## 🧩 SECTION 1 — Idea Normalization **Normalized Summary:** • Multi-tenant SaaS dashboard for monitoring AI agents in production environments • Real-time tracking of performance metrics (latency, accuracy, cost) across multiple LLM providers • Role-based access control for team-based workspaces • Automated alerting on performance degradation • Historical reporting and comparative analysis capabilities • Focus: operational visibility + cost optimization **Identified Ambiguity Points:** • ⚠️ "Accuracy" — unclear metric (LLM output verification impossible without external scoring) • ⚠️ "Debug failed requests" — what constitutes a "failure"? • ⚠️ "Different agents" — single agent with multiple runs or multiple distinct agent systems? • ⚠️ "Reporting" — real-time dashboards vs. scheduled reports vs. both? • ⚠️ Cost tracking scope — token-level granularity or request-level only? --- ## 🔍 SECTION 2 — Assumption Resolution Engine **Ambiguity #1: "Accuracy" Metric** • Interpretations: LLM output quality scoring | User-provided correctness tags | Response time vs. timeout ratio • Selected Assumption: **User-provided correctness tagging (manual annotation of agent responses as correct/incorrect)** • Rationale: Eliminates need for external validation; aligns with MVP scope • Risk Level: 🟡 MODERATE (requires user participation in feedback loop) **Ambiguity #2: Agent Failure Definition** • Interpretations: API errors | Token limits exceeded | Response latency > threshold | Invalid output format • Selected Assumption: **Failure = API error, timeout, or user-marked as "failed"** • Rationale: Observable, actionable; avoids subjective quality judgment • Risk Level: 🟢 LOW **Ambiguity #3: "Different Agents" Scope** • Interpretations: Separate agent instances | Same agent with different prompts | Different models/LLM providers • Selected Assumption: **Agent = unique system identifier; tracks all invocations of that agent across time** • Rationale: Simplest mental model; enables per-agent dashboards • Risk Level: 🟢 LOW **Ambiguity #4: Reporting Types** • Interpretations: Real-time dashboards only | Scheduled daily/weekly reports | Both • Selected Assumption: **MVP = real-time dashboards only; scheduled reports in Phase 2** • Rationale: Reduces MVP scope; provides core value immediately • Risk Level: 🟡 MODERATE (stakeholder expectations around "reporting") **Ambiguity #5: Cost Granularity** • Interpretations: Per-token pricing | Per-API-call | Per-agent | Aggregated only • Selected Assumption: **Track cost at API call level (aggregatable by agent, model, date range)** • Rationale: Maximum flexibility; follows user query patterns • Risk Level: 🟢 LOW **Assumption Register Summary:** • 🟢 2 low-risk assumptions (failure definition, agent scope) • 🟡 2 moderate-risk assumptions (accuracy feedback loop, phased reporting) • 🟢 1 low-risk assumption (cost tracking granularity) --- ## 👤 SECTION 3 — User & Stakeholder Mapping **Primary Users:** • **AI Platform Engineers** — Goal: Monitor agent health, debug failures, optimize performance • **SaaS Founders** — Goal: Track AI spend, compare model performance, optimize margins • **Ops Managers** — Goal: Receive alerts on anomalies, generate compliance reports **Secondary Users:** • **Finance/CFO** — Goal: Audit AI costs, forecast spend • **Security/Compliance** — Goal: Track data flow, ensure multi-tenancy isolation **Stakeholder Objectives:** • Platform engineers: rapid debugging, observability • Founders: cost control, revenue optimization (if agent-as-product) • Ops: reliability assurance, alert accuracy **Permissions Model:** • **Owner** — full access, invite users, billing settings • **Admin** — add agents, configure alerts, view all metrics • **Editor** — tag responses, acknowledge alerts, cannot modify setup • **Viewer** — read-only access to dashboards **User Goals Map:** • Engineers: "I need to see why this agent failed in production" • Founders: "Show me which model is cheapest for my use case" • Ops: "Alert me