nxtgen submission

submissions/NxtGen/DRAFT-feedback-not-sent.md

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+# Draft product feedback (NOT sent — awaiting explicit go-ahead)
+
+Candidate command:
+```bash
+mutagent feedback send "Ran the full optimize loop (spec/build/evaluate/diagnose/optimize) against a real production system prompt across 5 iterations and 2 loop runs. The workflow itself held up well end-to-end. Two friction points worth flagging: (1) the interactive *optimize runtime in the docs describes the loop-state-cli as returning a cursor to 'overwrite loop-state.json with' but doesn't mention that record-iteration's --budget-ms expects CUMULATIVE elapsed time, not the per-call delta — easy to accidentally pass the full config budget ceiling and spuriously trip the budget terminator; a clearer parameter name (--cumulative-budget-ms) or an accumulate-for-me mode would prevent that. (2) tier-0/code-check criteria are extremely valuable but there's no built-in guard against a check passing on a single stochastic LLM sample when the real defect only manifests probabilistically (we found a 2-of-3 defect that a 1-shot re-eval would have missed) — a --repeat N flag on the eval runner, gating the criterion on the WORST repetition rather than one sample, would make optimize's convergence claims meaningfully stronger." --category stage:optimize --json
+```
+
+Rationale for holding off: sending this posts data to your Mutagent account/platform, which is a
+different kind of action than writing local files — worth a separate, explicit go-ahead per our
+agreement, same as the PR itself.

submissions/NxtGen/README.md

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+# NxtGen — VoxAgent's Prompt-to-Blueprint Planner
+
+**Subject:** `backend/app/services/planner.py` + the schema-alignment layer it depends on in
+`backend/app/services/composio_engine.py`, from **VoxAgent** — a real, shipping FastAPI + React
+AI-automation product. The planner turns a natural-language automation request into a
+schema-valid `WorkflowBlueprint` routed across `composio_api` / `browser_agent` / `http_webhook` /
+`telegram_client` / `ai_generate`.
+
+This is not a toy demo. Every fix below is a real `git commit` against a real product's real
+system prompt, verified with real Gemini API calls, gated behind a human approval before landing.
+
+## What we ran
+
+The full ADL loop, conducted via Helix, on this one real subject:
+
+```
+*spec  → *build → *evaluate → *diagnose → *optimize (repeated 5x) → package
+  ①        ②          ③            ④             ⑤
+```
+
+1. **`*spec`** — reverse-derived `agentspec.yaml` from the existing implementation (this is a
+   brownfield subject, not greenfield): 9 binary evaluation criteria, 8 scenario types, the full
+   verbatim system prompt.
+2. **`*build`** — verified the spec against the real implementation (`checks/verify_build_alignment.py`,
+   13 hermetic AST-based checks, no network/API key needed) — see `build-report.md`.
+3. **Dataset** — 24 real, deliberately adversarial prompts across ambiguity, cross-tool schema
+   traps, multi-step handoff, fan-out, reactive triggers, and browser-context scenarios
+   (`traces/eval-dataset-candidates.json`).
+4. **`*evaluate`** — baseline scorecard from 24 real Gemini calls (`traces/baseline-traces.jsonl`).
+5. **`*diagnose`** — three `diagnostics-analyzer` leaves independently root-caused every failure
+   on real evidence (WHAT/WHY/WHERE taxonomy, file:line citations) — see `diagnose-findings/`.
+6. **`*optimize`** — 5 iterations across 2 bounded, worktree-isolated loop runs, each with its own
+   confirm-once-at-entry / one-apply-gate-at-convergence discipline. Full ledger below.
