Automation Surfaces

AI Layer

1. Overview

Each role in TeamFlow sees a customized automation surface—AI-generated work that's relevant to their responsibilities. This document describes what each role sees, how they interact with AI outputs, and what remains in their hands.

2. BUSINESS_ANALYST Surface

2.1 Requirement Review Queue

Where: New section in Requirement Dashboard: "AI Proposals – Ready for Review"

What BAs see:

┌─────────────────────────────────────────────────────────────────┐
│ AI Proposals – Ready for Review (3 pending)                       │
├─────────────────────────────────────────────────────────────────┤
│                                                                  │
│ ✓ User Onboarding Flow – Profile Setup Module                   │
│   Status: PROPOSED (from transcript_abc123)                     │
│   Confidence: 87%                  Timestamp: 2026-08-02 14:12  │
│   Recording: [Play] (90 min)       Transcript: [View]           │
│   ───────────────────────────────────────────────────────────── │
│   Business Objective: Reduce time-to-first-value from 8 to < 3 │
│   Actors: New User, Customer Success Manager                    │
│   Main Workflow: 5 steps (signup → verify → profile → org → ...) │
│   Acceptance Criteria: 3 items (90% < 3 min, mobile + desktop, │
│                         async email queue)                      │
│   Out of Scope: SSO integration, bulk user import               │
│   ───────────────────────────────────────────────────────────── │
│                                                                  │
│   Ambiguity Flags (2 found):                                     │
│   ⚠ "< 3 minutes" – needs baseline network speed                │
│     Questions: [1] Standard broadband? [2] Includes email time? │
│   ⚠ "See dashboard" – which dashboard?                          │
│     Questions: [1] User or admin dashboard?                     │
│                                                                  │
│   UX Suggestions:                                                │
│   📐 4-step mobile-first flow, reuse LoginForm component        │
│   [View Full UX Suggestion]                                      │
│   ───────────────────────────────────────────────────────────── │
│                                                                  │
│   [ACCEPT] [REJECT] [MODIFY] [MORE CONTEXT]                    │
│                                                                  │
└─────────────────────────────────────────────────────────────────┘

2.2 BA Actions on AI Proposals

Accept

Creates a RequirementVersion in DRAFT status
Links AIExtraction as a source artifact ("AI-Generated from Recording")
Copies all extracted fields into the requirement
Logs: "BA_ACCEPTED_AI_PROPOSAL" with BA user ID, timestamp
Next step: BA can now submit requirement for PM review

Modify

Opens an edit form pre-filled with AI-extracted data
BA can update any field: title, objective, acceptance criteria, scope, etc.
After edits, BA clicks "ACCEPT AS MODIFIED"
Logs: "BA_MODIFIED_AI_PROPOSAL" with diff of changes
All modifications are visible in requirement version history

Reject

Opens a dialog: "Why are you rejecting this proposal?"
Reason options: "Low quality", "Incomplete", "Out of scope for this project", "Off-topic", "Other"
If "Other", BA provides text
Logs: "BA_REJECTED_AI_PROPOSAL" with reason, timestamp
Extraction remains in audit log; not deleted
Recommended action: BA manually creates requirement, or records new meeting

More Context

Plays the recording segment that led to this extraction
Shows the full transcript with speaker attribution and timestamps
BA can jump to a specific time in the recording to hear surrounding conversation

2.3 Clarification Response Workflow

Where: "Clarifications – AI-Flagged" section in Requirement Dashboard

┌─────────────────────────────────────────────────────────────────┐
│ Clarifications – AI-Flagged (2 pending response)                 │
├─────────────────────────────────────────────────────────────────┤
│                                                                  │
│ ✓ "< 3 minutes" – how is this measured?                         │
│   Status: OPEN                                                   │
│   AI Confidence in Ambiguity: 92%                               │
│   Suggested Questions:                                           │
│   1. Is this measured on standard broadband (5 Mbps+)?          │
│   2. Does this include email delivery time?                    │
│   3. Is 90% of users a hard SLA or a goal?                     │
│                                                                  │
│   Historical Context: Similar term "fast" in req_456 was        │
│   resolved to "< 2 sec response time"                          │
│                                                                  │
│   ┌─────────────────────────────────────────────────────────┐  │
│   │ Your Response:                                           │  │
│   │ The 3-minute target is measured on standard broadband    │  │
│   │ (≥ 5 Mbps). It includes only user actions (form fill,   │  │
│   │ clicks); email delivery is not counted in the 3 minutes. │  │
│   │ This is a goal, not an SLA—90% is our target but not    │  │
│   │ contractual.                                             │  │
│   │                                                           │  │
│   │ [SUBMIT RESPONSE]                                       │  │
│   └─────────────────────────────────────────────────────────┘  │
│                                                                  │
│ ✓ "See dashboard" – which dashboard?                            │
│   Status: OPEN                                                   │
│   ...                                                            │
│                                                                  │
└─────────────────────────────────────────────────────────────────┘

