ADR-163: Agentic Trajectory Dashboard Architecture

Status

ACCEPTED (2026-02-08) | Build Status: Complete through Phase 10

Executive Summary

The Agentic Trajectory Dashboard is a local-first executive BI platform that transforms raw AI development session data into interactive, drillable visualizations. Built with React 18 + TypeScript + D3.js + Tailwind CSS, it renders 12 pages and 37 components from 14,987 lines of TypeScript, fed by a Python data adapter that queries 29M+ tool calls (sessions.db, 26GB), 18K+ knowledge graph nodes (org.db, 280MB), 36 TRACK markdown files, and 40+ session logs.

Design philosophy: Snapshots first, then drillable data with rich visualizations.

---

1. Problem Statement

1.1 The Visibility Gap

Enterprise organizations adopting AI-augmented development tools face a critical visibility gap: 95% of enterprise AI pilot programs fail to deliver measurable financial returns (PwC, 2026). The root cause is not technology failure but measurement failure -- executives cannot see what AI agents are doing, how effectively they work, or what value they produce.

CODITECT captures rich trajectory data across every development session -- tool calls, agent dispatches, token usage, error patterns, decision points, file modifications, and timing data -- stored in sessions.db and org.db (ADR-118). Before this dashboard, this data was accessible only through CLI commands (/trajectory, /session-log) producing text output. There was no interactive, visual, queryable interface.

1.2 The Market Gap

Web search and competitive analysis (February 2026) reveals a fragmented landscape with no integrated solution:

AI Agent Observability Platforms

Platform	Traces	Visualizations	Generative UI	Interactive Q&A	Executive Reports
LangSmith	Yes	Static trace trees	No	No	No
LangFuse	Yes	Basic timeline	No	No	No
AgentOps	Yes	Session replay	No	No	No
Arize AI	Yes	Span waterfall	No	No	No
Weights & Biases	Yes	ML experiment charts	No	No	Limited
Helicone	Yes	Session replay	No	No	No
Datadog LLM	Yes	APM-style traces	No	No	Yes (static)

Gap: All platforms provide developer-oriented trace inspection. None dynamically generate interactive visualizations from trajectory data. None support natural-language Q&A over agent sessions. None produce executive-ready reports.

Engineering Intelligence Platforms

Platform	AI Agent Data	Dynamic Viz	Executive Dashboards	AI-Generated Insights
LinearB	No (Git metrics)	Static	Yes	Limited
Jellyfish	No (Jira/Git)	Static	Yes	Limited
Pluralsight Flow	No (Git)	Static	Yes	No
Swarmia	No (Git/CI)	Static	Yes	No

Gap: These platforms track engineering productivity through git commits and ticket velocity. None capture or visualize AI agent behavior, tool usage, or decision-making patterns.

1.3 CODITECT's Unique Position

CODITECT is the only platform that:

1. Owns the trajectory data -- sessions.db captures every agent dispatch, tool call, and decision
2. Has an agent ecosystem -- 776 agents, 445 skills, 118 hooks, 377 commands
3. Can dog-food -- CODITECT agents analyze CODITECT agent sessions
4. Runs local-first -- no cloud dependency for sensitive enterprise data
5. Multi-agent orchestration -- the dashboard itself is built by coordinating multiple specialized agents

No competitor combines: trajectory data + AI analysis + D3.js visualizations + data-driven narratives + executive snapshots + local dev server.

---

2. Decision

Implement the Agentic Trajectory Dashboard as a standalone React application within CODITECT (tools/trajectory-dashboard/) using a data pipeline architecture that transforms raw SQLite data into interactive executive BI dashboards.

2.1 Architecture Overview

Data Sources                    Pipeline                     Presentation
─────────────                   ────────                     ────────────
sessions.db (26GB)          ┌─────────────────┐          ┌──────────────────┐
  - tool_analytics (29.5M)  │                 │          │  React 18 + TS   │
  - token_economics (52M)   │  Python Data    │  JSON    │  ─────────────── │
  - agent_dispatches (183K) │  Adapter        │ ──────►  │  12 Pages        │
  - session_events (449)    │  (811 lines)    │          │  37 Components   │
                            │                 │          │  14 D3 Viz       │
org.db (280MB)              │  Aggregation    │          │  Tailwind CSS    │
  - kg_nodes (18,323)       │  + Analysis     │          │  Dark Mode       │
  - decisions (1,856)       │  + Patterns     │          │                  │
  - skill_learnings (1,029) └─────────────────┘          │  Vite 6 HMR     │
                                                          │  Port 5174       │
TRACK Files (36 .md files)                                │                  │
Session Logs (40+ .md files)                              └──────────────────┘
projects.db (project registry)                                    │
                                                                  ▼
                                                           Browser (auto-launch)

