15 Real AI Agent Examples (and the Template to Build Each One)

Most “AI agent examples” lists are a wall of ideas you can't actually run. This one is the opposite. Every example below is a pattern that real companies have shipped, and every one maps to a template you can open and run in the browser in a couple of clicks — no setup, no API keys to wrangle. Think of it as a catalog to steal from: find the agent closest to your problem, run the template, then bend it to your data.

Here's the thing that makes a list like this possible at all: under the surface, these 15 agents are almost the same program. They differ in their prompt, their tools, and the documents they can read — not in their fundamental shape.

Browse the full set by department first, then read the details underneath — each example names the company shipping that pattern (where it's public) and links the template that builds it.

Customer-facing agents

This is where agents have earned the most public receipts. Klarna's OpenAI-powered assistant handled two-thirds of its customer-service chats in its first month — work the company equated to roughly 700 full-time agents. Bank of America's Erica has passed two billion customer interactions. The pattern is the same one you can run below: a bounded, grounded assistant with clear escalation.

• 1. Product support assistant — answers customer questions from your own docs and cites its sources, so it can't invent a refund policy. Build it: Product Support Assistant template.
• 2. Support ticket triage — reads an incoming complaint and outputs category, priority, and sentiment, then routes it — the unglamorous automation that actually saves a support team. Build it: Support Ticket Triage template.
• 3. Sales outreach drafter — researches a prospect and drafts a tailored first email instead of a mail-merge blast. Build it: Sales Outreach Drafter template.

Research & knowledge agents

The archetype here is Morgan Stanley's advisor assistant, which lets financial advisors query around 100,000 internal research documents in plain language. And JPMorgan's COIN reads commercial-loan contracts and was reported to reclaim on the order of 360,000 hours of manual review a year. Both are the same move: point a grounded agent at a corpus nobody has time to read.

• 4. Research Q&A over a document corpus — ask questions and get answers grounded in your papers or knowledge base, with citations. Build it: Research Q&A on AI Papers template.
• 5. Long-context document analyst — feed it a 100-page contract or report and have it extract the clauses, risks, and numbers that matter. Build it: Long-Context Document Analyst template.
• 6. Graph-RAG explorer — answers relationship questions (“which vendors touch which systems?”) that a flat vector store simply can't. Build it: Graph RAG Explorer template.

Engineering agents

Coding is the domain where autonomous agents have advanced fastest — it's the whole premise of tools like GitHub Copilot and Cognition's Devin, and the reason benchmarks like SWE-bench (agents fixing real GitHub issues in sandboxed repos) became an industry yardstick. You don't need a full autonomous engineer to get value, though; the highest-ROI coding agents are narrow.

• 7. Code review assistant — finds bugs, explains why they're bugs, and rewrites the code with comments. Build it: Code Review Assistant template.
• 8. Python traceback fixer — paste a stack trace, get the root cause and a patch. Build it: Python Traceback Fixer template.
• 9. GitHub repo explainer — scrapes an unfamiliar repository and explains how the pieces fit together before you touch it. Build it: GitHub Repo Explainer template.

Data & operations agents

The least glamorous category and often the highest ROI: turning messy, unstructured input into clean, structured records. The same document-understanding capability behind JPMorgan's COIN is what powers a humble invoice parser — and Morgan Stanley's meeting-notes tool (Debrief) is the same idea pointed at call transcripts.

• 10. Invoice parser — turns a messy invoice into a validated, typed JSON record you can trust downstream. Build it: Invoice Parser template.
• 11. CRM data cleanser — normalizes names, dedupes records, and fixes fields across a whole export. Build it: CRM Data Cleanser template.
• 12. Meeting action-item extractor — turns a raw transcript into owned, dated action items. Build it: Meeting Action-Item Extractor template.

Content & marketing agents

The category with the lowest barrier to a useful first agent. These don't need tools or a knowledge base to be valuable — just a sharp prompt and consistent structure — which makes them the ideal place to build your first one.

• 13. SEO meta-tag generator — writes titles and meta descriptions tuned to a target keyword and length. Build it: SEO Meta-Tag Generator template.
• 14. Brand voice translator — rewrites any copy into your house style, consistently. Build it: Brand Voice Translator template.
• 15. Landing page roaster — critiques a page's copy and conversion logic, bluntly, before you ship it. Build it: Landing Page Roaster template.

These aren't toys — the receipts

It's worth grounding the whole list in the numbers the companies themselves have published, because the gap between “cute demo” and “this replaced a workflow” is exactly what these figures measure.

From example to your own agent

Running a template is step one. When you want to make one of these yours, the path is short: open the closest template, then customize it in the Agent Builder — swap in your knowledge base, your tools, your guardrails. Need several of these to work together (triage → research → draft)? Wire them on the visual swarm canvas. And when it's ready for the world, embed it on your own site as a chat widget or a task bot with one iframe.

Two things worth reading before you build in earnest. If you're not sure a use case even needs an agent, our feasibility framework will save you a wasted month. And if any of these should run autonomously — deciding their own steps rather than following a script — read Autonomous AI Agents: what they are and how to build one next, because autonomy is exactly where the failure modes start.