if response times exceed 5 seconds" --- ## 🔄 SECTION 4 — End-to-End User Flow Architecture **Flow #1: Setup & Agent Integration** • User logs in → navigates to Integrations • Selects LLM provider (Claude, GPT-4, Gemini) • Generates API key for agent → copies webhook URL • Installs agent SDK/listener in production code • System validates first inbound request • Agent appears on dashboard • ✅ Success state: Agent begins logging requests **Flow #2: Real-Time Monitoring (Primary Workflow)** • User lands on dashboard • System displays: all agents, 24-hour request count, error rate, avg latency, total cost • User clicks agent card → views detailed metrics (hourly breakdown, model used, cost per request) • System highlights anomalies (red alerts for >2σ deviation) • User can drill into specific request • ✅ Success state: Engineer locates failure, reviews request/response **Flow #3: Accuracy Feedback Loop** • User views failed request details • Sees: input prompt, model, output, timestamp, cost • Optionally tags response as "correct" or "incorrect" • System records feedback • Feedback aggregates into agent accuracy score • ✅ Success state: Accuracy metric becomes data-driven for that agent **Flow #4: Alert Configuration** • User navigates to Alerts • Creates rule: "If agent latency > 2s for 10+ consecutive requests, alert" • Selects notification channel (email, Slack, webhook) • System monitors in real-time • ✅ Success state: Alert triggers, user notified • Failure state: User dismisses alert, creates silence rule **Flow #5: Cost Analysis** • User navigates to Billing dashboard • Selects date range • Sees: total spend, spend per agent, spend per model, cost per request • Can export data or compare periods • ✅ Success state: User identifies cost optimization opportunity **Flow #6: Team Collaboration** • Owner invites engineer with "Editor" role via email • Engineer logs in → sees only agents they have access to (inherited from workspace) • Tags response as incorrect • Owner sees the feedback in the agent detail view • ✅ Success state: Team shares observability across role boundaries **Edge Flow — Failure States:** • Request arrives but agent not yet configured → system queues, retries on next config push • LLM provider returns error → logged as "error" type, counts against error rate • User loses permission to agent → views redirect to access-denied page • Webhook fails → system stores locally, retries async • API key expires → dashboard shows "authentication required" warning --- ## 📋 SECTION 5 — Functional Requirements Specification **🥇 MUST-HAVE Features:** • Agent registration via API key + SDK integration • Real-time request logging (input, output, model, latency, cost, timestamp) • Per-agent performance dashboard (latency histogram, error rate %, request count) • Multi-provider support (Claude, GPT-4, Gemini with separate cost tracking) • Date range filtering (24h, 7d, 30d, custom) • Error state tracking (API error, timeout, invalid response) • Cost aggregation and per-request cost attribution • Multi-workspace multi-tenancy with role-based access control (Owner, Admin, Editor, Viewer) • Email-based user invitations + role assignment • Basic alert rules (latency threshold, error rate threshold) • Email notifications for alerts • Workspace-level and agent-level permissions isolation **🥈 SHOULD-HAVE Features:** • Accuracy feedback tagging (user marks response as correct/incorrect) • Accuracy score aggregation per agent • Slack integration for alerts • 30-day request history retention • Hourly metrics granularity (charts by hour) • Comparative analysis (Agent A vs Agent B performance) • Model performance comparison (Claude vs GPT-4 accuracy/cost) **🥉 OPTIONAL Features (Phase 2+):** • Scheduled PDF reports • Webhook alerts • Advanced anomaly detection (automatic threshold tuning) • Request replay debugging • Cost forecasting • Custom metrics • Audit logs • SSO / SAML integration **Business Rules:** • Each workspace is completely isolated (no