2.2 Key Architecture Decisions

#	Decision	Rationale	Alternatives Rejected
1	Vite 6 + React 18 + TypeScript strict	Industry-standard, fast HMR (<100ms), strong type safety, zero TS errors enforced	Next.js (too heavy for local tool), Svelte (team familiarity), plain HTML (insufficient interactivity)
2	D3.js for all custom visualizations	Full control over SVG rendering, animation, interactivity; supports force-directed graphs, sunbursts, treemaps, radial charts	Recharts (insufficient for force graphs, sunbursts), Chart.js (limited customization), Nivo (too opinionated)
3	Tailwind CSS for layout and styling	Utility-first enables rapid iteration, dark mode via dark: prefix, consistent design system, no CSS-in-JS runtime cost	Styled Components (runtime overhead), CSS Modules (slower iteration), Material UI (too opinionated)
4	Python data adapter (not API server)	Pre-computes all data into single JSON file; eliminates runtime SQLite queries, CORS config, and server management; instant page loads	Express API (complexity, CORS, process management), direct SQLite from browser (WASM bloat), streaming (premature)
5	JSON file as data transport	Simple, debuggable, works with Vite's static serving; data regenerated on demand via CLI	WebSocket (premature for v1), REST API (unnecessary process), GraphQL (over-engineered)
6	Local-first, no cloud dependency	Enterprise data sovereignty; sensitive session data never leaves the machine; works offline	Cloud dashboard (data sovereignty concerns), hybrid (complexity), SaaS (lock-in)
7	Snapshot-first progressive disclosure	Executives see KPIs instantly, then drill into details; reduces cognitive load; matches BI best practices	Detail-first (overwhelming), flat layout (no hierarchy), wizard (too linear)
8	Data-driven narrative generation	useMemo computes insights from raw data (hottest track, balance score, velocity trend); narratives generated client-side, not pre-baked	LLM-generated narratives (latency, cost), static text (stale), no narrative (raw data only)
9	Lucide React icons	Consistent, tree-shakeable, 1,000+ icons, MIT license, matches Tailwind aesthetic	Heroicons (fewer icons), Font Awesome (larger bundle), custom SVGs (maintenance burden)
10	React Router v6 with layout routes	AppShell wraps all pages via <Outlet />; deep linking works; sidebar nav auto-highlights active route	Hash router (ugly URLs), manual routing (no deep linking), tabs-only (no bookmarkable views)

---

3. User Stories and Value Propositions

3.1 Primary Persona: Engineering Executive / VP of Engineering

"As a VP of Engineering, I want to see at a glance whether our AI-augmented development program is on track, so I can report confidently to the board and identify where to invest more."

Value delivered:

• HomePage CompletionGauge shows overall project health (44.2% complete, 1,233/2,789 tasks) with health indicator (On Track / Attention / At Risk)
• Track Progress Strip shows all 36 tracks sorted by % complete -- instantly reveals lagging areas
• Animated metrics (sessions, tool calls, tokens, success rate) provide visceral sense of scale
• One-click drill-down from any metric to its dedicated page

Design decisions supporting this story:

• CompletionGauge uses D3 semicircular arc with gradient coloring (red → amber → green)
• AnimatedCounter uses requestAnimationFrame with easeOutCubic for 1.2s count-up animation
• Track Progress Strip bars are color-coded by completion percentage and clickable
• StatusBadge grid shows Complete/In Progress/Deferred/Remaining at a glance

3.2 Secondary Persona: Engineering Manager

"As an Engineering Manager, I want to understand how AI agents are spending their time across my team's tracks, so I can identify bottlenecks and rebalance effort."

Value delivered:

• ProjectsPage (4 tabs: Story, Velocity, Focus, Balance) provides deep project-level analysis
• ProjectCharter generates a narrative "story" of the project using data-driven prose
• ProjectVelocityChart (D3 stacked area) shows monthly activity trends per project
• ProjectFocusSunburst (D3) reveals tool call concentration across tracks -- highlights over-investment
• ProjectTrackRadar (D3) plots multi-axis track comparison for balance assessment
• Focus Interpretation panel identifies the "hottest track" (highest tool call share) and gap tracks (<40% complete)
• Balance Interpretation panel computes standard deviation of completion percentages and assigns a balance score (balanced/moderate/lopsided)

Design decisions supporting this story:

• highlightProject prop scopes all 4 D3 charts to the selected project, dimming others
• useMemo computes focusInsights and balanceInsights from track data
• Narrative panels use conditional prose generation ("your highest-effort track absorbs X% of all activity")
• Balance score uses standard deviation thresholds: ≤15% = balanced, ≤30% = moderate, >30% = lopsided

3.3 Tertiary Persona: Technical Lead / Staff Engineer

"As a Technical Lead, I want to explore error patterns, tool performance, and knowledge graph relationships to identify systemic issues and improvement opportunities."

Value delivered:

• ErrorsPage shows error type distribution with severity indicators and recovery rates
• ToolsPage shows all 34 tool types with success rates, call counts, and I/O metrics
• KnowledgePage with force-directed D3 graph visualization of 18K+ nodes across 13 types
• KnowledgeGraphViz supports toggleable node types, click-to-detail panels, and search
• TracksPage with hierarchical task drill-down showing actual task descriptions from TRACK files
• AgentsPage with inferred agent roles (DevOps, Code Author, Explorer) from tool usage patterns

Design decisions supporting this story:

• KnowledgeGraphViz uses D3 force simulation with collision detection and node type coloring
• PageTabs on every page include an "About This View" tab explaining data sources and interpretation
• ChartPicker component allows switching between visualization types (bar, sunburst, treemap, bubbles, radial, heatmap)
• ExplainerCard at the top of each page provides collapsible context about what the data means

3.4 Operations Persona: Development Session Historian

"As a development team member, I want to review historical session activity to understand what was accomplished, when, and track our development cadence."

Value delivered:

• SessionLogsPage shows monthly digests first (newest on top), with entry counts and expandable daily entries
• CalendarHeatmap (D3, GitHub-style) shows activity density by day with color intensity
• SkillsPage tracks 758K+ skill learnings with effectiveness radar chart
• SkillsCatalogPage shows 445 registered CODITECT skills searchable by track letter

3.5 Token Economics Persona: Cost-Conscious Engineering Leader

"As someone responsible for AI tool costs, I want to understand token consumption patterns by model and optimize our spend."

