TeamFlow AI Agents – Detailed Specifications

1. Overview

Each agent is a focused LLM service that takes structured input and produces JSON output. Agents run independently; orchestration happens via queues and event logs, not direct agent-to-agent calls.

2. Transcription Agent

2.1 Purpose

Convert raw audio + diarization metadata into a searchable, timestamped transcript with speaker attribution.

2.2 Input

• recordingId: Reference to stored audio
• language: Detected or configured language code (e.g., "en-US")
• participants: List of user IDs present (optional; used for accurate speaker identification)

2.3 Output: Transcript

{
  "id": "transcript_...",
  "recordingId": "recording_...",
  "rawText": "full transcript text",
  "speakerSegments": [
    {
      "speaker": "Speaker_1",
      "identifiedAs": "user_ba1",
      "timestamp": "00:00:00",
      "duration": "00:05:30",
      "text": "segment text",
      "confidence": 0.95
    }
  ],
  "overallConfidence": 0.92,
  "language": "en-US",
  "wordCount": 4250,
  "createdAt": "ISO8601",
  "status": "COMPLETE"
}

2.4 Success Criteria

• Speaker diarization confidence ≥ 0.80 (else flagged in transcript)
• Word error rate (WER) ≤ 0.05 for technical terms (requires domain lexicon for each project)
• Timestamp accuracy ± 0.5 seconds
• Detects and marks inaudible segments (silence, crosstalk, low SNR)

2.5 Failure Modes

• Low SNR (< 20 dB): Mark segments as low-confidence; do not skip
• Crosstalk: Transcribe best-effort; flag segment with confidence < 0.60
• Non-English languages: Detect and process accordingly; mark if multi-language
• Speaker identification ambiguity: Use audio fingerprinting + historical voice samples; fall back to "Speaker_1", "Speaker_2"

2.6 Configuration

• Max audio duration: 240 minutes (scalable)
• Supported languages: English, Spanish, French, German, Japanese, Mandarin (configurable)
• Re-run threshold: Transcript is immutable once COMPLETE; corrections require manual annotation

3. Requirement Extraction Agent

3.1 Purpose

Draft a structured requirement from a transcript, inferring business objective, actors, workflows, rules, scope, and acceptance criteria.

3.2 Input

{
  "transcriptId": "transcript_...",
  "projectContext": {
    "projectId": "proj_xyz",
    "existingRequirements": [
      {
        "id": "req_123",
        "title": "...",
        "status": "APPROVED",
        "version": 3
      }
    ],
    "domainGlossary": {
      "term": "definition"
    }
  }
}

3.3 Output: AIExtraction (type=REQUIREMENT_DRAFT)

{
  "id": "extraction_...",
  "transcriptId": "transcript_...",
  "extractionType": "REQUIREMENT_DRAFT",
  "payload": {
    "title": "User Onboarding Flow – Profile Setup Module",
    "businessObjective": "Reduce time-to-first-value for new users from 8 min to < 3 min",
    "actors": [
      {
        "role": "New User",
        "description": "User signing up for the first time"
      },
      {
        "role": "Customer Success Manager",
        "description": "Internal team supporting onboarding"
      }
    ],
    "mainWorkflow": [
      "1. User signs up with email",
      "2. Email verification link sent",
      "3. User creates profile (name, picture optional)",
      "4. User connects organization (by domain)",
      "5. User sees dashboard"
    ],
    "alternativeWorkflows": [
      {
        "trigger": "User already has organization account",
        "steps": ["Suggest join existing org", "Require invite token", "Skip profile setup"]
      }
    ],
    "businessRules": [
      "Profile picture is optional",
      "Org connection must validate against domain whitelist",
      "CSM can trigger guided tour on first login",
      "Duplicate emails are rejected"
    ],
    "acceptanceCriteria": [
      "Flow completes in < 3 min for 90% of users on standard broadband",
      "Mobile and desktop supported",
      "Email verification uses async queue (max 2-min delivery)"
    ],
    "outOfScope": [
      "SSO integration (scheduled for Q4)",
      "Bulk user import (separate Epic)"
    ],
    "assumptions": [
      "Email service is available (SLA 99%)",
      "Org database is responsive (< 100ms)"
    ],
    "dependencies": [
      "Email verification service (existing)",
      "Org validation API (new, medium complexity)"
    ],
    "nonFunctionalRequirements": [
      "Performance: Page load < 2 sec on 3G",
      "Accessibility: WCAG 2.1 AA",
      "Mobile: iOS 12+, Android 8+"
    ]
  },
  "confidence": 0.87,
  "sourceSegments": [
    {
      "speaker": "user_ba1",
      "timestamp": "00:02:30",
      "text": "...",
      "relevance": "core workflow"
    }
  ],
  "status": "PROPOSED",
  "createdAt": "ISO8601"
}