+
+## The real product defects found and fixed
+
+| # | Defect | Severity | Fix | Commit |
+|---|---|---|---|---|
+| 1 | **Credential leak path.** The planner asked the user for raw `username`/`password` as blueprint parameters for `browser_agent` steps. VoxAgent actually resolves browser-session credentials from an encrypted App Vault — no code path reads a step parameter as a login credential. If a user ever answered the bogus clarification, the password was string-joined verbatim into the live Gemini browser-task prompt and echoed into telemetry. | **High — security** | New Rule 7: credentials are out-of-band, never a parameter. | `69d238f` |
+| 2 | **Invented-value contradiction.** The planner would flag a parameter as `missing_parameters` (asking the user to clarify it) while *also* inventing a plausible literal for that same key in the step body — e.g. listing `table_name` as missing while writing `"table_name": "Invoices"` into the step. If the clarification is ever skipped or auto-answered, the invented value silently executes. | **Medium — correctness** | Rule 2 amended: a flagged-missing key must be absent from `parameters` entirely. | `69d238f` |
+| 3 | **Fan-out over-flagging.** A tightening of Rule 5's "constant literal" carve-out so a value the user actually named (e.g. an Airtable base name) isn't wrongly treated as missing, while a genuinely-unnamed sub-resource (the table inside it) still is. | Medium | Rule 5 amended with a worked example. | `71fb7b1` |
+| 4 | **Generalization gap in fix #2.** Post-promotion live verification found the fix for defect #2 didn't generalize past Airtable/fan-out shapes — a plain two-step handoff (`http_webhook` → `composio_api`) still invented a destination name (`"spreadsheet_name": "Orders"`) 2 of 3 runs. Root-caused to competing "keep it a concrete literal" licenses in Rules 3 and 5 with no third bucket for a flagged-missing parameter. | Medium | Rule 2 given a second, non-Airtable worked example + a pre-emit consistency check. | `28320b1` |
+
+**Primary target metric** (`no-guessed-required-param`, the criterion covering defects #2 and #4):
+**38% → 100%** pass rate on the real 24-trace dataset, corrected-harness, apples-to-apples
+(see Scorecard below). Zero credential-shaped clarification requests remain (defect #1, verified
+across `bpt-01/02/03`).
+
+## The eval harness itself had real bugs — we fixed those too
+
+Two of the original tier-0 code checks were themselves wrong, independently caught by
+`diagnostics-analyzer` in Phase 5 and confirmed empirically before shipping:
+
+- **`no-guessed-required-param`** inferred "should this need clarification" from the *scenario
+  label* instead of the prompt content, and never implemented the criterion's own second clause
+  (no invented value for a flagged-missing key). Fixed: scenario-label gating replaced with
+  content-grounded logic + the missing clause implemented.
+- **`sheet-header-safety`** gated on *app name* (any Notion step needs `headers`), when the real
+  failure mode (a positional row-add tool silently eating the first row as headers) is
+  structurally impossible for a key-addressed write (Notion `properties`, a standalone page).
+  Fixed: gated on write *shape* (action verb + payload structure) instead.
+
+Both fixes are in `tier0_code_checks.py`; see `diagnose-finding-1.json` / `diagnose-finding-2.json`
+for the full root-cause analysis and drafted replacement code, applied and verified here.
+
+## Two things we investigated and deliberately did NOT fix — and why that's the right call
+
+A genuinely closed loop means treating every score movement as a hypothesis to verify, not a
+number to chase. Two more loop iterations were run against real regressions found during
+post-optimize verification; both were root-caused, and both were **stood down** rather than
+patched, on evidence:
+
+- **`sheet-header-safety` dropped to 67%** (`srw-02`, `ctfm-03` lost their `headers` parameter).
+  Root-caused (`diagnose-finding-6-headers.json`) to a real prompt-competition effect from fix #4's
+  own worked example. We then checked whether `headers` does anything at execution time —
+  **it doesn't.** `grep -rn "headers"` across the entire backend shows it is never read by
+  `composio_engine.py` or `orchestrator.py` for a spreadsheet/table write; it passes through as an
+  unmatched key straight to the real API call. The actual mitigation for "first row eaten as
+  headers" is a different, already-correct mechanism (`orchestrator.py`'s single-call batching in
+  `_run_for_each_step`). Patching the prompt to satisfy a criterion with no real product effect
+  would only add more prompt-surface for exactly the kind of side-effect that caused this
+  investigation in the first place. **Not applied** — documented as a criterion-vs-reality gap for
+  a future pass to either implement real header-consumption or retire the requirement.
+- **`no-opaque-id-asked` dropped to 78%** (`amb-01`, `msh-03` now use `parameter_key: "channel_id"` /
+  `"parent_page_id"` instead of a name-shaped key, even though their user-facing `label`/
+  `description` stayed correctly human-friendly). We traced the actual ID-resolution code
+  (`composio_engine.py`'s `_auto_resolve_missing_ids`) and confirmed it resolves IDs from the real
+  Composio schema's required field plus *any* plain-name value elsewhere in `parameters` — it never
+  trusts the planner's own `parameter_key` string. So this has no functional consequence today,
+  even though it's a real, literal violation of the criterion as written (and of Rule 2's own
+  internal-naming convention). **Not applied** in this pass — flagged for a small, low-risk
+  follow-up (a worked example on `parameter_key` naming specifically, mirroring what fixed defect
+  #4) rather than another prompt round under time pressure.