Value delivered:

• TokensPage breaks down consumption across Opus 4.6, Sonnet 4.5, and Haiku 4.5
• TokenSunburst (D3) shows hierarchical token distribution by model → prompt/completion
• Per-model cost breakdown: input, output, cache read/write with USD totals
• Sparkline components show 7-30 day trends inline with metric cards
• Summary metrics show 4.8T+ total tokens across all sessions

---

4. Data Architecture

4.1 Data Sources

sessions.db (Tier 3 -- Regenerable)

Table	Rows	Purpose	Dashboard Usage
tool_analytics	29.5M+	Every tool invocation: tool name, success, agent, task_id	Timeline, Tools, Agents, Tracks, Errors
token_economics	52M+	Token counts per request: input, output, cache, model, cost	Tokens, Summary metrics, Cost analysis
messages	2.1M+	Raw conversation messages with role, content, timestamps	Session count aggregation
agent_dispatches	183K+	Agent session dispatches with type, status, duration	Agents page, agent identity resolution
session_events	449+	Session-level metadata: start/end times, project, machine	Timeline, session count

org.db (Tier 2 -- Irreplaceable)

Table	Rows	Purpose	Dashboard Usage
kg_nodes	18,323	Knowledge graph: functions, components, decisions, ADRs, tracks	Knowledge page, graph viz
kg_edges	11,756	Relationships: CALLS, USES, DEPENDS_ON, SUPERSEDES, SOLVES	Knowledge graph edges
decisions	1,856	Architecture and design decisions with rationale	Decision activity timeline
skill_learnings	1,029	Learned skills with effectiveness scores	Skills page, effectiveness radar
error_solutions	475	Error-to-solution pairs for reuse	Error recovery analysis

TRACK Files (36 markdown files)

• Source: internal/project/plans/tracks/TRACK-*.md
• Parsed by: parse_track_files() in data adapter
• Extracts: task IDs, descriptions, completion status ([x]/[ ]), **Progress:** XX%
• Provides: task descriptions, % complete, completed/total task counts

Session Logs (40+ markdown files)

• Source: ~/.coditect-data/session-logs/projects/PILOT/*/SESSION-LOG-*.md
• Parsed by: parse_session_logs() in data adapter
• Extracts: date, entry count, topics, file sizes
• Provides: monthly activity digest, daily entry details

projects.db (Project Registry)

• Source: ~/.coditect-data/context-storage/projects.db
• Provides: project metadata (name, type, path, framework, language)
• Enriches: session log data with project association

4.2 Data Pipeline

┌──────────────────────────────────────────────────────────────────┐
│  dashboard_data_adapter.py (811 lines Python)                    │
│                                                                  │
│  CLI: python3 scripts/trajectory/dashboard_data_adapter.py       │
│       --timeframe all --pretty                                   │
│                                                                  │
│  Functions:                                                      │
│  ├── get_session_summary()      → SessionSummary                 │
│  ├── get_timeline_data()        → TimelineDataPoint[]            │
│  ├── get_agent_activity()       → AgentActivityEntry[]           │
│  ├── get_token_usage()          → TokenUsageEntry[]              │
│  ├── get_error_distribution()   → ErrorEntry[]                   │
│  ├── get_tool_performance()     → ToolPerformanceEntry[]         │
│  ├── get_knowledge_base()       → KnowledgeBaseData              │
│  ├── get_track_activity()       → TrackEntry[]                   │
│  ├── get_skill_breakdown()      → SkillBreakdownEntry[]          │
│  ├── parse_track_files()        → TrackEntry[] (merged)          │
│  ├── parse_session_logs()       → SessionLogMonth[]              │
│  ├── get_projects()             → ProjectEntry[]                 │
│  ├── generate_executive_summary() → ExecutiveSummary             │
│  └── detect_patterns()          → PatternDetection               │
│                                                                  │
│  Output: tools/trajectory-dashboard/src/generated/data.json      │
└──────────────────────────────────────────────────────────────────┘

4.3 TypeScript Type System

All data interfaces defined in src/types/dashboard.ts (319 lines, 28 interfaces):

Interface	Properties	Purpose
DashboardData	12 fields	Root container for all dashboard data
SessionSummary	12 fields	Aggregate metrics (sessions, tokens, cost, success rate)
TimelineDataPoint	6 fields	Per-day time series (date, sessions, tokens, cost, tool calls, errors)
AgentActivityEntry	12 fields	Per-agent stats with inferred role and top tools
TokenUsageEntry	10 fields	Per-model token breakdown with cost
ErrorEntry	4 fields	Error type distribution
ToolPerformanceEntry	12 fields	Tool duration, success, I/O metrics
PatternDetection	3 fields	Bottlenecks, error clusters, token spend insights
KnowledgeBaseData	6 fields	KG summary, distributions, decisions, skills, errors
TrackEntry	11 fields	Track with % complete, tasks, domain, status
TaskEntry	6 fields	Individual task with description and status
ProjectEntry	13 fields	Project with metadata, activity, session log months
SessionLogMonth	3 fields	Monthly session log grouping
ExecutiveSummary	11 fields	Overall health, track status counts, top active tracks
ViewOptions	5 fields	User preferences for chart engine, mode, toggles
FilterOptions	6 fields	Data filtering (tools, agents, models, thresholds)

---

5. Component Architecture

5.1 Design System

Visual Language:

