IT playbook · AI Employee: Ivy

Web-Sourced Troubleshooting

Deflection rate on generic issues > 70%

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The problem

Half of IT tickets are answered by Stack Overflow. Not because the answer isn't in-house, but because in-house documentation is stale + fragmented + hard to search. Engineer / user hits a specific error message; internal wiki doesn't have it; external web search returns 40 results with mixed quality; time-to-resolution is limited by search + reading + trial-and-error, not by IT capability.

At a glance
Trigger
Chat
Approvals
None
What it does
Read-only
Systems
Web search · KB
How it feels in production

An hour-by-hour walkthrough.

Priya messages Ivy: "getting error 'signature verification failed' when running kubectl apply — never seen this before." Ivy searches internal + external sources in parallel: - Internal: runbook wiki, prior tickets, Slack history — no direct match - External: Stack Overflow (12 relevant results), GitHub Issues (3 relevant), Kubernetes docs (2 pages) - Vendor status pages: no active incident - Community forums (Discord, Reddit r/kubernetes): recent thread from 2 hours ago Ivy synthesizes: "This error typically means the kubectl client's OIDC token cache is stale or the auth webhook returned an invalid signature. Common causes ranked by frequency: 1. Expired token in ~/.kube/cache/oidc (fix: rm the cache, re-auth) — most common 2. Auth webhook misconfiguration on cluster side (fix: check auth webhook logs) 3. Clock skew between client and cluster (fix: sync system clock) For your setup (per your kubeconfig context), option 1 is most likely. Run: `rm ~/.kube/cache/oidc/* && kubectl get pods` — this should re-auth and succeed. Want me to walk through it?" Priya tries option 1. Works. Total time from error to resolution: 90 seconds. Ivy's answer is grounded in real sources (linked); trustworthy; specific to Priya's context.
How it works

Step by step.

  1. 01

    Detect novel error / issue query

    User describes an error, symptom, or specific issue. Internal-first search; escalate to external if no match.

    Internal wiki · Ticket history · Slack search
  2. 02

    Multi-source web research

    Stack Overflow, GitHub Issues, official docs, vendor status, community forums. Curated source list per technology.

    Web search · SO API · GitHub · Vendor docs
  3. 03

    Synthesize + rank by relevance to user's context

    Not a list of links — synthesized answer with cited sources, ranked by likelihood based on user's setup / environment.

    Reasoning · Context-aware ranking
  4. 04

    Present with actionable next step + verification

    Direct instruction to try. Verification step to confirm resolution. Fallback options if primary doesn't work.

    Slack · Teams · Web UI
  5. 05

    Log resolution + feed internal wiki

    Successful resolution feeds internal knowledge base for future users. Compounds over time.

    Internal wiki · Ticket resolution log
Systems and wiring

What you connect to make this run.

Internal wiki · Confluence · Notion · Ticket history

read+write

First-source. External answers feed back to internal wiki for future queries.

Web search · Stack Overflow · GitHub Issues

read

External source aggregation. Curated per technology for quality.

Vendor docs + status + release notes

read

Vendor-authoritative sources prioritized. Version-specific answers.

Slack · Teams

read+write

Primary support surface. Interactive; walks user through resolution.

What changes

Before and after, honestly.

% of novel issues resolved via web sources
Before
20-40% (user manually searches)
After
70-85% (Ivy searches + synthesizes)
Time from error to resolution (novel issue)
Before
30-120 minutes
After
2-15 minutes
IT ticket volume from Stack-Overflow-answerable issues
Before
15-30% of tickets
After
3-6%
Internal wiki quality (from resolution feedback)
Before
Static + stale
After
Continuously enriched
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Complex tooling queries benefit from web-sourced context.

Frequently asked

Answers about this playbook.

What about outdated Stack Overflow answers (5+ years old)?

Recency + relevance weighted. Older answers cited only if still applicable; newer answers prioritized. Version-specific matching where technology has evolved.

How does it handle vendor-proprietary tooling with limited public docs?

Vendor docs + support portal + private community. Best-effort with clearly labeled uncertainty. Escalates when public sources exhausted.

Can it distinguish good answers from bad on Stack Overflow?

Vote count + accepted answer + recency + comment quality all weighted. Higher-quality answers ranked higher; low-quality flagged as uncertain.

What about internal-only technology (in-house tools)?

Internal-only tools have no external answers; internal wiki + team Slack search. Team-specific expertise routed to appropriate SME.

How does it protect against wrong answers causing harm?

Actions with side effects (rm, delete, modify) always explained + confirmed before user runs. Never blindly executes on user's behalf.

See it run on your data.

Free plan, no credit card. Connect the systems this playbook needs and run it against a past event first.

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