3.4 Success Criteria

• Confidence ≥ 0.75 for main workflow extraction
• Actors and roles clearly identified
• At least 3 acceptance criteria extracted
• Out-of-scope items identified and listed
• No hallucination of features not mentioned in transcript
• Alignment with project domain glossary (e.g., uses correct terminology)

3.5 Failure Modes

• Insufficient context: Transcript does not contain enough detail about requirements; confidence < 0.60 → routed to human re-work queue
• Conflicting statements: Transcript contains contradictory information; agent flags and includes both interpretations with confidence scores
• Underspecified acceptance criteria: Agent generates clarification questions instead of guessing
• Domain mismatch: Extracted requirement uses terminology inconsistent with project glossary; agent flags with suggestion

3.6 Configuration

• Confidence threshold for acceptance: 0.75 (configurable per organization)
• Max required fields to extract: 12 (title, objective, actors, workflows, rules, criteria, scope, assumptions, dependencies, NFRs)
• Domain glossary: auto-updated from approved requirement versions

4. Ambiguity Detector Agent

4.1 Purpose

Identify vague or underspecified terms in a requirement transcript or draft, generate clarifying questions, and auto-create Clarification records.

4.2 Input

{
  "transcriptId": "transcript_...",
  "draftRequirementId": "extraction_...",
  "projectContext": {
    "historicalClarifications": [
      {
        "term": "fast",
        "resolution": "< 2 sec response time"
      }
    ]
  }
}

4.3 Output: AIExtraction (type=CLARIFICATION)

{
  "id": "extraction_clarif_...",
  "transcriptId": "transcript_...",
  "extractionType": "CLARIFICATION",
  "payload": {
    "flaggedTerms": [
      {
        "term": "< 3 minutes",
        "context": "Flow completion time",
        "ambiguity": "Network speed affects perceived time; unclear if measured user action time or wall-clock time",
        "suggestedClarifications": [
          "Is this measured on standard broadband (5 Mbps+)?",
          "Does this include email delivery time or just user actions?",
          "Is 90% of users a hard SLA or a goal?"
        ],
        "historicalContext": "Similar term 'fast' was resolved to '< 2 sec response time' in req_456",
        "confidence": 0.92
      },
      {
        "term": "See dashboard",
        "context": "Final step in main workflow",
        "ambiguity": "Multiple dashboards exist: admin dashboard, user dashboard, analytics dashboard",
        "suggestedClarifications": [
          "Which dashboard should a new user see? (Admin/User/Analytics?)",
          "Should the dashboard be pre-populated with sample data?"
        ],
        "confidence": 0.88
      }
    ],
    "questionSummary": [
      "What is the baseline network speed for the < 3 minute target?",
      "Which dashboard should new users see first?"
    ]
  },
  "confidence": 0.90,
  "status": "PROPOSED",
  "createdAt": "ISO8601"
}

4.4 Success Criteria

• Identifies vague terms with > 80% precision (low false-positive rate)
• Generates 2–5 clarifying questions per vague term
• Links to historical clarifications where relevant
• Confidence score reflects term ambiguity (higher = more ambiguous)

4.5 Failure Modes

• Over-flagging: Agent flags every relative term (small, large, fast); mitigated by weighting against project glossary
• Context misses: Agent doesn't understand project domain and flags terms that are already defined; mitigated by providing glossary
• Missed ambiguity: Requirement is genuinely vague but agent doesn't flag; caught downstream in BA review

4.6 Configuration

• Vague-term lexicon (configurable per organization)
• Historical clarification lookback: 1 year
• Max flagged terms per requirement: 10
• Clarification auto-created in status=OPEN; BA responds with clarification text, and response is linked to requirement

5. UX Gap-Filler Agent

5.1 Purpose

Suggest UI/UX design patterns, wireframes, and component recommendations when a requirement lacks visual or interaction details.