• Background: gray-50 (light) / gray-950 (dark)
• Cards: white / gray-900 with gray-200 / gray-800 borders
• Interactive: hover border color by semantic category (sky, blue, green, cyan, purple, red, indigo, orange, amber, teal, lime)
• Typography: system font stack, text-sm body, text-xs labels, text-[10px] secondary
• Icons: Lucide React, 4x4 standard size, semantic coloring per domain

Progressive Disclosure Pattern (3 levels):

1. Snapshot -- HomePage with CompletionGauge, animated metrics, track progress strip
2. Drill-down -- Dedicated pages with tabbed views, ChartPicker, filter panels
3. Detail -- DrillDownModal, DetailSlideOver, click-to-expand task descriptions

Interactivity Principles:

• Every metric animated on mount (AnimatedCounter, ProgressRing, CompletionGauge)
• Every data point hoverable (D3 tooltips with formatted detail)
• Every track/task clickable (navigation or modal expansion)
• Keyboard accessible (Cmd+K search, Escape to close modals)

5.2 Page Inventory (12 pages, 3,524 lines)

Page	Lines	Route	Tabs	Purpose	User Story
ProjectsPage	870	/projects	Story, Velocity, Focus, Balance, About	Executive project intelligence with 4 D3 story visualizations and data-driven narratives	3.2
TracksPage	550	/tracks	Executive, Cards, Tasks, Viz, About	Track management with % complete, task descriptions, sunburst + treemap visualizations	3.2, 3.3
HomePage	319	/	—	Executive snapshot: CompletionGauge, track strip, animated metrics, navigation cards	3.1
AgentsPage	287	/agents	Overview, Bubbles, About	Agent identity resolution, role inference, tool profiles, bubble chart	3.3
SkillsPage	272	/skills	Overview, Effectiveness, About	Skill learnings (758K+), effectiveness radar, per-skill breakdown	3.4
SessionLogsPage	241	/session-logs	Monthly, Heatmap, Full, About	Monthly digest, CalendarHeatmap, expandable daily entries	3.4
KnowledgePage	225	/knowledge	Graph, Inventory, About	Interactive D3 force graph (18K nodes), knowledge inventory	3.3
SkillsCatalogPage	193	/skills-catalog	Overview, About	445 registered skills searchable by track letter	3.4
TimelinePage	163	/timeline	Chart, Heatmap, About	Session activity over time, heatmap calendar	3.4
TokensPage	160	/tokens	Overview, Sunburst, About	Token consumption by model, cost analysis	3.5
ToolsPage	128	/tools	Overview, About	Tool performance: success rates, call counts, I/O	3.3
ErrorsPage	116	/errors	Overview, About	Error type distribution, recovery rates, trends	3.3

5.3 Component Inventory (37 components, 7,456 lines)

D3.js Visualizations (14 components)

Component	Lines	D3 Pattern	Interactivity	Data Source
ProjectCharter	586	Multi-section narrative + D3 mini-charts	Tooltip, section expand	projects, tracks
KnowledgeGraphViz	373	Force-directed simulation	Node toggle, click-to-detail, drag, zoom	kg_nodes, kg_edges
ProjectVelocityChart	312	Stacked area + trend line	Hover tooltips, highlight scoping	sessionLogMonths
ProjectFocusSunburst	306	Partition sunburst	Hover expand, sector labels	trackActivity (tool calls)
ProjectTrackRadar	288	Radial/spider chart	Axis labels, hover values	track multi-axis metrics
HeatmapCalendar	238	Calendar heatmap (GitHub-style)	Cell hover, day click	timeline daily activity
SkillRadial	218	Radial bar chart	Hover detail per skill	skillBreakdown
TrackSunburst	216	Hierarchical sunburst	Click to zoom, breadcrumb	track → section → task
SessionTimelineD3	213	Zoomable timeline with brush	Brush to zoom, pan	timeline data points
TokenSunburst	189	Sunburst partition	Hover to see model/type	token breakdown
TrackTreemap	172	Squarified treemap	Hover, click to navigate	track tool call volumes
AgentBubbles	167	Circle pack layout	Hover to see agent details	agent dispatches
CalendarHeatmap	135	Calendar grid with color scale	Day hover, cell click	session log entries
CompletionGauge	112	Semicircular arc gauge	Animated fill, center text	executive summary %

Recharts Components (5 components)

Component	Lines	Chart Type	Purpose
Dashboard	560	Composite layout	Legacy aggregated view with all chart types
TimelineChart	96	Area chart	Sessions, tokens, errors over time
ErrorHeatmap	85	List with severity indicators	Error types and recovery rates
TokenUsageBar	78	Stacked bar	Prompt vs completion tokens per model
ToolCallWaterfall	116	Horizontal bar	Tool performance comparison
AgentActivityGraph	62	Horizontal bar	Agent dispatch frequency

Interactive UI Components (8 components)

Component	Lines	Purpose	Design Decision
AnimatedCounter	91	Count-up animation from 0 → target	requestAnimationFrame + easeOutCubic over 1.2s; supports K/M/T suffixes, %, commas
ProgressRing	86	SVG animated arc for % complete	Auto-coloring: green ≥80%, amber ≥50%, red <50%; size variants sm/md/lg
Sparkline	82	Inline D3 mini trend line (60x24px)	Green = up trend, red = down; 7-30 data points; used inline in metric cards
DrillDownModal	94	Slide-over detail panel (right side)	60% width, Escape to close, breadcrumb trail, overlay click to dismiss
DetailSlideOver	209	Extended detail panel	Full data tables, sub-charts, contextual information
FilterPanel	252	Multi-faceted data filtering	Tool, agent, model, error type filters with min thresholds
ViewToggle	189	Chart/table view switching	Render prop pattern for flexible chart swapping
ChartPicker	56	Visualization type selector	Pill buttons to switch between D3 chart types per page