BA Action: Type clarification response → SUBMIT

Links response to Clarification record
Logs: "BA_CLARIFIED" with timestamp
Requirement can now move forward with ambiguities resolved

2.4 Acceptance Criteria Suggestions

When editing acceptance criteria, AI offers auto-complete suggestions based on:

Historical acceptance criteria from similar requirements
Industry best practices (e.g., "Page load < 2 sec", "Mobile + desktop")
Project glossary and past clarifications

3. PROJECT_MANAGER Surface

3.1 Change Impact Dashboard

Where: "Changes & Impact – AI Analysis" section in Project Dashboard

┌──────────────────────────────────────────────────────────────────┐
│ Changes & Impact – AI Analysis (1 pending PM review)              │
├──────────────────────────────────────────────────────────────────┤
│                                                                   │
│ ✓ User Onboarding Flow – Change from v2 to v3                   │
│   Status: PROPOSED (from extraction_abc123)                     │
│   Changed From: req_v12 (APPROVED, dated 2026-07-30)           │
│   ───────────────────────────────────────────────────────────── │
│                                                                   │
│   📋 SCOPE CHANGE: MEDIUM                                         │
│   New workflow step: Org connection (validated by domain)        │
│                                                                   │
│   💼 EFFORT IMPACT: +10 hours (confidence: 72%)                 │
│   • user-auth module: +2 hrs (email async queue)                │
│   • org-api module: +5 hrs (new API integration, MEDIUM risk)   │
│   • ui-onboarding module: +3 hrs (mobile responsiveness)        │
│                                                                   │
│   ⚠ RISKS:                                                        │
│   • Third-party org validation API is new (vendor integration)  │
│   • Mobile responsiveness adds complexity                        │
│   • Email async queue is new pattern in codebase                │
│                                                                   │
│   📊 HISTORICAL CONTEXT:                                          │
│   Similar requirement (req_456) was estimated at 12 hrs,        │
│   took 14 hrs (original estimate: 8 hrs). Scope grew 15%.       │
│                                                                   │
│   ✅ RECOMMENDED ACTIONS:                                         │
│   • Schedule design review before implementation                 │
│   • Verify org validation API contract with vendor              │
│   • Create spike for email async queue pattern                  │
│   ───────────────────────────────────────────────────────────── │
│                                                                   │
│   [APPROVE CHANGE] [REQUEST MORE ANALYSIS] [HOLD]               │
│                                                                   │
└──────────────────────────────────────────────────────────────────┘

3.2 PM Actions on Change Impact Analysis

Approve Change

Creates a Change Request (manual follow-up still needed for business approval)
Effort impact is pre-filled from AI analysis
Logs: "PM_APPROVED_CHANGE_IMPACT"
Change request workflow proceeds normally (PM → BA → CTO review)

Request More Analysis

Re-runs Change Impact Agent with feedback
Example feedback: "Focus on third-party API integration risks" or "Get actual org-api team estimate"
AI re-analyzes and provides revised impact summary

Hold

Marks ChangeImpact as HOLD (doesn't create Change Request)
Reason: PM waits for more context or decides change is not viable
Change can be un-held later if decision changes