cross-workspace data leakage) • Cost calculations use official LLM provider pricing at time of request • Accuracy score = (correct_tags / (correct_tags + incorrect_tags)); undefined if no feedback • Alerts fire once per rule per agent per day (prevent alert spam) • Alerts require explicit dismissal or silence rule • Free tier: 1 agent, 7-day retention, 1 workspace member • Paid tier: unlimited agents, 30-day retention, unlimited members --- ## ⚠️ SECTION 6 — Edge Case Discovery Engine **Invalid Inputs:** • ⚠️ API key missing/invalid → return 401, log as "auth failed" • ⚠️ Request timestamp is in future → flag as anomaly, store with warning • ⚠️ Cost is negative (credit/refund) → store as negative value, note in UI • ⚠️ Agent name contains special characters → sanitize, validate, store • ⚠️ User provides malformed JSON in request body → return 400, log error **User Mistakes:** • ⚠️ User deletes active agent → confirm deletion, notify if alerts exist for that agent • ⚠️ User creates alert with impossible threshold (latency > 10,000s) → warn but allow • ⚠️ User removes all team members from workspace → prevent, show error "at least 1 owner required" • ⚠️ User tags same request as both correct AND incorrect → show conflict warning, use most recent tag • ⚠️ User invites user already in workspace → silently update role instead of duplicating **System Failures:** • ⚠️ Webhook delivery fails → retry 3x exponential backoff, then store in dead-letter queue • ⚠️ Cost calculation API down → queue request, calculate retroactively when service recovers • ⚠️ Database connection lost → return 503, client retries • ⚠️ Real-time metric aggregation lag (>5min behind) → show "data may be delayed" warning on dashboard • ⚠️ Alert rule fires while alert notification service is down → queue and replay when service recovers **Permission Issues:** • ⚠️ Viewer tries to create alert → 403 Forbidden, suggest request access upgrade • ⚠️ User from Workspace A tries to access Workspace B agent → 403 Forbidden, no data leak • ⚠️ Owner deleted → reassign ownership to most senior remaining user • ⚠️ User invites external user not yet in system → create account with "pending" status • ⚠️ Role change happens mid-session → user session remains valid until refresh, show "permissions updated" banner **Integration Failures:** • ⚠️ Claude API deprecated endpoint called → detect, log warning, gracefully degrade • ⚠️ GPT-4 returns cost as null → set to "unknown", flag in cost report as incomplete • ⚠️ Gemini API rate limit hit → implement exponential backoff, queue requests • ⚠️ Agent SDK not installed in production → system shows "no requests in 24h" warning **Data Inconsistencies:** • ⚠️ Accuracy feedback arrives for non-existent request → store but don't break accuracy score • ⚠️ Request logged without corresponding cost record → show cost as pending, update retroactively • ⚠️ Alert fires for agent that no longer exists → silently skip, clean up rule on next workspace access • ⚠️ Duplicate request IDs from same agent → deduplicate on request ID + timestamp, use first received **Expected Behaviors & Recovery:** • For webhook failure: exponential backoff, dead-letter queue, admin alert if > 100 failures in 1h • For data lag: show timestamp of last metric update, disable real-time refresh if lag > 10min • For permission denial: friendly error message with "contact workspace admin" link • For duplicate requests: log as info, count once, alert engineers if frequency > 5% of requests --- ## 🚧 SECTION 7 — Scope Fence Generator **❌ NOT INCLUDED in MVP:** • Historical trend analysis (multi-month comparisons) • Scheduled PDF/email reports • Slack, Discord, webhook integrations (email only) • Advanced anomaly detection (static thresholds only) • Request payload/response body storage (headers + summary only) • Audit logs / compliance-grade request history • Cost forecasting / budget tracking • Custom dashboards / widget builders • API for programmatic metric access • Agent performance