5.2 Input

{
  "requirementId": "extraction_...",
  "transcript": "...",
  "projectContext": {
    "existingComponents": [
      {
        "name": "LoginForm",
        "description": "Email + password form with validation"
      },
      {
        "name": "ProgressBar",
        "description": "Multi-step progress indicator"
      }
    ],
    "designSystem": "Material Design 3 | Tailwind",
    "accessibility": "WCAG 2.1 AA"
  }
}

5.3 Output: Artifact (attached to AIExtraction)

{
  "type": "ux_suggestion",
  "payload": {
    "wireframeDescription": "4-step mobile-first onboarding:
1. Email/Password (reuse LoginForm component)
2. Profile (name + optional picture)
3. Org Connection (domain input + validation feedback)
4. Welcome (summary + CTA to dashboard)
Desktop variant: same steps, card layout with progress bar on left side.",
    "suggestedComponents": [
      {
        "name": "LoginForm",
        "purpose": "Email + password entry in step 1",
        "reuseNote": "Existing component; no changes needed"
      },
      {
        "name": "ProgressBar",
        "purpose": "4-step progress indicator",
        "reuseNote": "Existing; configure with 4 steps"
      },
      {
        "name": "ImageUpload",
        "purpose": "Optional profile picture",
        "reuseNote": "New; similar to component used in settings flow"
      }
    ],
    "accessibilityNotes": [
      "Ensure keyboard navigation between steps",
      "Form labels must be associated with inputs (aria-label)",
      "Error messages must be associated with invalid fields (aria-describedby)",
      "Mobile viewport: ensure touch targets ≥ 48x48 dp"
    ],
    "mobileConsiderations": [
      "Stack all inputs vertically",
      "Use native date picker for date inputs (if any)",
      "Use native email keyboard on mobile"
    ],
    "designTokens": [
      "Primary color: [reference design system]",
      "Spacing: 16px baseline (4px grid)",
      "Font: [system font from design system]"
    ]
  },
  "confidence": 0.80,
  "createdAt": "ISO8601"
}

5.4 Success Criteria

• Suggests components that exist in design system 80% of the time
• Accessibility recommendations are WCAG 2.1 AA compliant
• Wireframe description is clear enough for designer to sketch without AI assistance
• No over-design (e.g., suggesting a custom component when a standard one exists)

5.5 Failure Modes

• Missing design context: Agent doesn't know project's design system; mitigated by providing design token reference
• Over-suggestion: Agent recommends new component when reuse is better; mitigated by scoring existing component fit first
• Accessibility gaps: Agent misses specific WCAG criterion; mitigated by explicit WCAG 2.1 AA validation in review

5.6 Configuration

• Design system reference (Material 3, Tailwind, Figma library URL)
• Accessibility standard (WCAG 2.1 AA by default)
• Target platforms (iOS, Android, Web; can be mixed)
• Existing components index (auto-fetched from Figma or component library)

6. Change Impact Agent

6.1 Purpose

Compare a new requirement extraction to an existing approved requirement, identify differences, estimate effort impact, and flag risks.

6.2 Input

{
  "oldVersionId": "req_v12",
  "newExtractionId": "extraction_...",
  "projectContext": {
    "estimateHistory": [
      {
        "requirementId": "req_456",
        "originalEstimate": "8 hours",
        "actualTime": "10 hours",
        "notes": "Underestimated component reuse complexity"
      }
    ]
  }
}

