CHATBOTS

IT Helpdesk Slack Bot: Agentic Multi-Step Request Resolver

An agent assigned to a request handles it end to end in Slack — searching Confluence, asking clarifying questions, filing a Linear ticket when action is needed.

CategoryChatbots
Enginepaperclip
Difficultyadvanced
Triggerevent
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerEmployee @-mentions the IT bot in SlackSlack
  • ActionAgent searches Confluence runbooks and policiesConfluenceConfluence
  • LogicAsk clarifying question if details missing
  • LogicDecide resolve-now vs needs human action
  • ActionFile Linear ticket with gathered contextLinearLinear
  • OutputPost resolution or ticket link and close threadSlack

What it does

Assigns an autonomous IT agent to each incoming request and lets it work the whole problem: it reads the question, searches your Confluence docs, asks the employee follow-ups when the request is ambiguous, decides whether a human action is required, and either resolves in-thread or files a Linear ticket — all from one conversation.

When to use it

When requests are messy and multi-step ("my laptop won't join the VPN after the update") and a single canned answer won't do. The agent reasons across docs and dialogue instead of pattern-matching one message.

How it works

  1. 1A mention of the IT bot in Slack triggers the flow and hands the thread to the agent.
  2. 2The agent searches Confluence for relevant runbooks and policies.
  3. 3If key details are missing, it asks the employee a clarifying question in-thread and waits.
  4. 4The agent decides: resolvable now, or needs IT hands-on.
  5. 5For hands-on cases it opens a Linear ticket with the gathered context attached.
  6. 6It posts the resolution or the ticket link back to the thread and marks the conversation closed.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect SlackChannels, DMs, threads, mentions.
  2. 2
    Connect ConfluenceSpaces, pages, blueprints.
  3. 3
    Connect LinearIssues, projects, cycles, triage.
  4. 4
    Connect OpenAIModels, embeddings, files.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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