3.3 Sprint Burndown (Auto-Generated)

Where: "Sprint Burndown – AI-Updated" in Project Dashboard

┌──────────────────────────────────────────────────────────────────┐
│ Sprint 15 Burndown – AI-Updated (last updated 30 min ago)        │
├──────────────────────────────────────────────────────────────────┤
│                                                                   │
│ Task Status Breakdown:                                            │
│ • Completed: 6 / 40 (15%)                                        │
│ • In Progress: 8 / 40 (20%)                                      │
│ • To Do: 26 / 40 (65%)                                           │
│                                                                   │
│ Burndown Curve:                                                   │
│ Ideal: ─────────────  (40 → 0 over 10 days)                     │
│ Actual: ════════════  (40 → 26 after 3 days, on pace)          │
│                                                                   │
│ Blockers (3):                                                     │
│ 1. Org API Integration – waiting on vendor API key               │
│ 2. Onboarding UI – waiting on design review approval             │
│ 3. Requirement clarification – "See dashboard" ambiguity         │
│                                                                   │
│ At-Risk Tasks (2):                                                │
│ 1. Email Async Queue – estimate 3 hrs, no owner assigned        │
│ 2. Mobile Responsiveness – estimate 3 hrs, high complexity       │
│                                                                   │
│ Velocity Forecast:                                                │
│ Sprint avg velocity: 38 pts                                       │
│ This sprint (current): 36 pts (on pace to match)                │
│ Forecasted completion: Aug 5 (on schedule)                      │
│                                                                   │
│ 💡 Insights from AI:                                              │
│ • Sprint is on track; no rework detected                        │
│ • Two blockers identified 3 hours earlier than team would have  │
│ • One at-risk task could benefit from pair programming         │
│                                                                   │
└──────────────────────────────────────────────────────────────────┘

The burndown is read-only for PM; actual task completion is manual. AI just aggregates task status and highlights anomalies.

3.4 Risk & Change Request Queue

Where: "Risk & Requests – Pending" in Project Dashboard

┌──────────────────────────────────────────────────────────────────┐
│ Risk & Change Requests – Pending (5 items)                       │
├──────────────────────────────────────────────────────────────────┤
│                                                                   │
│ 🚨 HIGH RISK – Third-party org validation API integration       │
│    (flagged by Change Impact Agent)                              │
│    Mitigation: Schedule design review, verify API contract      │
│    [View change request]                                         │
│                                                                   │
│ ⚠ MEDIUM RISK – Email async queue is new pattern                │
│    (flagged by Change Impact Agent)                              │
│    Mitigation: Create spike to evaluate existing solutions       │
│    [View change request]                                         │
│                                                                   │
│ 📋 CHANGE REQUEST – Org connection step (new workflow)          │
│    Impact: +10 hours                                             │
│    Status: PROPOSED (AI-generated)                              │
│    [Review] [Approve] [Defer]                                    │
│                                                                   │
└──────────────────────────────────────────────────────────────────┘

4. DEVELOPER Surface

4.1 Task Assignment & Auto-Estimate Queue

Where: "My Tasks – AI-Suggested" in Developer Dashboard

┌──────────────────────────────────────────────────────────────────┐
│ My Tasks – AI-Suggested (2 new, estimated)                       │
├──────────────────────────────────────────────────────────────────┤
│                                                                   │
│ ✓ Implement Email Verification Async Queue                      │
│   Requirement: User Onboarding Flow (req_v12)                  │
│   Estimated: 3 hours (AI: 72% confidence)                       │
│   Priority: HIGH                                                  │
│   Status: NOT_STARTED                                            │
│                                                                   │
│   Suggested Subtasks:                                            │
│   • Set up queue infrastructure (1.5 hrs)                        │
│   • Write verification email handler (1 hr)                     │
│   • Add tests (0.5 hrs)                                          │
│                                                                   │
│   [ACCEPT ESTIMATE] [ADJUST ESTIMATE] [ACCEPT TASK]            │
│   ───────────────────────────────────────────────────────────── │
│                                                                   │
│ ✓ Integrate Org Validation API                                  │
│   Requirement: User Onboarding Flow (req_v12)                  │
│   Estimated: 4 hours (AI: 68% confidence)                       │
│   Priority: HIGH                                                  │
│   Status: BLOCKED (waiting on org_api team)                     │
│                                                                   │
│   Blockers:                                                       │
│   • Vendor API key not yet provided                              │
│   • Org validation API contract needs review                     │
│   Unblock: Notify PM for vendor follow-up                        │
│                                                                   │
│   [ACCEPT ESTIMATE] [ADJUST ESTIMATE]                           │
│   ───────────────────────────────────────────────────────────── │
│                                                                   │
└──────────────────────────────────────────────────────────────────┘

4.2 Developer Actions on AI-Suggested Tasks

Accept Estimate

Acknowledges AI-suggested effort; uses it in sprint planning
Logs: "DEVELOPER_ACCEPTED_ESTIMATE"
Developer can now ACCEPT TASK to start work

Adjust Estimate

Opens estimate editor
Developer provides their own estimate (if they think AI is off)
Logs: "DEVELOPER_ADJUSTED_ESTIMATE" with old and new values
AI learns from this feedback (used in future model training)

4.3 Daily Digest (Standup Digest Agent)

Where: Slack / Email, sent at 9 AM

Good morning dev_dev1! Here's your TeamFlow standup for Project Onboarding (Aug 2, 2026):