6.3 Output: ChangeImpact (and AIExtraction for review)

{
  "id": "impact_...",
  "oldVersionId": "req_v12",
  "newExtractionId": "extraction_...",
  "projectId": "proj_xyz",
  "diffSummary": {
    "fieldsChanged": [
      "acceptanceCriteria (added 1, removed 0)",
      "actors (no change)",
      "businessRules (added 2: duplicate email rejection, CSM tour trigger)"
    ],
    "textDiff": {
      "mainWorkflow": {
        "added": ["4. User connects organization (by domain)"],
        "removed": [],
        "modified": []
      }
    }
  },
  "impactAnalysis": {
    "scopeChange": "MEDIUM (new workflow step for org connection)",
    "affectedModules": [
      {
        "module": "user-auth",
        "reason": "Email verification changes (async queue)",
        "effortEstimate": "+2 hours",
        "risk": "LOW"
      },
      {
        "module": "org-api",
        "reason": "New org validation integration",
        "effortEstimate": "+5 hours",
        "risk": "MEDIUM (third-party API integration)"
      },
      {
        "module": "ui-onboarding",
        "reason": "New steps, mobile responsiveness",
        "effortEstimate": "+3 hours",
        "risk": "LOW"
      }
    ],
    "totalEffortDelta": "+10 hours",
    "effortConfidence": 0.72,
    "riskLevel": "MEDIUM",
    "riskFactors": [
      "Third-party org validation API is new integration",
      "Mobile responsiveness adds complexity",
      "Email async queue is new pattern in codebase"
    ]
  },
  "historicalContext": {
    "similarRequirements": [
      {
        "requirementId": "req_456",
        "similarity": 0.68,
        "originalEstimate": "12 hours",
        "actualTime": "14 hours",
        "deltaNote": "Scope grew 15% after stakeholder feedback"
      }
    ]
  },
  "recommendedActions": [
    "Schedule design review before implementation",
    "Verify org validation API contract with vendor",
    "Create spike for email async queue pattern"
  ],
  "status": "PROPOSED",
  "createdAt": "ISO8601"
}

6.4 Success Criteria

• Identifies all changed fields with > 95% accuracy (compared to manual diff)
• Effort estimate is within ±20% of actual (validated against historical data)
• Risk level matches CTO/PM assessment in post-mortems 75% of the time
• Identifies affected modules with no false positives (precision > 0.90)

6.5 Failure Modes

• Underestimation of scope: New requirement seems small but actually requires significant refactoring; mitigated by providing historical estimate variance data
• Module misidentification: Agent flags module as affected when it's not; caught in PM/CTO review
• Risk blindness: Agent misses a critical risk (e.g., third-party API sunset); mitigated by allowing CTO to override and provide reason

6.6 Configuration

• Historical estimate lookback: 2 years
• Effort estimate variance tolerance: ±20%
• Risk assessment model (can be customized per organization)
• Similarity threshold for historical lookups: 0.60

7. Task Breakdown Agent

7.1 Purpose

Only runs on APPROVED requirements. Suggests a task list with estimates and subtasks.

7.2 Input

{
  "requirementVersionId": "req_v12",
  "projectContext": {
    "team": [
      {
        "userId": "user_dev1",
        "role": "DEVELOPER",
        "skills": ["React", "Node.js"],
        "currentWorkload": 32
      }
    ]
  }
}

7.3 Output: Task (DRAFT status) + AIExtraction

{
  "id": "task_...",
  "requirementVersionId": "req_v12",
  "projectId": "proj_xyz",
  "title": "User Onboarding Flow – Implementation",
  "status": "DRAFT",
  "priority": "HIGH",
  "estimatedHours": 10,
  "estimateConfidence": 0.72,
  "suggestedOwner": "user_dev1",
  "subtasks": [
    {
      "title": "Implement email verification async queue",
      "owner": "user_dev1",
      "estimatedHours": 3,
      "dependencies": []
    },
    {
      "title": "Integrate org validation API",
      "owner": "user_dev1",
      "estimatedHours": 4,
      "dependencies": ["email verification"]
    },
    {
      "title": "Build onboarding UI (desktop + mobile)",
      "owner": null,
      "estimatedHours": 3,
      "dependencies": []
    }
  ],
  "aiGeneratedNote": "Estimates based on historical effort data and similar requirements. Subtask 'UI' requires designer; other subtasks are dev-only. Total assumes 1 developer; parallel work may reduce actual time.",
  "createdAt": "ISO8601"
}