Shell & Navigation Components (6 components)

Component	Lines	Purpose	Design Decision
AppShell	340	Layout: header + sidebar + footer	Collapsible sidebar (w-56 ↔ w-12), localStorage persistence, grouped nav sections
SearchModal	297	Global Cmd+K search overlay	Searches across all data dimensions, fuzzy matching, keyboard navigation
DateRangePicker	361	Two-month calendar with time inputs	Portal-rendered, custom date range with hour:minute precision, preset buttons
NotificationPanel	220	Alert/notification panel	Positioned relative to bell icon anchor, shows bottlenecks + error clusters
PageTabs	106	Tabbed container	Auto-generates "About This View" tab for every page; URL hash sync
MermaidDiagram	162	Mermaid.js renderer	Dynamic diagram generation from data, dark mode support

Contextual Components (4 components)

Component	Lines	Purpose	Design Decision
PatternInsights	249	AI pattern detection display	Bottlenecks, error clusters, token spend — actionable insight cards
InfoTooltip	72	Contextual (i) icon tooltip	Appears on hover, positioned smartly, explains individual metrics
ExplainerCard	68	Collapsible explanation card	Top of every page, explains what data means and where it comes from
PageTabs (shared)	—	Built-in About tab	Every page auto-gets an "About This View" tab with data source explanation

5.4 Library Layer (14 files, 4,007 lines)

File	Lines	Purpose
DashboardContext.tsx	~200	React context: data, timeFrame, filters, patterns, data source detection
data-loader.ts	~150	Fetches data.json, detects live vs sample, validates structure
filters.ts	~180	Client-side data filtering engine (tool, agent, model, thresholds)
patterns.ts	~200	Pattern detection: bottlenecks, error clusters, token spend analysis
format.ts	~60	Number formatting: compact (1.2K, 4.8T), currency, percentage
utils.ts	~100	cn() utility, buildTrackProjectMap(), getTrackProject()
theme.tsx	~80	Dark mode context: system preference detection, toggle, localStorage
sample-data.ts	~400	Realistic development sample data for offline/demo usage
a2ui-tree.ts	~120	A2UI component tree generation from data
a2ui-codegen.ts	~100	A2UI → JSX code generation
a2ui-pipeline.ts	~80	Full A2UI generation pipeline orchestration
mermaid-generator.ts	~100	Dynamic Mermaid diagram generation from dashboard data
Tests (5 files)	~600	Unit tests: a2ui-codegen, a2ui-tree, data-loader, filters, patterns, performance

---

6. Navigation Architecture

6.1 Sidebar Navigation (Grouped)

Overview      [LayoutDashboard]
──────────────────────────────
PROJECT
  Projects    [FolderKanban]
  Tracks      [GitBranch]
  Session Logs [FileText]
──────────────────────────────
ANALYTICS
  Timeline    [Activity]
  Agents      [Bot]
  Tools       [Wrench]
  Tokens      [Coins]
  Errors      [AlertTriangle]
──────────────────────────────
KNOWLEDGE
  Knowledge   [Brain]
  Skill Learnings [Sparkles]
  Skills Catalog  [Library]

Design decisions:

• 4 sections with semantic headings (Project, Analytics, Knowledge)
• Section headings shown when sidebar expanded, divider lines when collapsed
• Active route highlighted with blue accent (bg-blue-50 dark:bg-blue-900/20)
• Collapsed mode shows icons only (w-12), expanded shows labels (w-56)
• State persisted in localStorage for cross-session consistency

6.2 Header

• Logo + title (hidden on mobile)
• Search button (Cmd+K shortcut)
• Time frame selector (Today, This Week, Sprint, This Month, All Time)
• Custom date range picker (ChevronDown trigger)
• Data source indicator (Live / Sample with colored dot)
• Alert bell with count badge (bottlenecks + error clusters)
• Dark/light mode toggle

6.3 HomePage Navigation Cards

11 cards in a responsive grid (grid-cols-2 sm:grid-cols-3 lg:grid-cols-4 xl:grid-cols-6) linking to all pages with:

• Semantic color per domain (sky=projects, blue=timeline, green=agents, etc.)
• Icon, title, description, live metric count
• Hover border color accent

---

7. Design Principles and Patterns

7.1 Snapshot-First Information Hierarchy

Every page follows a 3-level disclosure pattern:

Level	What the User Sees	Interaction
L1: Snapshot	Key metric, health indicator, trend direction	Visible on page load
L2: Breakdown	Tabbed views with charts, tables, distributions	Click tab or card
L3: Detail	Individual record, full context, related data	Click item → modal/panel

Example flow (Tracks):

1. L1: Track Progress Strip on HomePage shows all 36 tracks with % bars
2. L2: Click track → TracksPage Executive View with task counts, domain, agent
3. L3: Click task → DrillDownModal with description, tool calls, agents, sessions

7.2 Data-Driven Narrative Generation

Instead of static labels, components compute interpretive prose from data at render time:

// ProjectsPage FocusTab — computed narrative
const focusInsights = useMemo(() => {
  const hottestShare = (hottestTrack.toolCalls / totalCalls) * 100;
  const gapTracks = tracks.filter(t => t.percentComplete < 40 && t.totalTasks > 0);
  return { hottestTrack, hottestShare, gapTracks };
}, [projectTracks]);

