// directory · 15 entries

AI agent use cases.

Practical patterns for presales and enterprise IT. Each entry names the problem, how an agent helps and the kind of tools involved.

$ grep --category

presales01

problem: Teams spend days assembling answers to long questionnaires from scattered past responses.
how an agent helps: An agent retrieves approved past answers, drafts responses per question and flags gaps for experts.
tools: LLM, vector search, SharePoint or Drive

presales02

problem: Notes from discovery calls are inconsistent and requirements get lost.
how an agent helps: An agent turns call transcripts into structured requirements, risks and open questions.
tools: Transcription, LLM, CRM

presales03

problem: Early sizing estimates depend on a few senior people.
how an agent helps: An agent applies your sizing rules to captured requirements and produces a reviewable estimate with assumptions listed.
tools: LLM, spreadsheets, pricing rules

presales04

problem: Account teams lack quick, current context on competing offers.
how an agent helps: An agent compiles public information into a short briefing with sources for each claim.
tools: Web search, LLM, docs

sales05

problem: Reps prepare for meetings by hand across many websites.
how an agent helps: An agent gathers public news, filings and org changes into a one-page brief before each meeting.
tools: Web search, LLM, calendar

sales06

problem: Opportunity records go stale and forecasts suffer.
how an agent helps: An agent reads emails and meeting notes, suggests field updates and asks the owner to confirm.
tools: CRM API, email, LLM

sales07

problem: Generic proposals don't reflect what the client said.
how an agent helps: An agent tailors standard proposal sections using discovery notes, keeping approved language intact.
tools: LLM, document templates

it ops08

problem: On-call engineers sift through noisy alerts.
how an agent helps: An agent groups related alerts, pulls recent changes and suggests a likely cause and runbook.
tools: Monitoring tools, ITSM, LLM

it ops09

problem: Change requests arrive incomplete and get bounced back.
how an agent helps: An agent checks each request against policy, fills obvious gaps and lists what's missing.
tools: ITSM, LLM, policy docs

data10

problem: Business users wait on analysts for simple questions.
how an agent helps: An agent translates questions into queries on a governed data model and explains the result.
tools: SQL warehouse, semantic layer, LLM

data11

problem: Bad records reach dashboards before anyone notices.
how an agent helps: An agent runs checks, explains anomalies in plain language and opens tickets for owners.
tools: Data pipelines, LLM, ticketing

security12

problem: Analysts spend time gathering context for every alert.
how an agent helps: An agent enriches alerts with asset, user and threat-intel context and drafts a triage note.
tools: SIEM, threat intel, LLM

security13

problem: Staff can't easily find what security policy allows.
how an agent helps: An assistant answers questions from approved policy documents and cites the section.
tools: LLM, retrieval, intranet

support14

problem: Repeat questions fill the support queue.
how an agent helps: An assistant answers from the knowledge base and hands over to a person with full context when unsure.
tools: Help desk, retrieval, LLM

support15

problem: Articles drift out of date as products change.
how an agent helps: An agent spots resolved tickets with no matching article and drafts updates for review.
tools: Help desk, LLM, docs