Circulos AI

Support AI Skill

Internal Collaboration Knowledge Sharing

Enable agents to collaborate on complex tickets, share expertise, and build collective team knowledge. Use when managing peer-to-peer support, creating expert networks, facilitating knowledge sharing, setting up @mention workflows, or building a culture of continuous learning. Triggers on phrases like "internal collaboration", "peer support", "knowledge sharing", "agent collaboration", "expert network", "team knowledge", "@mention agent", "internal notes", "peer review", "collective intelligence".

Internal Collaboration & Knowledge Sharing

Enable seamless collaboration among support agents to resolve complex issues faster and build organizational knowledge.

Workflow

  1. Agent encounters complex or unfamiliar issue beyond their expertise.
  2. Agent adds internal note with @mention to subject matter expert (SME).
  3. SME receives notification via Slack, email, or in-app alert.
  4. SME reviews ticket context and adds expert guidance in internal notes.
  5. Original agent uses guidance to resolve customer issue.
  6. System prompts agent to capture solution as knowledge base article.
  7. Team lead reviews and publishes article to internal/external KB.
  8. Collaboration logged for performance tracking and recognition.
  9. Monthly knowledge sharing sessions surface top solutions.

Collaboration Models by Team Size

COLLABORATION STRUCTURE MATRIX
===============================

Tier 1 — Small Teams (5–15 agents):
  Model: Flat collaboration, everyone-to-everyone
  - No formal hierarchy; agents reach out directly
  - Slack/Teams channel for quick questions
  - Weekly "lessons learned" standup (15 min)
  - Shared internal wiki for solutions
  - Average response time to @mention: < 30 min
  - Collaboration rate: ~25% of tickets involve peer consult

Tier 2 — Mid-Size Teams (16–75 agents):
  Model: Hub-and-spoke with designated SMEs
  - 3–5 SMEs per product area
  - Tiered escalation: agent → SME → team lead → engineering
  - Dedicated #support-experts Slack channel
  - Bi-weekly knowledge sharing sessions (30 min)
  - Internal KB with structured categories
  - Average response time to @mention: < 45 min
  - Collaboration rate: ~35% of tickets involve peer consult

Tier 3 — Large/Enterprise Teams (76–500+ agents):
  Model: Formal collaboration network with governance
  - SME council: 10–20 recognized experts across products
  - Tiered support: L1 → L2 → L3 (engineering) → L4 (vendor)
  - Dedicated escalation queues with SLAs (L2: < 4 hrs, L3: < 24 hrs)
  - Monthly knowledge conference (60 min, recorded)
  - Internal KB platform with peer review workflow
  - Expert recognition program (badges, rewards, career pathing)
  - Average response time to @mention: < 2 hrs
  - Collaboration rate: ~40% of tickets involve peer consult

Tier 4 — Global/Multi-Region Teams (500+ agents):
  All Tier 3 PLUS:
  - Follow-the-sun handoff protocols
  - Time-zone-aware escalation routing
  - Translated knowledge sharing (key articles in top 5 languages)
  - Regional knowledge champions
  - Cross-region virtual collaboration sessions
  - Async collaboration emphasis (documentation-first culture)

@Mention and Expert Notification System

@MENTION WORKFLOW AND SLA
==========================

Trigger: Agent types @username or @team-name in internal ticket notes

Step 1 — Notification Delivery:
  → In-app notification: Badge on agent's name in help desk
  → Slack/Teams DM: "@You — [Agent Name] needs help on ticket #12345"
  → Email (backup): For agents not in Slack/Teams, sent within 1 min
  → SMS escalation: If not acknowledged within 30 min and ticket is P1

Step 2 — Expert Response Expectations:
  Priority    | Expected Response Time | Escalation Threshold
  ------------|------------------------|---------------------
  P0 (Critical) | 15 minutes             | 20 min → manager page
  P1 (Urgent)   | 30 minutes             | 45 min → manager alert
  P2 (Normal)   | 2 hours                | 4 hrs → queue backup
  P3 (Low)      | 4 hours                | 8 hrs → next business day

Step 3 — Response Tracking:
  → Expert acknowledges receipt (clicks "I'm on it")
  → Expert adds internal note with solution or questions
  → Expert marks as "resolved" when satisfied
  → Original agent confirms solution works
  → System records: time-to-response, resolution quality