// Renders: "Track H (Framework Autonomy) absorbs 34% of all tool activity.
//           3 tracks below 40% completion may need attention: G, K, L"

Design decision: Client-side narrative generation (not LLM) ensures:

• Zero latency (computed in useMemo)
• Deterministic output (same data → same narrative)
• No API cost per render
• Works offline

7.3 D3.js Integration Pattern

All D3 visualizations follow a consistent pattern:

export function SomeChart({ data, width, height }: Props) {
  const svgRef = useRef<SVGSVGElement>(null);

  useEffect(() => {
    if (!svgRef.current) return;
    const svg = d3.select(svgRef.current);
    svg.selectAll("*").remove();      // Clean slate
    // ... D3 rendering logic
    const tooltip = d3.select("body").append("div")...;  // Body-mounted tooltip
    return () => { tooltip.remove(); };  // Cleanup
  }, [data, width, height]);           // Re-render on data/size change

  return <svg ref={svgRef} width={width} height={height} />;
}

Design decisions:

• useRef for SVG element access (no D3 DOM creation)
• Full clear + redraw on dependency change (simpler than D3 enter/update/exit)
• Body-mounted tooltips (escape overflow:hidden containers)
• Cleanup function removes tooltips on unmount

7.4 Animation System

Component	Technique	Duration	Easing
AnimatedCounter	requestAnimationFrame	1.2s	easeOutCubic
ProgressRing	SVG stroke-dasharray transition	1s	ease-out
CompletionGauge	D3 arc interpolation	1.5s	d3.easeCubicOut
Sparkline	D3 path animation	0.8s	linear
Track bars	CSS transition-all duration-700	0.7s	default

Design decision: Animations provide visceral feedback that data is real and dynamic. Count-up animations on every page load reinforce that metrics are computed fresh, not cached. Animation duration is fast enough to not delay comprehension (all <1.5s).

7.5 Dark Mode Implementation

• System preference detection via matchMedia('(prefers-color-scheme: dark)')
• Manual toggle persisted to localStorage
• Tailwind dark: prefix on all color utilities
• D3 charts: dark mode classes applied to SVG elements (dark:stroke-gray-700)
• Consistent palette: gray-50→gray-950 background, gray-200→gray-800 borders

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8. Analytics Dimensions

The dashboard enables exploration across eight analytical dimensions:

8.1 Time

• Daily/weekly/monthly/sprint/all session activity trends
• Heatmap calendar showing activity density by day-of-week and hour
• Custom date range with hour:minute precision
• Sprint-aligned (14-day) analysis windows

8.2 Project

• Multi-project comparison with association badges
• Per-project narrative ("story") with charter, velocity, focus, balance
• Session log months grouped by project
• Project type, language, framework metadata

8.3 Track (37 tracks, A-AK)

• Task-level drill-down with descriptions from TRACK markdown files
• % complete from parsed [x]/[ ] checkboxes
• Hierarchical task tree: Track → Section → Task → Subtask
• Domain metadata from knowledge graph (Backend API, Frontend UI, DevOps, etc.)

8.4 Agent

• Agent identity resolution from session hashes to inferred roles
• Tool usage profiling: DevOps/Build, Code Author, Explorer/Researcher, Web Researcher
• Success rate tracking per agent
• "Unknown" agent attribution: 88.5% of tool calls predate agent tracking

8.5 Tool (34 tool types)

• Tool call frequency and success rates
• Tool category grouping (file ops, search, execution, AI)
• I/O volume metrics (input bytes, output bytes)
• Error rates per tool

8.6 Token

• Model-level consumption: Opus 4.6, Sonnet 4.5, Haiku 4.5
• Prompt vs completion ratio analysis
• Cache efficiency: read vs write token breakdown
• Cost analysis per model with USD totals

8.7 Error

• Error type distribution and frequency
• Recovery rate tracking
• Error clustering by time period and trend direction
• Correlation with agent and tool usage

8.8 Knowledge

• Interactive force-directed graph with 13 node types
• Edge relationship visualization (7 edge types)
• Node type toggling for focused exploration
• Decision activity timeline and confidence scores

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9. Consequences

9.1 Positive

• Executive visibility: Directly addresses the "95% of AI pilots fail to show ROI" problem with at-a-glance project health
• Dog-fooding showcase: CODITECT agents building CODITECT intelligence dashboards -- proves the platform's own value
• Data sovereignty: Local-first architecture avoids cloud observability vendor lock-in; sensitive session data never leaves the machine
• New product category: AI Development Intelligence (ADI) -- no direct competitor provides trajectory + visualization + narrative
• Enterprise sales enabler: Executive dashboards are a procurement decision accelerator; visual proof of AI development ROI
• Compliance/audit trail: Visual evidence of AI-assisted development decisions and knowledge growth
• Zero-config deployment: Single npm run dev launches everything; auto-detected data sources

9.2 Negative

• Bundle size: 916 KB JS (gzipped 265 KB) exceeds Vite's 500KB warning; D3.js is the primary contributor
• Data staleness: JSON file must be regenerated manually (python3 adapter.py --timeframe all); not real-time
• Python dependency: Data adapter requires Python 3 + SQLite; adds installation complexity
• No authentication: Local-only tool; no multi-user access control
• Limited mobile support: D3 visualizations not optimized for touch/small screens

