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  • Reviewed work and interview prep
  • Individual feedback every week
KLYMB.AI
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Career track 02This job is changing

L1/L2 Support, becoming Conversational AI Operations Specialist

Troubleshooting, ticket handling, incident response, escalation and customer communication.

Choose this trackSee the 30 days
AI skillsRAGAI agentsLLMOpsEvalsAI safetyGuardrails
Support agents wearing headsets at their desks
days
30days
checkpoints
4checkpoints
interview-prep days
2interview-prep days

What's changing in this role

Tier-one tickets are increasingly answered by AI assistants. The people who run those assistants, their knowledge, escalation rules and failure cases, come from support.

What you carry over

  • + How customers really describe problems
  • + Product knowledge that isn't documented
  • + De-escalation instinct

Who it is for

Built for people like you.

  • Customer support and helpdesk executives
  • Graduates targeting IT support roles
  • Support staff moving from L1 to L2

Skills covered

What you will practise.

  • Troubleshooting
  • Ticket handling
  • Incident response
  • Escalation
  • Customer communication
  • Knowledge base writing
  • AI assistant operations
A team gathered around a laptop, talking through a piece of work

30-day overview

Every day, planned.

Four weekly phases of daily problems, an assessment after each week, and interview prep on Days 29–30. Open a week to see every day.

Enroll now
Week 1Days 1–7FoundationsThe automated queueHow ticket deflection works and which tiers still need people.
  1. Day 1Anatomy of a good ticket: Rewrite five poor tickets with correct priority and category.
  2. Day 2Structured troubleshooting: Diagnose a 'can't log in' issue step by step.
  3. Day 3Customer communication: Reply to an angry customer without escalating the situation.
  4. Day 4SLAs and priority: Assign priority to 20 tickets against an SLA matrix.
  5. Day 5Logs for support: Read application logs to find the error behind a complaint.
  6. Day 6What can a bot close?: Classify a queue by what an AI assistant can resolve.
  7. Day 7Checkpoint: Queue classificationClassify a month of tickets by what a bot can close, and justify the exceptions.Practical task + short written reasoning, scored against a rubric
Week 2Days 8–14Practical executionKnowledge engineeringTurning undocumented support knowledge into content an assistant can answer from.
  1. Day 8Writing a knowledge article: Turn a solved ticket thread into a clear help article.
  2. Day 9L2 investigation: Reproduce a reported issue and collect evidence for engineering.
  3. Day 10Escalation notes: Write an escalation engineers can act on immediately.
  4. Day 11Knowledge gaps: Find the top questions your knowledge base cannot answer.
  5. Day 12Macros vs. real answers: Decide when a template helps and when it harms.
  6. Day 13Retrieval-ready content: Restructure articles so an AI assistant answers accurately.
  7. Day 14Checkpoint: Knowledge domainTurn one support domain into usable retrieval content.Practical task + short written reasoning, scored against a rubric
Week 3Days 15–21Real-world problem solvingIncidents, flows and escalationIncidents under pressure, plus intents, handover rules and refusal behaviour for assistants.
  1. Day 15Major incident simulation: Handle a live outage: triage, updates and escalation.
  2. Day 16Status page updates: Write customer updates at 15, 60 and 120 minutes into an incident.
  3. Day 17Root cause summary: Write a post-incident summary for customers and leadership.
  4. Day 18Designing handover to a human: Define when an assistant must hand over.
  5. Day 19Refusals and safety: Write rules for what an assistant must never answer.
  6. Day 20Difficult escalations: Handle a VIP complaint that crosses three teams.
  7. Day 21Checkpoint: Flow designDesign intents, handover and refusals for one product area.Practical task + short written reasoning, scored against a rubric
Week 4Days 22–28Portfolio and interview readinessQuality, evals and portfolioMeasuring containment, accuracy and harm; reviewing transcripts.
  1. Day 22Support metrics: Interpret FCR, CSAT, AHT and containment for a support team.
  2. Day 23Transcript review: Review 20 bot conversations and flag failures.
  3. Day 24Improvement plan: Propose three changes that reduce repeat tickets.
  4. Day 25Portfolio: support playbook: Package your articles, flows and escalation rules.
  5. Day 26Portfolio: walkthrough: Present your playbook in 5 minutes.
  6. Day 27Interview drill: troubleshooting: Talk through a live troubleshooting scenario.
  7. Day 28Checkpoint: Assistant reviewPresent a working assistant design with its evaluation results.Practical task + short written reasoning, scored against a rubric

Weekly checkpoints

Four checkpoints. Real feedback.

Each one is marked on your reasoning, not just the output: and you defend it.

  1. 7After Day 7Queue classificationClassify a month of tickets by what a bot can close, and justify the exceptions.
  2. 14After Day 14Knowledge domainTurn one support domain into usable retrieval content.
  3. 21After Day 21Flow designDesign intents, handover and refusals for one product area.
  4. 28After Day 28Assistant reviewPresent a working assistant design with its evaluation results.
A woman presenting her work to colleagues in a meeting room

Expected outcomes

What you will be able to do.

  • Troubleshoot methodically and document findings
  • Handle tickets and escalations within SLA
  • Communicate clearly during incidents
  • Write knowledge content AI can use
  • Evaluate AI assistant quality

Role-specific projects

Evidence you can show.

  1. 01Support playbook: knowledge articles, escalation matrix and incident templates
  2. 02AI assistant flow design with evaluation of real transcripts

Interview preparation

Practise the questions L1/L2 Support interviews actually ask.

Preparation runs through Week 4, then the most-asked questions on Days 29–30. Here is what you will be asked, and what a strong answer looks like.

  • Days 29–30Interview prep after Week 4
  • Three kinds of questionTechnical, scenario, behavioural
  • Model answersWhat a strong answer shows
  1. 01Technical

    “A user says 'the internet is not working'. Walk me through your steps.”

    A strong answer shows

    Asks clarifying questions and narrows down layer by layer.

  2. 02Technical

    “What is the difference between an incident and a problem?”

    A strong answer shows

    Correct ITIL distinction with an example.

  3. 03Scenario

    “Three P1 tickets arrive at once. How do you prioritise?”

    A strong answer shows

    Uses impact and urgency, communicates, and escalates appropriately.

  4. 04Scenario

    “An AI assistant gave a customer wrong refund information. What do you do?”

    A strong answer shows

    Fixes the customer issue first, then the knowledge source and the rule.

  5. 05Behavioural

    “Tell me about calming down a frustrated customer.”

    A strong answer shows

    Empathy, ownership and a clear resolution.

  6. Topics covered

    • Troubleshooting methodology
    • Ticket prioritisation and SLAs
    • Incident management
    • Customer communication
    • ITIL basics
    • AI in support
    Enroll now