📋 YOUR TASKS (4)
✓ Completed: 1 task (Email verification module, 3 hrs spent vs. 3 hrs estimated)
⏳ In Progress: 2 tasks (Org API integration, Email async queue - no blockers)
⏱ To Do: 1 task (Mobile responsiveness testing, starts tomorrow)

🚨 YOUR BLOCKERS (1)
- Org API Integration blocked on vendor API key
  → PM has been notified; expected response Aug 2 EOD

💬 CLARIFICATIONS WAITING (1)
- Requirement "Onboarding Flow" needs BA clarification on "dashboard display"
  → Response expected by EOD; may affect UI task scope

📊 TEAM NEWS (1 item affecting you)
- QA team flagged a defect in LoginForm component (req_v5)
  → Your org validation task may depend on LoginForm refactoring
  → Check defect PRJ-456 for details

✏ UPDATE YOUR STATUS
   Reply in Slack or log time in TeamFlow

5. QA_ENGINEER Surface

5.1 Test Case Review Queue

Where: "Test Cases – AI-Generated" in QA Dashboard

┌──────────────────────────────────────────────────────────────────┐
│ Test Cases – AI-Generated (1 pending QA review)                  │
├──────────────────────────────────────────────────────────────────┤
│                                                                   │
│ ✓ User Onboarding Flow – Test Case Suite                        │
│   Requirement: req_v12 (APPROVED)                               │
│   Status: DRAFT (AI-generated)                                  │
│   Estimated Total Execution Time: 15 minutes                    │
│   ───────────────────────────────────────────────────────────── │
│                                                                   │
│   Test Cases (7 total):                                          │
│                                                                   │
│   ✓ Happy Path: New user completes onboarding                   │
│     GHERKIN: Given new user → When completes all steps → Then  │
│     sees dashboard & time < 3 min                               │
│     Automatable: Yes (Playwright)                               │
│     Est. Time: 2 min                                             │
│     [ACCEPT] [MODIFY] [DELETE]                                 │
│                                                                   │
│   ✓ Error Path: Invalid organization domain                     │
│     GHERKIN: Given invalid domain → When clicks Validate →      │
│     Then sees error message & remains on org step               │
│     Automatable: Yes (Playwright)                               │
│     Est. Time: 1 min                                             │
│     [ACCEPT] [MODIFY] [DELETE]                                 │
│                                                                   │
│   ✓ Accessibility: Mobile layout (iOS)                          │
│     GHERKIN: Given iPhone 12 → When navigates flow → Then       │
│     all inputs visible, touch targets ≥ 48x48 dp                │
│     Automatable: No (requires manual QA)                        │
│     Est. Time: 5 min                                             │
│     [ACCEPT] [MODIFY] [DELETE]                                 │
│                                                                   │
│   [ACCEPT ALL] [REJECT ALL] [CUSTOM SELECTION]                 │
│                                                                   │
└──────────────────────────────────────────────────────────────────┘

5.2 QA Actions on AI-Generated Test Cases

Accept

Moves test case to ACCEPTED status
Test case is now linked to RequirementVersion
QA can execute test case immediately (or schedule for later)
Logs: "QA_ACCEPTED_TEST_CASE"

Modify

Opens test case editor
QA can refine GHERKIN syntax, change automation flag, adjust estimated time
After edits: clicks ACCEPT
Logs: "QA_MODIFIED_TEST_CASE" with changes

Delete

Marks test case as deleted (not removed from audit log)
Reason: QA determines coverage is unnecessary or duplicate
Logs: "QA_DELETED_TEST_CASE" with reason

Custom Selection

QA can accept some test cases and reject others
Useful if AI over-generated or missed nuances
Example: accept automatable cases, manually create edge-case cases

5.3 Requirement Testability Review

Before BA submits a requirement for approval, QA can review it and flag testability concerns:

┌──────────────────────────────────────────────────────────────────┐
│ Testability Review – User Onboarding Flow (req_v12, DRAFT)       │
├──────────────────────────────────────────────────────────────────┤
│                                                                   │
│ ✅ TESTABLE: This requirement has clear acceptance criteria     │
│                                                                   │
│ Issues Found (0):                                                 │
│ (None – no vague acceptance criteria)                            │
│                                                                   │
│ Suggested Test Gaps:                                             │
│ • Network timeout scenario (what if email service is down?)     │
│ • Concurrent user scenario (multiple users onboarding at once)  │
│ • Retry logic for failed org validation                         │
│                                                                   │
│ Coverage Estimate: 7 automated + 3 manual test cases            │
│ Estimated QA Time: 30 minutes per release                       │
│                                                                   │
│ [FLAG TESTABILITY CONCERN] [APPROVE FOR SUBMISSION]             │
│                                                                   │
└──────────────────────────────────────────────────────────────────┘

