG subsystem
- `docs/guide_gui_2.md` (477 lines) — ImGui application (App/Controller state delegation, hot-reload, defer-not-catch)

### Manual Slop source (selectively read, in service of the user-corrections)

- `src/models.py` lines 510-559 (FileItem schema), 909-937 (ContextPreset schema)
- `src/context_presets.py` (30 lines, full file) — the `ContextPresetManager`
- `src/project_manager.py` lines 429-450 (`branch_discussion`, `promote_take`)
- `src/aggregate.py` first 80 lines (context composition pipeline)
- `src/history.py` (full file, 141 lines) — `UISnapshot` and the snapshot model

The user-corrections specifically drove a re-survey of `FileItem` + `ContextPreset` + `aggregate.py` + `HistoryManager` after the first draft overstated Manual Slop's gaps.

---

## 6. Architectural Reference

- **nagent source code:** https://github.com/macton/nagent (read in full for this analysis)
- **nagent README:** https://github.com/macton/nagent/blob/main/README.md (the 14-section "teaching document")
- **Mike Acton's data-oriented design talks:** https://www.youtube.com/results?search_query=mike+acton+data+oriented (foundational; nagent is a specific application)
- **Ryan Fleury "errors are just cases":** https://www.dgtlgrove.com/p/the-easiest-way-to-handle-errors (cited in `data_oriented_error_handling_20260606`; consistent with nagent's data-over-control-flow stance)
- **Internal:** `docs/guide_meta_boundary.md` for the Application/Meta-Tooling split
- **Internal:** `docs/guide_architecture.md` §"Thread Domains" for the cross-thread state-sync problem that nagent sidesteps by having no GUI

---

## 7. See Also

### Internal Documentation

- `docs/Readme.md` — Manual Slop documentation index
- `docs/guide_architecture.md` — Threading model and provider dispatch
- `docs/guide_ai_client.md` — The Application's LLM client
- `docs/guide_mma.md` — 4-tier MMA orchestration
- `docs/guide_meta_boundary.md` — The Application vs Meta-Tooling split
- `docs/guide_tools.md` — MCP tool inventory and Hook API
- `docs/guide_mcp_client.md` — 45 tools + 3-layer security
- `docs/guide_context_curation.md` — Granular AST Control + Fuzzy Anchor Slices + AST Inspector
- `docs/guide_personas.md` — Unified agent profile model
- `docs/guide_rag.md` — RAG subsystem
- `docs/guide_gui_2.md` — ImGui application

### Related Tracks

- `data_oriented_error_handling_20260606` — Already cites Acton by name. The `Result[T]` + `ErrorInfo` data model from this track is consistent with nagent's "data, not control flow" stance.
- `qwen_llama_grok_integration_20260606` — The "OpenAI-compatible shared helper" pattern is exactly nagent's "thin boundary adapter on a normalized data structure" approach.
- `mcp_architecture_refactor_20260606` — Already blocked by `data_oriented_error_handling_20260606`. The sub-MCP extraction (planned) will benefit from nagent's "small helper per concept" decomposition pattern.
- `data_structure_strengthening_20260606` — The type-alias work is consistent with nagent's "make the data shape explicit" stance. The audit script + NamedTuple work parallels nagent's split-index / patch-artifact approach.

### External

- Mike Acton, "Data-Oriented Design and C++" (cppCon 2014) — The original DOD talk that nagent operationalizes
- Ryan Fleury, "The Easiest Way To Handle Errors Is To Not Have Them" — Companion framework; same "errors as data" thesis
- Timothy Lottes (@NOTimothyLottes) — Cited in the `data_oriented_error_handling` review; same "error codes are data" stance
- Valigo (@valigotech) — Cited in the data_oriented_error_handling review; "exceptions mess with control flow in very weird ways"

---

## 8. Scope Boundaries

### In Scope

- The 14-section nagent philosophy
- The 6 (revised) concrete pitfalls in Manual Slop
- Mapping each pitfall to a future-track candidate (in `decisions.md`)
- Application vs Meta-Tooling domain classification for every recommendation
- The philosophical grounding for existing Manual Slop conventions (data-oriented, thread-disciplined, GUI-decoupled)

### Out of Scope

- **Implementation work.** This is a reference/analysis track. No code is being changed.
- **Replacing nagent in the Meta-Tooling.** The Meta-Tooling is whatever the external agent (Gemini CLI, OpenCode) is. nagent is a *reference example*, not a competitor. It's worth reading for ideas, not adopting wholesale.
- **Building a new "data-oriented" track for Manual Slop.** The `data_oriented_error_handling_20260606` track already covers the data-vs-control-flow axis. This track is the *philosophical foundation* for that work; the implementation track is separate.
- **Comparing nagent to other LLM agent frameworks (LangChain, AutoGen, CrewAI, etc.).** nagent is a specific small reference; those are different scales. This track is about nagent specifically.

### Known Trade-offs (called out in the report)

- **Manual Slop's personas are a feature, not a bug, in the Application domain.** A user-facing chatty assistant benefits from "persona = named configuration that the user can save and recall." nagent's "data, not personality" stance is correct for sub-agent invocations but wrong for long-lived assistant sessions. (Per user: personas are config bundling; the user can opt out by using AI settings directly.)
- **Manual Slop's RAG is a feature, not a bug, in the Application domain.** RAG enables semantic search across large codebases. nagent's "git history → summaries" is exact but doesn't help when the user asks "how does the execution clutch work" and the relevant information is in `guide_architecture.md` (a doc, not source). RAG is opt-in.
- **Manual Slop's GUI is a feature, not a bug, for its domain.** It enables the rich persona, curation, RAG, and snapshot UX. nagent explicitly has no GUI; the Application explicitly has a GUI. They serve different needs.
- **The "1,500-line reference" vs "13,000-line production" comparison is not fair.** nagent is a teaching example. Manual Slop is a working tool. The right comparison is "nagent's principles vs Manual Slop's implementation," not "which codebase is better."

---

## 9. Verification Criteria

This is a reference/analysis track. The verification is:

- [ ] `report.md` exists and covers all 14 nagent principles with a Manual Slop assessment for each
- [ ] `comparison_table.md` exists as a flat side-by-side reference
- [ ] `decisions.md` exists with future-track candidates (each is a separate conductor track to be specced independently)
- [ ] Every "Manual Slop could learn from nagent here" recommendation is tagged with the domain (Application / Meta-Tooling / Both)
- [ ] No code is being modified by this track
- [ ] The companion doc is read by ≥1 person who is planning a future track (the report.md file is referenced by the relevant future-track specs)
- [ ] (Post-correction) The report's verdicts on nagent §3 (Conversations are editable state) and §6 (Per-File Memory) are *corrected* per user feedback — the first draft overstated gaps

---

## 10. Status

**Approved 2026-06-08 (initial); revised 2026-06-08 with user corrections.** Ready for human review of `report.md`.

After human review of `report.md`, the `decisions.md` candidates will be evaluated:
- High-priority items (e.g., stateless `LLMClient` class, non-MMA sub-conversations, RAG pre-staging) → new conductor tracks
- Medium-priority items (e.g., self-describing MCP tools, conversation file persistence) → research spikes
- Low-priority items → deferred until a specific Application need surfaces

The current `data_oriented_error_handling_20260606` track and the future `mcp_architecture_refactor_20260606` track are already philosophically aligned with nagent's principles; this track is the *explicit* reference to that alignment.