SLA tracking • Multi-region deployment (single region only) • Caching layer optimization (PostgreSQL direct) **❌ FUTURE FEATURES (Phase 2+):** • Advanced anomaly detection with configurable sensitivity • Scheduled report delivery • Integrations: Slack, PagerDuty, Datadog webhook • Cost forecasting • Audit log retention for compliance • Custom metrics / user-defined KPIs • Request replay for debugging **❌ EXPLICITLY OUT OF SCOPE:** • Training/fine-tuning agents based on logged data • Automatic prompt optimization • LLM cost negotiation / broker features • Billing/payment processing (separate system; we just track) • Live request interception / request mutation **⚠️ Non-Goals:** • Replace vendor-provided analytics (e.g., OpenAI usage dashboard) — *complementary, not replacement* • Become a general-purpose APM tool — *AI agents only* • Support on-premise deployment in MVP — *cloud SaaS only* --- ## 🏗️ SECTION 8 — Technical Architecture Guidance **System Components:** • **API Gateway** — REST endpoint for agent webhook + authentication • **Request Logger Service** — async processor, writes to time-series DB • **Metrics Aggregator** — rolls up requests into hourly buckets (latency p50/p95/p99, error rate, cost) • **Real-Time Dashboard Backend** — WebSocket or Server-Sent Events for live metric updates • **Alert Engine** — evaluates rules against incoming metrics, triggers notifications • **Auth Service** — issues JWTs, manages workspaces + roles • **Notification Service** — email dispatch (Sendgrid or AWS SES) **Data Models:** • **Workspace** — id, name, owner_id, created_at, plan_tier • **Agent** — id, workspace_id, name, api_key_hash, model_provider, created_at, last_request_at, status • **Request** — id, agent_id, timestamp, model, latency_ms, cost_usd, error_type, input_hash, output_hash • **Feedback** — id, request_id, user_id, is_correct, tagged_at • **Alert Rule** — id, workspace_id, agent_id, metric_type, operator, threshold, notification_channel • **Team Member** — id, workspace_id, user_id, role, invited_at, accepted_at • **User** — id, email, password_hash, created_at **Integrations:** • Claude / GPT-4 / Gemini APIs (for cost lookups, not inference) • Sendgrid (email notifications) • PostgreSQL (relational data: workspaces, users, alerts) • ClickHouse or TimescaleDB (time-series metrics) • Redis (caching, rate limiting, real-time updates) **Scalability Considerations:** • Assume 10K agents per workspace, 10M requests/day per workspace • Use time-series DB for metrics (ClickHouse scales to billions of rows) • Implement request batching (flush to DB every 1s or 1000 requests) • Use Redis pub/sub for real-time metric broadcasts to WebSocket clients • Alert rules evaluated in-memory for sub-100ms latency --- ## 📊 SECTION 9 — Delivery Risk Assessment **Requirement Risk: 🔴 MODERATE** • Accuracy feedback loop requires user participation — may have low adoption if not intuitive • Mitigation: Design feedback UI with 1-click tagging, show impact on accuracy score immediately • Cost tracking accuracy depends on LLM provider API correctness — we inherit their bugs • Mitigation: Implement cost validation, alert on outliers, maintain provider-specific pricing lookup tables **Complexity Risk: 🟡 MODERATE** • Multi-tenancy isolation requires careful permission checks at every layer • Mitigation: Implement row-level security (RLS) in PostgreSQL, test extensively • Real-time metric aggregation at scale (10M+ requests/day) is non-trivial • Mitigation: Use proven time-series DB (ClickHouse), batch inserts, pre-aggregate **Timeline Risk: 🟢 LOW** • Core MVP achievable in 6-8 weeks with 2 engineers • Risk: Integration with multiple LLM provider cost APIs adds complexity • Mitigation: Start with Claude + GPT-4, add Gemini after launch **Dependency Risk: 🟡 MODERATE** • Depends on stable LLM provider APIs (Claude, OpenAI, Google) • Depends on customer-side SDK integration (agent teams must install) • Mitigation: Build offline fallback, queue requests