7.4 Success Criteria

• Subtasks are independent or dependencies are clearly marked
• Estimated hours are within ±25% of actual (validated post-release)
• Suggested owner matches PM assignment 70% of the time
• No subtask is < 1 hour or > 16 hours (breaks scope too small or too large)

7.5 Failure Modes

• Overly granular: Subtasks are too small; mitigated by enforcing min 1-hour estimate
• Missing subtasks: Agent misses an obvious task; caught in developer estimation review
• Poor owner suggestion: Suggested owner doesn't have the skills; mitigated by consulting skills matrix

7.6 Configuration

• Historical estimate database (queries last 2 years of similar work)
• Skills matrix (auto-fetched from team profiles)
• Max subtasks per task: 10
• Effort estimate granularity: 0.5 hours

8. Test Case Generator Agent

8.1 Purpose

Only runs on APPROVED requirements. Generates test cases in GHERKIN format (Given-When-Then).

8.2 Input

{
  "requirementVersionId": "req_v12",
  "acceptanceCriteria": [
    "Flow completes in < 3 min for 90% of users on standard broadband",
    "Mobile and desktop supported"
  ]
}

8.3 Output: TestCase (DRAFT status) + AIExtraction

{
  "id": "test_...",
  "requirementVersionId": "req_v12",
  "projectId": "proj_xyz",
  "title": "User Onboarding Flow – Test Cases",
  "status": "DRAFT",
  "testCases": [
    {
      "scenario": "New user completes profile setup",
      "gherkin": "Given a new user on the signup page
When the user enters email, password, profile name
And clicks 'Next'
And enters organization domain
And clicks 'Connect'
Then the user sees the dashboard
And the total time elapsed is < 3 minutes",
      "type": "HAPPY_PATH",
      "automatable": true,
      "estimatedExecutionTime": "2 minutes"
    },
    {
      "scenario": "User provides invalid organization domain",
      "gherkin": "Given a user on the org connection step
When the user enters 'invalid-domain.com'
And clicks 'Validate'
Then the user sees error message 'Domain not recognized'
And the user remains on the org connection step",
      "type": "ERROR_PATH",
      "automatable": true,
      "estimatedExecutionTime": "1 minute"
    },
    {
      "scenario": "Flow renders correctly on mobile (iOS)",
      "gherkin": "Given a new user on iPhone 12 (Safari)
When the user navigates through onboarding steps
Then all form inputs are visible without horizontal scroll
And touch targets are ≥ 48x48 dp",
      "type": "ACCESSIBILITY",
      "automatable": false,
      "estimatedExecutionTime": "5 minutes"
    }
  ],
  "estimatedTotalExecutionTime": "15 minutes",
  "aiGeneratedNote": "Test cases derived from acceptance criteria and main workflow. Non-automatable cases (accessibility, UI layout) require manual QA. Consider adding performance test for < 3 min SLA (requires load testing infrastructure).",
  "createdAt": "ISO8601"
}

8.4 Success Criteria

• At least 1 happy-path test case per workflow step
• At least 1 error-path test case per validation rule
• GHERKIN syntax is valid and executable (can be parsed by test framework)
• Automatable vs. manual classification matches QA's assessment 85% of the time

8.5 Failure Modes

• Over-automation: Agent suggests automating a test that is too brittle; caught in QA review
• Missed scenarios: Agent misses edge cases (e.g., network timeout, concurrent users); caught in QA review
• Unmaintainable GHERKIN: Syntax is invalid or too verbose; caught by parser

8.6 Configuration

• GHERKIN style guide (Given-When-Then format, max 10 steps per scenario)
• Automatable vs. manual heuristics (e.g., performance tests are manual unless load-testing framework exists)
• Test framework: Cucumber, Playwright, Selenium (configurable)

9. Standup Digest Agent

9.1 Purpose

Daily digest of task activity, blockers, at-risk items, and key decisions. Role-based output (developer gets their tasks, PM gets portfolio view).