6. CTO / MANAGEMENT Surface

6.1 Portfolio Health Dashboard

Where: Executive Dashboard, read-only

┌──────────────────────────────────────────────────────────────────┐
│ Portfolio Health – AI-Aggregated (as of 2026-08-02 4:00 PM)      │
├──────────────────────────────────────────────────────────────────┤
│                                                                   │
│ 📊 REQUIREMENTS CAPTURE                                           │
│ • Total requirements (all projects): 247                         │
│ • AI-assisted: 127 (51%)                                         │
│ • Manual entry: 120 (49%)                                        │
│ • Trend: AI adoption +15% this quarter                          │
│                                                                   │
│ 📝 AI EXTRACTION QUALITY                                         │
│ • Avg confidence score: 0.83 (target: > 0.80) ✓                │
│ • BA acceptance rate: 84% (target: > 80%) ✓                    │
│ • Rejection rate: 12% (target: < 20%) ✓                         │
│ • Rework rate: 4% (BA modifies AI output)                       │
│                                                                   │
│ 🚨 RISK & CHANGE REQUESTS                                        │
│ • Open change requests: 8                                        │
│ • High-risk changes: 2 (third-party API, new email pattern)   │
│ • Avg approval cycle time: 2.5 days                              │
│ • Blocked due to clarifications: 1 (waiting on BA response)     │
│                                                                   │
│ ⏱ DELIVERY VARIANCE                                              │
│ • Requirements on-time: 91% (target: > 90%) ✓                   │
│ • Tasks on-time: 87% (target: > 90%) ⚠                         │
│ • QA cycle time: 3.2 days avg                                    │
│ • Rework rate: 8% (AI-assisted reqs: 6%, manual: 10%)          │
│                                                                   │
│ 💰 AI ROI                                                         │
│ • Cost/month: $2,400 (AI agent compute)                         │
│ • BA time saved: ~40 hours/month (manual transcription)         │
│ • BA hourly rate: $100/hr → $4,000 value/month                 │
│ • ROI: +67% (savings exceed costs)                              │
│                                                                   │
│ 📉 TRENDS                                                         │
│ • Rework is down 12% since AI adoption (July vs. June)          │
│ • Clarification response time: down 2 days (async Q&A)          │
│ • First-pass requirement quality: up 8% (confidence correlation) │
│                                                                   │
└──────────────────────────────────────────────────────────────────┘

6.2 Agent Performance Dashboard

Where: Engineering / AI Team Only

┌──────────────────────────────────────────────────────────────────┐
│ Agent Performance – Engineering View (last 7 days)               │
├──────────────────────────────────────────────────────────────────┤
│                                                                   │
│ Requirement Extraction Agent                                     │
│ • Calls: 42  Success: 41  Errors: 1 (timeout)                  │
│ • Avg confidence: 0.85  Acceptance rate: 86%                   │
│ • Avg latency: 8.3 sec  Cost/call: $0.045                      │
│ • Top error type: Low confidence (<0.70), 5 instances          │
│                                                                   │
│ Change Impact Agent                                              │
│ • Calls: 8  Success: 8  Errors: 0                              │
│ • Accuracy vs. manual estimates: 78% within ±20%                │
│ • Avg latency: 12.1 sec  Cost/call: $0.089                     │
│                                                                   │
│ Test Case Generator Agent                                        │
│ • Calls: 12  Success: 11  Errors: 1 (invalid GHERKIN)         │
│ • QA acceptance rate: 92%  Coverage adequacy: 89%              │
│ • Avg latency: 15.2 sec  Cost/call: $0.125                     │
│                                                                   │
│ Token Usage (weekly):                                            │
│ • Input tokens: 1.2M  Output tokens: 892K                      │
│ • Model: Claude Opus 5 (100%), cost: $48                       │
│                                                                   │
│ [View Logs] [Tune Thresholds] [Fine-tune Model?]               │
│                                                                   │
└──────────────────────────────────────────────────────────────────┘