locally, support async integration **Scalability Risk: 🟢 LOW** • Architecture designed for horizontal scaling from day one • Time-series DB proven at 100M+ events/day • Risk: PostgreSQL for relational data may bottleneck at 100K+ team members • Mitigation: Shard by workspace_id if needed, implement read replicas **🚨 Top 3 Blockers to Monitor:** • SDK adoption friction — agents must integrate actively • Cost data accuracy — reliant on provider API stability • Real-time performance under load — WebSocket scaling --- ## 🧾 SECTION 10 — Final Build Brief™ ### 1️⃣ Executive Summary **AI Agent Performance Dashboard** is a B2B SaaS monitoring platform for production AI agents. It provides real-time visibility into agent performance (latency, accuracy, error rate, cost) across multiple LLM providers, enabling platform engineers and AI-focused founders to debug failures, optimize spend, and make data-driven deployment decisions. **MVP Scope:** Single-region cloud SaaS with multi-workspace support, real-time dashboards, basic alerting, and cost tracking for Claude, GPT-4, Gemini. **Timeline:** 6-8 weeks (2 engineers) | **Target Launch:** Early adopter program --- ### 2️⃣ Product Vision Become the standard operational visibility layer for AI agents in production—the place where AI platform teams instinctively check when something goes wrong. **Core Value Props:** • See agent performance instantly (no vendor API polling) • Track actual costs per request (multi-provider comparison) • Get alerted before customers feel the impact • Team-based collaboration with role-based permissions • 30-day historical data for trend analysis --- ### 3️⃣ User Personas **Persona A: AI Platform Engineer (Primary)** • Goal: Monitor agent health, debug production failures rapidly • Pain: Vendor dashboards are slow, don't correlate cost with performance • Usage: Daily, 15+ minute sessions during incident response • Must-have: Fast request lookup, error breakdown, model comparison **Persona B: AI-Focused SaaS Founder** • Goal: Understand AI spend, optimize model selection for margin • Pain: Claude vs GPT-4 costs add up; unclear which is more accurate • Usage: Weekly, 5-minute sessions to check dashboard • Must-have: Cost breakdown by agent, accuracy tagging, cost per request **Persona C: Operations Manager** • Goal: Receive alerts on anomalies, ensure agent reliability • Pain: Manual monitoring is tedious; alerting is inconsistent • Usage: Several times/day for alert checks • Must-have: Configurable alerts, email notifications, clear incident details --- ### 4️⃣ Complete User Flows **Flow 1: Onboarding (First 5 minutes)** • User signs up with email • Creates workspace (name, industry optional) • Receives integration docs + API key • Installs SDK in one agent • Sees first request logged in real-time • Explores dashboard • ✅ Success: Agent visible, metrics flowing **Flow 2: Daily Monitoring (Typical usage)** • User lands on dashboard • Scans agent health cards (request count, error %, avg cost/req) • Clicks agent with anomaly • Views detailed breakdown (24h timeline, model used, error types) • Drills into failed request (prompt, response, error message) • Tags response as "incorrect" if applicable • Dismisses or acts on alert • ✅ Success: Issue diagnosed, action taken (escalate, rollback, tune) **Flow 3: Alert Configuration** • User navigates to Alerts tab • Creates new rule: "Latency > 3s for 5+ consecutive requests" • Selects email notification • Tests rule manually • Rule now evaluates in real-time • ✅ Success: Alert fires within seconds of threshold breach **Flow 4: Team Collaboration** • Owner navigates to Team Settings • Invites engineer: jane@company.com, role "Editor" • Jane receives email invitation • Jane logs in → sees all workspace agents, can tag responses • Owner sees Jane's tags in the feedback column • ✅ Success: Team shares observability **Flow 5: Cost Analysis** • User navigates to Billing dashboard • Selects "Last 30 days" range • Sees: Total spend ($1,200), spend