Six P2 items from the AI Workbench audit:
#7 Prompt ↔ loop budget sync:
System prompt now says "1 bis 5 Schritte pro Planungsrunde, bis zu 5
Planungsrunden" — matches MAX_REASONING_LOOP_ITERATIONS. Cross-ref
comment added to runner.ts.
#9 SceneHeader: useAgents() → useAgent(id):
Only loads the single bound agent instead of the full agent list.
Eliminates unnecessary Dexie churn on every scene header render.
#10 Unified scope filter:
New scope-filter.ts with filterByScopeTagMap() (batch, sync) and
filterByScopeAsync() (per-record). Both scope-context.ts (AI) and
scene-scope.svelte.ts (UI) now import from the shared module —
zero duplicated filter logic.
#11 Research dedup:
Research input ID changed from `news-research-${Date.now()}` to
`news-research-${mission.id}` — re-runs overwrite instead of
appending duplicates.
#12 Kontext injection policy clarified:
loadAgentKontextAsResolvedInput no longer falls back to the global
singleton. Comment + code aligned: kontext injection is explicit
(via input picker), not auto. Dead loadKontextAsResolvedInput
kept for potential future opt-in auto-inject feature.
Audit doc updated with all items marked DONE.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Enable real-time token streaming during the planner "calling-llm" phase
so the user sees live progress ("empfange Plan… 128 tokens") instead of
a static spinner. The parser still receives the full text once complete —
no partial-JSON risk.
Changes:
- Extract shared SSE parser from playground into @mana/shared-llm/sse-parser
- remote.ts: use stream:true when onToken callback is provided
- AiPlanInput: add optional onToken field (shared-ai)
- ai-plan task: pass onToken through to backend.generate()
- runner.ts: throttled (500ms) phaseDetail updates during streaming
- Playground: refactored to use shared SSE parser
Also includes: AI agent architecture comparison report (docs/reports/)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Debug log from a "tag 4 notes" mission showed the planner's second-round
response truncated mid-step: it was proposing one add_tag_to_note per
listed note but ran out of tokens halfway through note #2. Parser
rejected the malformed JSON → loop exited with 0 staged, user saw
nothing to approve.
Raising maxTokens to 4096 fits ~15-20 step objects, which covers the
batch-tagging / batch-save pattern the reasoning loop is designed for.
Also updating the system prompt so the planner actually knows about
the loop it's running inside: read-only tools are announced as
auto-executing with outputs visible next turn, and a new rule makes
explicit that batch jobs must emit all write-steps in one plan (because
staging a propose-tool ends the turn). Step count raised 1-5 → 1-10.
Prompt snapshot tests still pass (they check structure, not text).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
New local-only Dexie table _aiDebugLog (v20, never synced) holds one
row per mission iteration with the full system+user prompt, raw LLM
response, latency, every ResolvedInput the planner saw, and pre-step
state (kontext-injected? web-research-ok-or-error?). Capped at 50
newest rows.
aiPlanTask always returns the captured prompt/response on AiPlanOutput.
debug; the runner persists it only when isAiDebugEnabled() — toggled
via a checkbox in the Mission detail header (defaults to on in DEV
builds, off in prod, override via localStorage 'mana.ai.debug').
New <AiDebugBlock> component renders below each iteration card:
expandable sections for Pre-Step, Resolved Inputs (each input
individually collapsible), System Prompt, User Prompt, Raw Response,
plus a "📋 JSON" copy-to-clipboard button for bug reports.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Single source of truth for AI Workbench types shared between the webapp
(Vite/SvelteKit) and the server-side mana-ai Bun service. Prevents the
two runtimes from drifting on prompt shape or mission structure.
- `@mana/shared-ai` package:
- `actor.ts` — Actor union (user | ai | system) + helpers, mirrors the
webapp's runtime type so server-side consumers parse incoming actors
without re-declaring
- `missions/types.ts` — Mission, MissionCadence, MissionInputRef,
MissionIteration, PlanStep, MissionState. Adds optional
`iteration.source: 'browser' | 'server'` to distinguish foreground
vs server-produced iterations (groundwork for proposal write-back)
- `planner/prompt.ts` — `buildPlannerPrompt` pure function
- `planner/parser.ts` — `parsePlannerResponse` strict JSON validator
- Vitest smoke tests (2) cover prompt → parse round-trip + unknown-
tool rejection
- Webapp:
- `missions/types.ts` re-exports from shared-ai, keeps webapp-local
`MISSIONS_TABLE` constant + `planStepStatusFromProposal` bridge
- `missions/planner/{types,prompt,parser}.ts` become re-export stubs
so existing imports keep working unchanged
- Existing webapp tests (60) continue to pass — the wire code didn't
move, just its home
Next: mana-ai service imports buildPlannerPrompt/parsePlannerResponse
from shared-ai + wires mana-llm + writes iteration back as a
'source=server' row (tracked in services/mana-ai/CLAUDE.md).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