Step 4 — Quality Assurance:
  → Monthly review of @mention response times per expert
  → Recognition for fastest/most helpful responders
  → Coaching for experts with consistently slow responses
  → Identify recurring @mention topics → create KB articles

BEST PRACTICES FOR @MENTIONS:
  - Include context: Don't just say "help" — describe what you've tried
  - Reference KB: Show you searched before asking ("I checked article X but...")
  - Be specific: What exactly do you need? (Solution, explanation, access?)
  - Acknowledge: Thank the expert when they respond
  - Close the loop: Mark @mention as resolved when done

Internal Knowledge Capture from Collaborations

KNOWLEDGE CAPTURE PIPELINE
===========================

Goal: Convert every collaboration into reusable organizational knowledge

Step 1 — Auto-Detection:
  Monitor tickets with internal collaboration notes
  Trigger capture prompt when:
    - Ticket had @mention activity AND was resolved successfully
    - Customer gave CSAT ≥ 4.0
    - Issue type has < 3 KB articles covering it

Step 2 — Article Generation:
  AI analyzes collaboration thread and generates draft article:
    - Title: Extracted from issue description
    - Problem: Customer's original issue
    - Solution: Expert's guidance, structured as steps
    - Context: Product version, environment, edge cases
  Agent reviews and edits draft (avg time: 5 min vs 30 min fresh write)

Step 3 — Review and Publishing:
  Internal KB Review Workflow:
    1. SME reviews for accuracy (< 24 hrs)
    2. Team lead approves for publishing
    3. Article published to internal KB (immediately available)
    4. Optional: Convert to customer-facing article (additional review)
    5. Tag with metadata: product, category, difficulty, last reviewed

Step 4 — Quality Measurement:
  Track per article:
    - Views by agents (adoption signal)
    - "Was this helpful?" ratings (internal)
    - Tickets resolved after viewing (deflection signal)
    - Age of article (triggers review reminder)
  Target: > 80% of collaborations generate at least one KB article

KNOWLEDGE METRICS DASHBOARD:
  ════════════════════════════════════════════════════
  Metric                           | Target    | Industry Avg
  ════════════════════════════════════════════════════
  KB articles from collaborations  | 50/mo     | 15–25/mo
  Collaboration-to-article rate    | > 80%     | 40–60%
  Agent KB search success rate     | > 75%     | 55–65%
  Article freshness (< 6 mo old)   | > 90%     | 60–75%
  Avg time to find answer in KB    | < 3 min   | 5–8 min
  ════════════════════════════════════════════════════

Peer Recognition and Expert Program

EXPERT RECOGNITION FRAMEWORK
=============================

Level 1 — Contributor (Entry):
  Criteria: 
    - 12+ months in support role
    - Resolved 50+ tickets with internal collaboration
    - Created 10+ KB articles
  Benefits:
    - "Contributor" badge on profile
    - Featured in monthly knowledge newsletter
    - Access to advanced training materials

Level 2 — Subject Matter Expert (SME):
  Criteria:
    - Contributor level + 6 months
    - 200+ collaboration responses
    - Average @mention response time < 30 min
    - 50+ KB articles (with > 4.0 avg rating)
    - Recognized by peers and managers
  Benefits:
    - "SME" designation on profile and email signature
    - First dibs on complex/interesting tickets
    - Dedicated collaboration hours (2 hrs/week protected time)
    - SME badge with product area (e.g., "SME — Billing")
    - Input on hiring decisions for product area

Level 3 — Knowledge Champion:
  Criteria:
    - SME level + 6 months
    - Mentored 3+ other agents to Contributor level
    - Led 4+ knowledge sharing sessions
    - KB articles viewed 5000+ times
    - Peer-nominated for recognition
  Benefits:
    - "Knowledge Champion" title
    - Career pathing consideration (management or specialist track)
    - $1,000–$2,000 annual knowledge contribution bonus
    - Speak at company-wide knowledge conference
    - Direct line to product/engineering leadership

RECOGNITION MECHANISMS:
  → Weekly "Knowledge Hero" spotlight in team channel
  → Monthly leaderboard (articles created, help given, articles rated)
  → Quarterly knowledge awards ceremony (best article, most helpful, rising star)
  → Annual "Support Hall of Fame" (top 5 knowledge contributors)
  → Peer nomination system (anyone can nominate anyone)