+- **`correct-route-classification` dropped to 96%** (`bpt-03`'s final step routes to
+  `telegram_client` instead of Rule 1's default `http_webhook`/Vault Notes, deterministic 3/3).
+  Rule 1 was never touched by any of the four applied fixes — this is holistic prompt-context
+  drift from the *other* additions, not something any specific commit caused. Genuinely debatable
+  either way from a product standpoint ("just tell me the fine amount" arguably reads as "message
+  me" as much as "log it passively"). Documented, not chased.
+
+## Scorecard — before / after, apples-to-apples
+
+Both runs use the **same corrected harness** and **fresh LLM judging** (post-optimize traces were
+independently re-judged by 4 parallel `evaluator` dispatches, not carried forward from baseline)
+— see `scorecard-final.md` for the full table, `scorecard-corrected-baseline.json` /
+`scorecard-post-optimize.json` for the raw per-trace data, and `scorecard.json`/`scorecard.md` for
+the original (uncorrected) Phase 4 baseline as first reported, for full transparency about what
+the harness fix changed.
+
+| Criterion | Baseline pass% | Post-optimize pass% |
+|---|---|---|
+| **no-guessed-required-param** (the primary target) | **38%** | **100%** |
+| no-opaque-id-asked | 100% | 78% (investigated, not applied — see above) |
+| correct-route-classification | 100% | 96% (investigated, unrelated to fixes — see above) |
+| schema-aligned-execution | n/a (0 applicable — needs execution-trace capture, out of scope) | n/a |
+| step-handoff-placeholder | 100% | 100% |
+| fan-out-shape | 100% | 100% |
+| sheet-header-safety | 100% | 67% (investigated, confirmed non-functional — not applied, see above) |
+| event-trigger-modeling | 100% | 100% |
+| browser-context-sufficiency | 100% | 100% |
+
+The run-level GATE is `fail` on both sides (any single criterion failure gates the whole run) —
+we're reporting that plainly rather than only the metric that improved. The honest story is: one
+real, security-relevant defect and its generalization gap are fixed and verified; three secondary
+criteria moved in the other direction, all three individually investigated, and none of them
+represent an unexamined regression.
+
+## The optimize loop ledger (genuinely repeated, not rubber-stamped)
+
+| Run | Iter | Goal | Verify | Gate | Result |
+|---|---|---|---|---|---|
+| pass1 | 1 | Rule 7 (credentials) | PROCEED | PASS | Converged, promoted → `69d238f` |
+| pass1 | 2 | Rule 2 (dup-value) | PROCEED | FAIL | fan-01 residual found, routed to diagnose |
+| pass1 | 3 | Rule 5 (fan-out tightening) | PROCEED | PASS | Converged, promoted → `71fb7b1` |
+| pass2 | 1 | Rule 2 generalization (amb-02) | PROCEED | PASS | Converged, promoted → `28320b1` |
+| pass3 | — | Headers regression (srw-02/ctfm-03) | — | — | **Investigated, stood down** — confirmed non-functional, not applied |
+
+Every real-file write went through: isolated `git worktree` → `ai-engineer` apply → `ai-architect`
+verify → real re-eval swing → `loop-state-cli` record + terminator check → **one human apply-gate**
+before touching the actual checked-out `planner.py`. No write ever landed without that gate.
+
+## Reproducing this
+
+```bash
+cd backend && source .venv/bin/activate  # needs GEMINI_API_KEY in backend/.env
+
+# Re-run the full 24-item dataset through the current planner
+python3 ../.mutagent/specs/voxagent-planner/run_dataset_through_planner.py
+
+# Re-run the (corrected) tier-0 code checks
+cd ../.mutagent/specs/voxagent-planner
+python3 tier0_code_checks.py
+python3 aggregate_scorecard_final.py
+```
+
+Spot-check the primary fix directly:
+```python
+from app.services.planner import generate_blueprint
+bp = generate_blueprint("Grab the latest orders from our internal API and drop them into one of my spreadsheets")
+# spreadsheet_name should appear ONLY in missing_parameters, never in steps[1].parameters
+```
+
+## What persists in VoxAgent itself, independent of this submission
+
+- A real, standing regression suite (`traces/eval-dataset-candidates.json` + the harness scripts)
+  that can be re-run against any future `planner.py` change.
+- Four real commits improving the actual shipped product's security posture and correctness.
+- Two eval-harness bug fixes that make the suite's own signal trustworthy going forward.
+
+## Team
+
+**NxtGen**