AI Agent Use Cases: A Pattern-Level Reference

Eight canonical task patterns rather than eight industries. The same pattern serves customer support in SaaS, patient triage in healthcare, and tenant queries in real estate.

The editorial approach

Vendor use-case content typically organises by industry: AI agents for finance, healthcare, retail, manufacturing. Each chapter then makes specific ROI claims (“reduced handle time by X percent”) that are rarely sourced and rarely transferable. The approach here is different: organise by task pattern, describe what the pattern needs, name the failure modes, and resist the temptation to publish numbers we cannot verify.

For vertical-specific buyer’s guides, the portfolio includes dedicated sister sites (linked at the end of this page). This page covers the pattern level.

Eight canonical patterns

Pattern

Tier-1 deflection

Where it fits: Customer support, internal IT helpdesk, tenant queries, patient triage
What it typically needs: RAG over a knowledge base; bounded tool use (open ticket, send confirmation, read user record).
What can go wrong: Hallucinated answers when the knowledge base does not cover the question; over-confidence on edge cases that should escalate.

Pattern

Research synthesis

Where it fits: Literature reviews, competitive analysis, due-diligence summaries
What it typically needs: Web search or paper search tools; chunking and ranking of long documents; reflection on completeness.
What can go wrong: Citation fabrication; over-summarisation that loses the nuance the user actually wanted.

Pattern

Code review

Where it fits: Pull-request review, security scanning with reasoning context, codebase audit
What it typically needs: Code execution sandbox; access to a structured rubric; LLM-as-judge for consistency.
What can go wrong: Style nitpicks dominating substantive issues; missed context outside the diff.

Pattern

Document review

Where it fits: Contract review, policy compliance checking, RFP triage
What it typically needs: Long-context model or retrieval; structured extraction templates; human review on flagged sections.
What can go wrong: Confident misreading of legal nuance; the “passed review” failure when the document was glossed.

Pattern

Data wrangling

Where it fits: ETL-style transformation over semi-structured sources; entity resolution across messy datasets
What it typically needs: Code execution; schema validation; deterministic checks on the output rows.
What can go wrong: Silent data corruption when the agent invents plausible-looking values for missing fields.

Pattern

Workflow orchestration

Where it fits: Multi-step processes spanning several SaaS tools (ticket, calendar, CRM, file system)
What it typically needs: Authenticated tool access to each system; idempotent operations; transactional grouping where possible.
What can go wrong: Stateful side-effects that cannot be rolled back; permission escalation across systems.

Pattern

Sales-lead enrichment

Where it fits: Web research on prospects; matching company facts to internal records
What it typically needs: Web search; CRM read/write; deduplication and confidence scoring.
What can go wrong: Spamming low-confidence matches; privacy and compliance issues with the data sources used.

Pattern

Incident response

Where it fits: On-call alert triage, runbook execution, post-incident summarisation
What it typically needs: Read-only access to monitoring; controlled write access (acknowledge alert, restart service); human approval before destructive remediation.
What can go wrong: Cascading remediation when a runbook is wrong; alert fatigue if the agent escalates too aggressively.

Sister-site references

For the verticals with dedicated buyer’s-guide treatment in this portfolio:

agenticrunbook.com - incident response and SRE workflows
agenticcontractreview.com - legal-document and contract review
agenticinterviewer.com - HR interviewing and candidate screening
agenticediscovery.com - eDiscovery and litigation document workflows
agenticsalescall.com - AI sales calls and conversational outreach

Each treats its vertical as a buyer’s guide; this page treats the underlying patterns. Cross-references run both directions.

The common failure pattern

Across all of these use cases, the most consistent cause of production failure is the same: scoping too broadly. The pitch-deck version of the use case is “an agent that handles all of customer service.” The shipped version that survives is “an agent that answers shipping questions and escalates everything else.” The narrow version is faster to build, easier to evaluate, and dramatically more reliable in production. Vendor content encourages the broad scope; the engineering reality favours the narrow scope.

Working heuristic: pick a task small enough that you can write a 20-example evaluation set in an afternoon. Ship that. Expand only after the narrow version is reliable.

Frequently asked questions

Why organise by pattern rather than by industry?

The same task pattern serves different industries. Tier-1 deflection is the same architectural shape whether the customer is a SaaS user, a hospital patient, or a tenant. Industry-organised use-case content tends to make ROI claims (“X percent deflection in healthcare”) that are not transferable and are usually unsourced. Pattern-organised content focuses on what is actually transferable: the architecture, the tools needed, and the failure modes.

What is the most common failure across these use cases?

Scoping too broadly. Production deployments that succeed pick a narrow task and nail it before expanding. The pitch-deck use case (an agent that handles all your customer service) is typically a multi-year project. The shipped use case (an agent that answers FAQ questions about shipping and order status, then escalates everything else) is a six-week project. Vendor content encourages the broad scope; production teams that survive choose the narrow scope.

Can one agent handle multiple use cases?

Sometimes. A multi-agent system with a supervisor that routes to specialist agents (FAQ deflection, ticket creation, billing-record lookup) can present as a single agent to the user. The architectural cost is the supervisor and the routing logic. The benefit is a unified interface to multiple capabilities. The honest answer is that this is one of the harder architectures to make reliable.

Where can I read deeper coverage of specific verticals?

For sister-site verticals in this portfolio: agenticrunbook.com covers SRE/incident response in depth; agenticcontractreview.com covers legal-document review; agenticinterviewer.com covers HR interviewing; agenticediscovery.com covers eDiscovery; agenticsalescall.com covers AI sales calls. This page treats them at the pattern level only.

Related references

Patterns - the architectural patterns these use cases use
Build vs Buy
Honest Limitations - what can go wrong
How to Build

Sources and Further Reading

Anthropic, Building effective agents (2024). Includes worked examples of several patterns above.
Stanford HAI, AI Index Report, deployment patterns chapter (annual).
MIT Technology Review, AI coverage.
L. Wang et al., A Survey on LLM-based Autonomous Agents, arXiv:2308.11432 (2023).
Microsoft AutoGen, documentation, framework example workflows.