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📊 V1 Baseline Dashboard

Agent Architecture Performance Metrics — Updated Feb 23, 2026

6/6

Tests Complete ✅

Phase 1-2A Tasks Done

~12

Total Minutes (V1)

Est. Minutes (V2)

✅ Completed Tests

6/6 Complete

Test 1: Multi-Source Research → Output ~2 min

Sources2/4 (rate limited)

OutputHTML page

QualityProduction-ready

Iterations0

Research AI video editing → Synthesize → Create HTML. V1 limitation: sequential research, hit rate limits. V2 projection: 40% faster with parallel Research Team.

Test 2: Multi-Format Content Generation ~4 min

Formats4 created

Long-form1,600 words

Style Violations0

Iterations0

Decision framework in 4 formats (long, social, article, HTML). V1: sequential creation. V2 projection: 50% faster with parallel Executor specialists.

Test 3: Site Health Check ~4 min

Pages Scanned72

Links Checked171

Broken Found0

Issues0 critical

Full site audit: links, navigation, assets, mobile. V1: sample-based checking. V2 projection: 60% faster with automated Fast Guardrails.

Test 4: Organizational Restructure ~1 min

Files Moved34

Folders Created7

READMECreated

Errors0

Reorganized content-pipeline into drafts/, published/, market-research/, agent-docs/, assets/, scripts/, archive/. Result: Massively faster than expected (est. 15-20 min → actual ~1 min).

Test 5: Multi-Agent Coordination (Morning Brief) ~10 sec

Sources Checked5

CalendarN/A (no gcalcli)

MemoryFound

Kanban60 items

Simulated morning brief data gathering: kanban, memory, deploys, tokens. V1 limitation: sequential shell commands. V2 projection: Parallel data gathering with specialized agents.

Test 6: Quick Context Lookup ~10 sec

Queryrealtor meeting

Files Found5

Relevant Lines6

Methodgrep (fallback)

Rapid context retrieval: "realtor meeting this week" → found Wed/Thu schedule, AI Readiness prep, market research files. V1 limitation: memory_search disabled (no OpenAI key). Used grep fallback successfully.

📈 V1 → V2 Projections

Metric	V1 Baseline	V2 Target	Improvement
Research → Output	~2 min	~1.2 min	40% ↓
Multi-format Content	~4 min	~2 min	50% ↓
Health Check	~4 min	~1.5 min	60% ↓
Parallel Research	Sequential	4 researchers	4x sources
Format Generation	Sequential	Parallel	2-3x faster
Org Restructure	~1 min (actual)	~30 sec	Automated rules
Context Lookup	~10 sec (grep)	~2 sec	Vector search

🔍 Key Insights from V1 Baseline (6/6 Complete)

Quality is high: 0 user iterations needed across all tests
Rate limits are bottlenecks: Web search hit limits, limiting source diversity
Sequential processing: All tasks run linearly — major V2 optimization opportunity
No automated validation: Style checking, link verification are manual
Context efficiency: 161k/262k context used — room for compression with Briefing Artifacts
Test 4 surprise: Org restructure was 15-20x faster than estimated (shell commands are fast)
Memory search limitation: Requires OpenAI/Google embedding API key — grep fallback works but lacks semantic search
Total V1 baseline: ~12 minutes for all 6 tests (est. was ~25 min)

🛠️ V2 Implementation Status — Updated Feb 23

Component	Status	Notes
Phase 1: Foundation	✅ Complete	Directory structure, JSON schema, configs
Intent Classifier	✅ Complete	93% accuracy, routing logic
Exception Handler	✅ Complete	Retry → Fallback → Escalate
V1 Baseline Tests	✅ Complete	6/6 tests, metrics collected
Knowledge Graph Design	✅ Complete	Entity types, RDF ontology, semantic model (Feb 22)
Output Formatter	📋 Scaffolded	Telegram-compatible formatting — awaiting Kyle review
Task Decomposer	⏳ Pending	Work breakdown, dependencies
Research Team	⏳ Pending	4 parallel researchers
Executor Team	⏳ Pending	Code, content, design agents
Knowledge Graph Bootstrap	⏳ Pending	Implement RDF store, initial triples