by agent (Agent A: $600, Agent B: $400, Agent C: $200) • Sees: Spend by model (Claude: $700, GPT-4: $500) • Compares: cost per request for each agent • Identifies Agent B running inefficiently • Adjusts agent parameters, re-runs benchmark • ✅ Success: Cost optimized --- ### 5️⃣ Functional Requirements **Core Features (Must-Have):** 1. **Agent Registration** • User provides API key, receives webhook URL + SDK • SDK sends POST request: {agent_id, model, input_tokens, output_tokens, latency, cost, error_type, timestamp} • System validates, logs to time-series DB 2. **Real-Time Dashboard** • Displays: All agents, 24h request count, error rate %, avg latency, total cost • Clicking agent shows: Hourly metrics, model breakdown, request list (latest first) • Metrics update live every 5 seconds (WebSocket) 3. **Request Details Viewer** • Shows: Timestamp, model, latency, cost, input_summary, output_summary, error_type • User can tag response: correct / incorrect • Shows related alerts fired for this request 4. **Cost Tracking** • System receives cost from SDK, stores with request • Calculates: cost per request, cost per agent, cost per model, total spend • Supports date range filtering 5. **Error Tracking** • Logs error_type: API_ERROR, TIMEOUT, INVALID_RESPONSE, TAGGED_INCORRECT, or null (success) • Shows error breakdown: count by type, % of total requests 6. **Multi-Provider Support** • Tracks model field: "claude-3.5-sonnet", "gpt-4-turbo", "gemini-pro" • Applies correct pricing for each (cost_per_1k_input, cost_per_1k_output) • Shows cost attribution correctly 7. **Alerting Engine** • User creates rule: metric (latency/error_rate) + operator (>, <, ==) + threshold + workspace/agent scope • Rule evaluates every 10 seconds against latest metrics • Fires once per rule per agent per day • Sends email notification with rule name, current value, agent name 8. **Role-Based Access Control** • Owner: full access, can invite, delete agents, configure billing • Admin: can invite users, configure alerts, view all • Editor: can tag responses, acknowledge alerts, cannot modify setup • Viewer: read-only access to dashboards • Workspace isolation: no cross-workspace access 9. **User Invitations** • Owner sends invite via email • Link expires after 7 days • User can accept or decline • Role inherited from invite 10. **Accuracy Scoring** • Per-agent accuracy = (correct_tags) / (correct_tags + incorrect_tags) • Displayed on agent card with trend (up/down/stable) • Visible to all roles --- ### 6️⃣ Edge Case Register | Scenario | Expected Behavior | |----------|-------------------| | Agent sends request without API key | Return 401, log as "auth failed" | | User deletes agent with active alerts | Confirm deletion, cleanup related alerts | | Cost arrives late for a request | Backfill cost retroactively, update metrics | | User tags same request twice (contradictory) | Use most recent tag, show warning | | Webhook fails 3 times | Queue in dead-letter, retry async, admin alert | | Accuracy feedback for non-existent request | Store but ignore in scoring until request appears | | Viewer role tries to create alert | Return 403, show "upgrade access" message | | Alert rule threshold set impossibly high | Allow but warn user ("This threshold rarely triggers") | | Agent name contains SQL injection attempt | Sanitize, store safely, no injection | | Request timestamp is in future | Flag as anomaly, store with warning | --- ### 7️⃣ Assumption Register | Ambiguity | Assumption | Risk | Rationale | |-----------|-----------|------|-----------| | "Accuracy" metric definition | User-tagged correctness only | 🟡 MODERATE | Avoids external validation; requires user participation | | Request failure definition | API error, timeout, or tagged incorrect | 🟢 LOW | Observable, actionable | | Agent identity scope | Unique agent_id tracks all invocations | 🟢 LOW | Simple; enables per-agent dashboards | | Reporting in MVP | Real-time dashboards only; reports Phase 2 | 🟡 MODERATE | Reduces scope; core value immediate | | Cost granularity | Per-request level; aggregatable by any dimension | 🟢 LOW | Maximum flexibility | --- ### 8️⃣ Scope Fence Document **IN MVP:** ✅ Multi-workspace + role-based access control ✅ Real-time dashboards (latency, error rate, cost, accuracy) ✅ Request-level logging + details viewer ✅ Basic alerting (static thresholds) ✅ Cost tracking (multi-provider) ✅ Email notifications ✅ 30-day retention ✅ Accuracy feedback tagging ✅ Agent setup wizard **OUT OF MVP:** ❌ Scheduled PDF reports ❌ Slack / webhook / PagerDuty integrations ❌ Advanced anomaly detection (auto-tuned thresholds) ❌ Request payload/response storage (metadata only) ❌ Audit logs ❌ Cost forecasting ❌ Custom dashboards ❌ On-premise deployment ❌ Historical trend comparison (multi-month) ❌ LLM-side fine-tuning recommendations --- ### 9️⃣ Technical Recommendations **Recommended Stack:** • **Frontend:** Next.js (React) + TailwindCSS + TypeScript - Real-time updates: TanStack Query (polling) or WebSocket client library - Charts: Recharts (simple, lightweight) • **Backend:** Node.js + Express + TypeScript - API: REST (webhook endpoint + CRUD) - Auth: JWT-based (Passport.js optional) - Time-series: ClickHouse (requests) + PostgreSQL (relational data) - Caching: Redis (metrics, rate limiting, pub/sub) - Async jobs: Bull (Node.js job queue) • **Infrastructure:** - Cloud: AWS (or GCP/Azure equivalent) - Compute: Docker + ECS / Kubernetes - Database: RDS PostgreSQL + ClickHouse managed cluster - Cache: ElastiCache Redis - Email: Sendgrid or AWS SES - Storage: S3 (for exports, backups) **Architecture Overview:** ``` [Agent SDK] → [API Gateway] → [Request Logger] → [ClickHouse] ↓ [Metrics Aggregator] → [Redis] ↓ [Alert Engine] → [Email Service] ↓ [Dashboard Backend] ↓ [Next.js Frontend] ← [WebSocket] ``` **Scaling Strategy:** • Requests logged to ClickHouse in batches (1s or 1000 req threshold) • Metrics pre-aggregated hourly (latency percentiles, error rate) • Redis caches latest metrics, invalidates on new batch • WebSocket broadcasts metric updates to connected dashboards • Alert rules evaluated in-memory from Redis snapshots --- ### 🔟 Builder Handoff Specification **Ready to Build:** 1. **Phase 0 (Week 1-2): Foundation** • Set up monorepo (backend + frontend) • Implement user auth (signup, login, JWT) • Create workspace + team member models in PostgreSQL • Build role-based middleware 2. **Phase 1 (Week 2-4): Core Logging** • Implement agent webhook endpoint (/api/log-request) • Schema: {agent_id, model, latency, cost, error_type, timestamp, accuracy_feedback} • Connect to ClickHouse time-series DB • Build request list API endpoint 3. **Phase 2 (Week 4-6): Dashboard** • Agent health cards (request count, error %, avg cost) • Agent detail page (hourly metrics, request list) • Request details viewer • Real-time metric updates (Redis pub/sub to WebSocket) 4. **Phase 3 (Week 6-8): Alerts + Polish** • Alert rule creation UI + backend evaluation • Email notification dispatch • Accuracy feedback tagging • Cost breakdown dashboard • Integration tests, performance tests **Acceptance Criteria for MVP Launch:** ✅ All 10 functional requirements implemented ✅ Multi-workspace isolation validated (pen-test user A cannot see workspace B data) ✅ Alerts fire within 30 seconds of threshold breach ✅ Dashboard loads agent card in < 1s ✅ 100+ concurrent WebSocket users supported ✅ Deployment to production with automated backups ✅ Documentation: API spec, SDK integration guide, user guide **Go-Live Readiness Checklist:** - [ ] Staging environment mirrors production (data, config) - [ ] Error handling + logging at every critical path - [ ] Database indexes optimized for common queries - [ ] Load testing: 1000 requests/second sustained - [ ] Backup + recovery tested - [ ] Monitoring + alerting for system health - [ ] Security audit (auth, RBAC, data isolation) - [ ] Customer support runbook - [ ] Onboarding flow tested with 3 external users --- **END BUILD BRIEF**