Escalation Workflow with Knowledge Capture

TIERED ESCALATION MODEL
========================

ESCALATION PATH:

  L1 (Front-line agents):
    - Handle ~70% of all tickets
    - Access to standard KB and macros
    - Can @mention for help
    - Escalation criteria: issue outside scope, needs admin access,
      customer requests specialist, SLA at risk

  L2 (SMEs/Specialists):
    - Handle ~20% of tickets (escalated + complex)
    - Deep expertise in specific product areas
    - Admin-level system access
    - Escalation criteria: confirmed bug, needs engineering input,
      requires code/database access, enterprise customer demand

  L3 (Engineering/Product):
    - Handle ~8% of tickets
    - Bug investigation, code-level troubleshooting
    - Feature requests evaluation
    - Expected response: < 24 hrs for P1, < 48 hrs for P2
    - Return resolution to L2/L1 for customer communication

  L4 (Vendor/External):
    - Handle ~2% of tickets
    - Third-party integrations, infrastructure issues
    - SLA governed by vendor agreement
    - Expected response per vendor SLA
    - Escalation to L4 requires team lead approval

ESCALATION SLA COMPLIANCE:

  Tier | Acceptance Time | Resolution Target | Breach Action
  -----|-----------------|-------------------|--------------
  L1   | Immediate       | 4 hours           | Auto-escalate to L2
  L2   | < 30 min        | 24 hours          | Alert team lead
  L3   | < 4 hours       | 72 hours          | Engage engineering manager
  L4   | Per vendor SLA  | Per vendor SLA    | Executive escalation if needed

KNOWLEDGE CAPTURE AT EACH TIER:
  L1 → L2: Document troubleshooting steps tried before escalation
  L2 → L3: Include environment details, error logs, reproduction steps
  L3 → L4: Full technical summary, impact assessment, urgency justification
  Return path: Solution documented and pushed back to KB for lower tiers

Training and Continuous Learning

CONTINUOUS LEARNING PROGRAM
============================

WEEKLY STRUCTURE:
  → Monday: "Knowledge Kickoff" — Team lead shares 1–2 articles from past week
  → Wednesday: "Case Study Review" — Anonymous review of complex resolved ticket
  → Friday: "Win of the Week" — Best collaboration story shared in team channel

MONTHLY STRUCTURE:
  → Knowledge sharing session (60 min):
     - Rotating presenter (SMEs take turns)
     - Topics: New product features, complex troubleshooting, edge cases
     - Recorded for absent agents and new hires
     - Q&A portion (15 min)
  → Knowledge audit: Review KB articles for accuracy and completeness

QUARTERLY STRUCTURE:
  → Skills assessment: Agents test knowledge across product areas
  → KB refresh sprint: All articles reviewed, outdated ones updated
  → Cross-training: L1 agents shadow L2 for 2 sessions
  → Knowledge metrics review: Celebrate wins, identify gaps

NEW HIRE ONBOARDING — KNOWLEDGE COMPONENT:
  Week 1: Read 15 top-rated KB articles, complete quiz
  Week 2: Shadow 3 experienced agents on live tickets
  Week 3: Handle 10 supervised tickets with mentor review
  Week 4: Create first KB article, present to team
  Month 2: Achieve Contributor status (or receive development plan)

KNOWLEDGE TRANSFER SCORECARD:
  ════════════════════════════════════════════════════════════════════
  Metric                             | New Hire  | Experienced | Target
  ════════════════════════════════════════════════════════════════════
  KB articles read in first month    | 15+       | Ongoing     | 15
  Tickets resolved independently     | 10 (mo 1) | N/A         | 10
  First KB article created           | Month 2   | N/A         | Month 2
  Collaboration @mention responses   | 0 (month 1) | 50+/yr   | 50
  Average CSAT on handled tickets    | 3.5+      | 4.2+        | 4.2
  ════════════════════════════════════════════════════════════════════

Integration Points

Edge Cases

Disclaimer: All rights reserved by Circulos AI. These skills are specifically designed for Claude Code, Claude Cowork, Codex, and OpenClaw. When using or referencing any skill, please provide proper attribution to Circulos AI.