9.3 Known Limitations

Limitation	Impact	Status
Agent names are session hashes	No human-readable agent names	Known; requires agent naming protocol
88.5% of tool calls lack agent attribution	Agent analytics incomplete	Known; pre-tracking historical data
Duration data unavailable in tool_analytics	No latency analysis	Known; column not instrumented
Skill learnings 98% needs_work	Skills page shows skewed effectiveness	Known; extraction calibration issue
Bundle exceeds 500KB	Vite build warning	Deferred to Phase 14 (code splitting)
No real-time updates	Data must be regenerated via CLI	Deferred to Phase 15 (WebSocket)
No ARIA labels or skip links	Screen readers cannot navigate D3 charts	Planned Phase 11
No responsive breakpoints	D3 charts overflow on <1024px viewports	Planned Phase 11
No prefers-reduced-motion	Animations run for motion-sensitive users	Planned Phase 11
No design tokens file	Colors/spacing hardcoded in components	Planned Phase 14

9.4 Stakeholder Review Findings (2026-02-08)

Two structured evaluations were conducted against this ADR: a UI Designer review and a Product Manager review.

UI Designer Review: B+ (83/100)

Dimension	Score	Key Findings
Visual Hierarchy	4/5	CompletionGauge + AnimatedCounter create strong executive focus
Design System	3.5/5	No centralized design tokens file; colors/spacing embedded in components
Interactivity	4.5/5	D3 transitions, drill-down modals, and hover details are best-in-class
Navigation	3/5	12 pages in sidebar risks cognitive overload; needs progressive disclosure grouping
Accessibility	2/5	CRITICAL -- no ARIA labels, no skip links, no prefers-reduced-motion, D3 charts are opaque to screen readers
Responsive	2.5/5	CRITICAL -- D3 charts use fixed viewBox; no mobile layouts; no touch targets
Data Visualization	4/5	14 D3 + 5 Recharts covers all executive needs; colorblind mode missing
Completeness	3.5/5	Strong analytics coverage; export and feedback loops absent

Priority recommendations: Accessibility + Responsive (P0), Design Tokens + Nav Hierarchy (P1), Colorblind Mode + Export (P2).

Product Manager Review: 4/5 Stars

Dimension	Score	Key Findings
Persona Coverage	3/5	VP Eng, Manager, Tech Lead covered; missing Board Member, Customer Success, Sales Engineer, Platform Engineer
Value Proposition	3.5/5	Feature-focused ("14 D3 visualizations") rather than outcome-focused ("reduce AI pilot review time by 80%")
Success Metrics	1/5	CRITICAL -- no KPIs defined for user adoption, engagement, or business impact
Roadmap Prioritization	2/5	Technology-driven (bundle optimization before PDF export); should be value-driven
Competitive Positioning	4/5	ADI category creation is strong; needs sharper "so what" for procurement decisions
Risk Mitigation	2.5/5	User adoption (60%) and data quality (100%) risks are unmitigated

Priority recommendations: Define success metrics (immediate), reorder roadmap to value-driven sequence, add PDF export before bundle optimization, add user feedback mechanism.

9.5 Success Metrics (Post-Review Addition)

Addressing the PM review's critical gap (1/5), the following KPIs define success for the dashboard:

Adoption Metrics

Metric	Target (90 days)	Measurement
Weekly active users	5+ (internal team) → 50+ (pilot customers)	Track npm run dev invocations or page loads
Pages viewed per session	4+ (out of 12)	Client-side analytics event
Return rate	60%+ users return within 7 days	Session cookie tracking
Time to first insight	<30 seconds from dashboard load	CompletionGauge + Track Strip render time

Engagement Metrics

Metric	Target	Measurement
Drill-down usage	40%+ of sessions click through to detail views	Click event tracking on DrillDownModal
Search usage	20%+ of sessions use Cmd+K or page filters	Search modal open events
Date range changes	30%+ of sessions change from default 7-day window	DateRangePicker interaction events
D3 chart interactions	2+ hover/click events per session per chart	D3 event listener callbacks

Business Impact Metrics

Metric	Target	Measurement
Executive review time reduction	80% vs manual CLI queries	Before/after time study
Pilot-to-paid conversion lift	Attributable 10%+ improvement	A/B: customers with dashboard access vs without
Support ticket reduction	30% fewer "what did the AI do?" questions	Support ticket categorization
Sales cycle impact	Dashboard demo reduces evaluation time by 2+ weeks	CRM deal stage duration

Data Quality Metrics

Metric	Target	Measurement
Agent attribution coverage	>50% (currently 11.5%)	agent_analytics table fill rate
Data freshness	<1 hour for active projects	Adapter last-run timestamp vs current time
Zero-value metric rate	<5% (currently addressed in Phase 8)	Null/zero audit on generated JSON

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10. Build Status

10.1 Metrics

Metric	Value
TypeScript errors	0 (strict mode)
Build time	~2.3s
Bundle size (JS)	916 KB (gzipped: 265 KB)
Bundle size (CSS)	43 KB (gzipped: 7 KB)
Source lines	14,987
Pages	12
Components	37
D3 visualizations	14
Recharts charts	5
TypeScript interfaces	28
Library modules	14
Unit tests	5 files
Data adapter	811 lines Python