9.2 Input

{
  "role": "PROJECT_MANAGER",
  "projectId": "proj_xyz",
  "lookbackHours": 24
}

9.3 Output: Notification (sent to Slack/email)

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

📊 SPRINT STATUS
- Sprint 15: 6 tasks complete, 8 in progress, 2 blocked
- Burndown: On pace (34 of 40 tasks done)
- Velocity: 38 pts avg (this sprint: 36 pts so far)

🚨 BLOCKERS (attention needed)
- Task "Org API Integration" blocked on vendor API key (assigned to @dev1)
  → Contact vendor support; blocking @dev1 and 1 dependent task
- Requirement "User Onboarding" needs clarification on org domain validation logic
  → BA to respond by EOD

🎯 RELEASES PENDING
- Release v1.2.0: 4 of 5 requirements delivered, 1 awaiting QA approval
  → Expected release: Aug 5

💡 KEY DECISIONS (last 24h)
- Approved change request: Add email async queue (impact: +2 hrs, low risk)
- Rejected change request: SSO integration (deferred to Q4, per CTO)

Questions? Reply in Slack or open requirement in TeamFlow.

9.4 Success Criteria

• All blockers are correctly identified (recall > 0.95)
• Summary is concise (< 200 words)
• Role-specific information is relevant to recipient's role
• Sent at consistent time each day (configurable, default 9 AM local time)

9.5 Configuration

• Send time: 9 AM (configurable per organization)
• Role-based summaries (PM, BA, Dev, QA have different summaries)
• Lookback window: 24 hours (or custom)
• Notification channel: Slack, email, or both

10. Orchestration and Error Handling

10.1 Agent Invocation Sequence

1. Recording is marked COMPLETE → Trigger Transcription Agent
2. Transcript is COMPLETE → Trigger Requirement Extraction Agent + Ambiguity Detector Agent (parallel)
3. Requirement Extraction is PROPOSED → Trigger UX Gap-Filler Agent (optional, if design is incomplete)
4. Requirement Extraction is ACCEPTED by BA → Trigger Change Impact Agent (if old version exists)
5. Requirement is APPROVED → Trigger Task Breakdown Agent + Test Case Generator Agent (parallel)
6. Sprint/day ends → Trigger Standup Digest Agent (scheduled, not event-based)

10.2 Error Handling

• Timeout: If an agent takes > 5 minutes, cancel and log error; notify admin
• Low confidence: If confidence < threshold, route to human review queue (see §5 in architecture doc)
• API failure (e.g., transcription service down): Retry up to 3 times with exponential backoff; if still failing, alert admin and mark Recording as FAILED
• Invalid output: If agent output doesn't match expected JSON schema, log error and route to human re-work queue

10.3 Cost Tracking

Every agent call logs:

• modelId (e.g., "claude-opus-5")
• inputTokens, outputTokens
• duration (wall-clock time)
• status (SUCCESS, TIMEOUT, INVALID_OUTPUT, API_ERROR)

Cost is aggregated per project, per agent, and per month for transparency.

11. Monitoring and Observability

11.1 Metrics

• Extraction accuracy: % of AI extractions that are accepted by human reviewer (target: > 80%)
• Rejection rate: % of AI outputs rejected, by agent type (target: < 20% per agent)
• Confidence calibration: % of high-confidence extractions (> 0.80) that are accepted (target: > 85%)
• End-to-end time: Time from recording completion to BA review (target: < 15 min)
• Cost per requirement: Average $ spent on AI agents per requirement (for ROI analysis)

11.2 Logging

All AI events are logged with:

• timestamp, eventType (PROPOSED, ACCEPTED, REJECTED)
• agent_type, model_id, tokens_used
• confidence_score, user_id (for human review actions)
• reason (if rejected)

Logs are retained for 2 years for audit and training purposes.

11.3 Dashboards

• CTO: Agent health, error rates, cost per project
• PM: Extraction accuracy, time-to-review, blocked items
• BA: Clarification rates, extraction quality, rejection reasons

12. Future Enhancements

• Multi-language support: Expand beyond English; maintain glossaries per language
• Domain-specific fine-tuning: Custom models for specialized domains (healthcare, fintech)
• Feedback loop: Learn from rejected extractions to improve accuracy
• Agent composition: Combine agents to produce richer output (e.g., requirement + test cases in one pass)
• Real-time meeting co-pilot: Live transcript display + instant extraction suggestions during call