7. Notification & Alert Strategy

7.1 Role-Based Notifications

Role	Event	Channel	Urgency
BA	AI proposal ready for review	In-app + Slack	Normal
BA	Clarification auto-flagged	In-app + Email	Normal
PM	Change impact analysis ready	In-app + Slack	Normal
PM	High-risk change detected	In-app + Slack + Email	High
Dev	Task assigned (AI-suggested)	In-app + Email	Normal
Dev	Task blocked (AI-detected)	In-app + Slack	High
QA	Test case suite ready for review	In-app + Email	Normal
QA	Testability concern flagged	In-app + Slack	Normal
CTO	Portfolio metrics updated (daily)	Email digest	Low
CTO	Agent error rate spike	Slack + PagerDuty	Critical

7.2 Notification Frequency

Real-time: Blockers, high-risk changes, critical errors
Hourly digest: Task status updates, minor AI completions
Daily digest: Standup summaries, portfolio metrics, agent health
Weekly digest: Trend analysis, AI ROI, team velocity

8. Feedback Loop (AI Learns from Humans)

Every human action on AI output is logged and used to improve future extractions:

BA rejects a requirement → AI learns what caused rejection; feedback is used to adjust confidence score on similar future extractions
BA modifies an AI extraction → Diff is logged; AI can learn from the delta
Dev adjusts an AI estimate → New data point added to effort model
QA modifies test case → AI learns about real-world testability gaps

This data is aggregated monthly and used to:

Retrain or fine-tune agent models
Adjust confidence thresholds
Identify systematic gaps (e.g., "AI always underestimates mobile work")

9. Audit Trail for All AI Actions

Every AI-driven action is logged with:

timestamp, userId (if human review), agentId (if AI)
action_type (PROPOSED, ACCEPTED, REJECTED, MODIFIED)
confidence_score, model_id, tokens_used
reason (if rejected/modified)
output_artifact_id (link to what was created/modified)

Example audit log entry:

{
  "id": "audit_12345",
  "timestamp": "2026-08-02T14:12:00Z",
  "eventType": "AI_EXTRACTION_CREATED",
  "agentType": "REQUIREMENT_EXTRACTION",
  "extractionId": "extraction_abc123",
  "transcriptId": "transcript_abc123",
  "confidence": 0.87,
  "modelId": "claude-opus-5",
  "inputTokens": 4250,
  "outputTokens": 892,
  "status": "PROPOSED"
}

Followed by:

{
  "id": "audit_12346",
  "timestamp": "2026-08-02T16:30:00Z",
  "eventType": "BA_ACCEPTED_EXTRACTION",
  "extractionId": "extraction_abc123",
  "userId": "user_ba1",
  "changesApplied": ["acceptanceCriteria"],
  "resultingRequirementId": "req_v12",
  "resultingStatus": "DRAFT"
}

10. Configuration by Organization

Different organizations may want:

Different confidence thresholds
Different notification channels (Slack vs. email vs. SMS)
Different approval workflows (some may auto-approve low-risk changes)
Different role-based surfaces (e.g., a startup might not have QA; a large enterprise might have multiple CTO dashboards)

Recommendation: Expose a configuration panel (for SYSTEM_ADMIN) to customize all of the above.

TeamFlow AI – Role-Based Automation Surfaces

1. Overview

2. BUSINESS_ANALYST Surface

2.1 Requirement Review Queue

2.2 BA Actions on AI Proposals

Accept

Modify

Reject

More Context

2.3 Clarification Response Workflow

2.4 Acceptance Criteria Suggestions

3. PROJECT_MANAGER Surface

3.1 Change Impact Dashboard

3.2 PM Actions on Change Impact Analysis

Approve Change

Request More Analysis

Hold

3.3 Sprint Burndown (Auto-Generated)

3.4 Risk & Change Request Queue

4. DEVELOPER Surface

4.1 Task Assignment & Auto-Estimate Queue

4.2 Developer Actions on AI-Suggested Tasks

Accept Estimate

Adjust Estimate

4.3 Daily Digest (Standup Digest Agent)

5. QA_ENGINEER Surface

5.1 Test Case Review Queue

5.2 QA Actions on AI-Generated Test Cases

Accept

Modify

Delete

Custom Selection

5.3 Requirement Testability Review

6. CTO / MANAGEMENT Surface

6.1 Portfolio Health Dashboard

6.2 Agent Performance Dashboard

7. Notification & Alert Strategy

7.1 Role-Based Notifications

7.2 Notification Frequency

8. Feedback Loop (AI Learns from Humans)

9. Audit Trail for All AI Actions

10. Configuration by Organization