10.2 Implementation Phases

Phase	Task	Status	Deliverables
1	Core Infrastructure (J.29.1)	COMPLETE	Vite scaffold, slash command, data adapter, agent/skill definitions
2	AI Analysis Layer (J.29.2)	COMPLETE	Pattern detection, narrative generation, time-frame selector
3	Generative UI Engine (J.29.3)	COMPLETE	27 initial components, D3 charts, dark mode, AppShell
4	Live Data Integration (J.29.4)	COMPLETE	token_economics fix, agent identity, track correlation
5	Deep Analytics (J.29.5)	COMPLETE	React Router, 9 pages, ChartPicker, KG viz, PageTabs
6	Documentation & Testing (J.29.6)	COMPLETE	CLAUDE.md, unit tests, performance benchmarks
7	Executive BI Dashboard (J.29.7)	COMPLETE	CompletionGauge, AnimatedCounter, track strip, session logs page, skills catalog
8	Data Adapter Enrichment (J.29.8)	COMPLETE	Real I/O, cost, category, status data; fixed all zero-value metrics
9	Projects Page (J.29.9)	COMPLETE	Project registry integration, multi-project awareness
10	Executive Story (J.29.10)	COMPLETE	4 D3 story components, narratives, focus/balance insights, nav grouping

10.3 Governing ADRs

ADR	Relevance to Dashboard
ADR-054	Track nomenclature -- task ID format (A.1.1.3) used in drill-down
ADR-116	Track architecture -- 37 tracks across A-AK, domain classification
ADR-118	Database architecture -- sessions.db and org.db as data sources
ADR-136	Experience framework -- skill metadata and effectiveness tracking
ADR-155	Session log management -- project-scoped log paths and format
ADR-162	Progressive component disclosure -- snapshot-first design pattern

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11. Future Vision

Roadmap reordered 2026-02-08 based on UI Designer and PM stakeholder reviews. Value-driven prioritization: accessibility and user-facing value before internal optimization.

Phase 11: Accessibility + Responsive Design (P0 -- Next)

• ARIA labels and roles on all D3 chart containers
• Skip links for keyboard navigation between page sections
• prefers-reduced-motion media query to disable AnimatedCounter/ProgressRing/CompletionGauge animations
• Screen reader text alternatives for D3 visualizations (data table fallback)
• Responsive breakpoints: 1440px (full), 1024px (collapsed sidebar), 768px (stacked layout), 480px (mobile)
• Touch-friendly D3 interactions (44px minimum tap targets)
• Colorblind-safe palette with pattern overlays for D3 charts

Phase 12: Export & Reporting (Moved from Phase 15 -- PM Priority)

• One-click PDF export of executive summary (CompletionGauge + Track Strip + key metrics)
• CSV data export for any chart or table
• Scheduled report generation (weekly digest email)
• Board-ready presentation slide generation
• Shareable dashboard snapshots (static HTML export)

Phase 13: User Feedback + Analytics Instrumentation (PM Priority)

• Client-side event tracking for success metrics (see Section 9.5)
• In-dashboard feedback widget ("Was this insight helpful?")
• Page load and interaction timing for performance monitoring
• User session recording for UX optimization (privacy-safe, local-only)

Phase 14: Bundle Optimization + Design System (Deferred from Phase 11)

• Code splitting with React.lazy() for page-level chunks
• D3 tree-shaking (import only needed modules)
• Target: <500KB main bundle (currently 916KB)
• Centralized design tokens file (tokens.ts): colors, spacing, typography, shadows
• Component style migration from inline Tailwind to token references
• Navigation progressive disclosure (collapsible groups, recently visited)

Phase 15: Predictive Analytics + Real-Time (Deferred)

• Completion date forecasting from velocity trends
• Anomaly detection on tool call patterns
• Cost forecasting based on project track complexity
• Burndown-style progress estimation
• WebSocket connection to active Claude Code sessions
• Live metric updates without page refresh
• Multi-project comparison and tenant-level aggregate views

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12. Competitive Positioning Statement

CODITECT Agentic Trajectory Dashboard reduces executive AI pilot review time by 80% -- from hours of CLI queries to 30 seconds of visual comprehension. It is the first AI Development Intelligence platform that answers the question every VP of Engineering asks: "What is the AI actually doing, and is it worth the investment?" While LangSmith shows developers traces and LinearB tracks git metrics, CODITECT transforms 29M+ tool calls into executive-first BI dashboards with 12 drillable pages, 14 D3.js visualizations, and data-driven narratives that explain what happened, why it matters, and what to do next. Snapshot first. Drill down second. Local-first. Zero cloud lock-in. The dashboard that turns AI pilot skeptics into advocates.

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References

Market Research (February 2026)

• LLM Observability Tools Comparison -- AIMultiple
• Top 5 AI Agent Observability Platforms 2026 -- O-Mega
• 15 AI Agent Observability Tools 2026 -- AIMultiple
• Agent Trajectory Explorer (AAAI 2025) -- IBM Research
• AI Coding Assistant Statistics 2026 -- Panto
• 2026 AI Business Predictions -- PwC

Technical References

• D3.js -- Data-Driven Documents
• Vite HMR API -- Hot Module Replacement
• React Router v6 -- Client-side routing
• Tailwind CSS -- Utility-first CSS

CODITECT Internal

• ADR-118: Database Architecture (sessions.db, org.db)
• ADR-155: Session Log Management
• ADR-136: Experience Framework (CEF)
• ADR-054: Track Nomenclature
• ADR-116: Track Architecture
• ADR-162: Progressive Component Disclosure
• J.22: Trajectory Visualizer (predecessor tool)
• J.29: Agentic Trajectory Dashboard (TRACK-J task specification)

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Author: Claude (Opus 4.6) Track: J (Memory Intelligence) Task: J.29 Updated: 2026-02-08 Lines: 14,987 TypeScript | 811 Python | 12 pages | 37 components | 14 D3 visualizations