--- title: "AI Agent Examples: 7 Real Production Cases" description: "7 real AI agent examples with numbers: customer support, sales, tenders and internal knowledge. What we built, for whom, and what it delivered." canonical: "https://www.whatsnext-ai.com/blog/ai-agent-examples" published: "2026-08-13T00:00:00.000Z" updated: "2026-08-13T10:45:41.901Z" --- # AI Agent Examples: 7 Real Production Cases 7 real AI agent examples with numbers: customer support, sales, tenders and internal knowledge. What we built, for whom, and what it delivered. ![A portfolio of built AI agents across support, sales, tenders and knowledge](https://fgzcpjbyiakhjifaciaj.supabase.co/storage/v1/object/public/media/steve-a-johnson-1fd-e7ioblw-unsplash.jpg) Real AI agent examples: a customer service agent handling tickets in 11 languages, a lead engine scoring qualified leads daily, and a tender agent taking proposals from 10 days to 2 minutes. Below are seven real systems, with numbers. Search for "AI agent examples" and you mostly get definitions and generic lists: an agent that "could" do this or that. The examples below are not categories, they are systems we built for named clients, each with a sourced result from their own case page. They are grouped by use case, so you can quickly find the shape that resembles your own process. ### Customer support #### Just Carpets: customer service in 11 languages For Just Carpets we built a customer service agent as an orchestrator with specialised sub-agents: one fetches order status from the order API, one finds and sends the right invoice from ExactOnline, and one creates credit invoices by inverting the original invoice and posting it back. The orchestrator runs on hard rules: never surface raw API output, escalate on doubt, and answer in the customer's language. - 100,000+ customer conversations handled, 24/7 in 11 languages. - 100x lower cost per resolved ticket (staff around €5.00, own stack around €0.05). The invoice and credit-invoice sub-agents show this goes beyond chatting: the agent acts inside your accounting system. More on that in [AI invoice automation](https://www.whatsnext-ai.com/blog/ai-invoice-automation). [Read the Just Carpets case](https://www.whatsnext-ai.com/cases/just-carpets). ### Sales and lead generation #### Backstage IT: pipeline from real-time signals Backstage IT wanted more pipeline without more SDRs. We built an agent that collects real-time hiring signals (LinkedIn, TechMap, Adzuna), scores them against their ideal customer profile and prepares the outreach. Two SDRs now run a process that would previously have taken a full team. - 40% of pipeline comes from AI-surfaced deals. - 16 hours reclaimed per week. - 25% of all leads replied. [Read the Backstage IT case](https://www.whatsnext-ai.com/cases/backstage-it). #### Strix: an autonomous lead engine for €1 a day For Strix we built an autonomous lead engine that combines seven signals, including LinkedIn engagement, and scores each one against the ICP. The output arrives as a weekly digest, so the sales team only looks at qualified leads. - 435 qualified leads from a single run. - Around 27 qualified leads per day, ongoing. - €1 a day to run the full engine. - Around 51% conversion from raw signal to qualified lead. [Read the Strix case](https://www.whatsnext-ai.com/cases/how-strix-surfaced-435-qualified-leads). #### iClicks: leads and content in one pipeline At iClicks two agent pipelines run side by side: one for lead enrichment and scoring with automated outreach, and a three-agent pipeline, a brief agent, a writer agent and an editor agent, that produces SEO content. The outreach reporting goes out automatically. - 7,500+ qualified leads generated. - 28% of leads replied to messages. - 1,000+ automated reports delivered. [Read the iClicks case](https://www.whatsnext-ai.com/cases/how-iclicks-built-a-scalable-ai-sales-engine). ### Tenders and proposals #### CIRFOOD: from 4 to 45 tenders a year CIRFOOD had hit a ceiling on public tenders: dissecting one took days, so many opportunities went unbid. We built a tender extraction tool plus a bid-price calculator that reads 72 structured fields per tender, so the team has a substantiated bid in minutes instead of days. - 45 tenders a year, up from 4, with the same team. - Around 2 minutes per tender, down from 10 days. [Read the CIRFOOD case](https://www.whatsnext-ai.com/cases/how-cirfood-scaled-from-4-to-45-tenders-a-year-without-adding-headcount). ### Internal knowledge and research #### Traffic Today: "Nessy" monitors 100+ accounts For agency Traffic Today we built "Nessy", a Slack-native agent that reads along per client across Slack, email, VOIP, Asana and analytics, scoring sentiment and urgency. Account managers see early which client needs attention, without manually checking every channel. - 100+ client accounts monitored daily. - 10 hours per week returned to the team. - €1M+ of capacity freed at agency level. [Read the Traffic Today case](https://www.whatsnext-ai.com/cases/traffic-today). #### More2Win: five years of documents searchable in seconds More2Win sat on five years of proposals and documents in OneDrive, but no one could quickly pull the right thing. We built a private, EU-hosted research engine (Pinecone plus Microsoft Graph) that makes the whole document pile searchable with citations, without the data leaving the company. - 30 seconds per question. - 640x cheaper per proposal than manual search. - 1,000+ documents available in seconds. [Read the More2Win case](https://www.whatsnext-ai.com/cases/turning-scattered-knowledge-into-an-ai-powered-research-engine). ### What these seven examples have in common Four things recur in every case. First: these are agents that do something in a real system (an ERP, a CRM, a document archive), not chat windows that return text. Second: they run in code on an owned stack that the client controls and can audit, not as a separate low-code layer with a per-unit price. Third: there is a human in the loop where it counts. On doubt, the agent escalates with context instead of forcing an answer. Fourth: every number above comes from that client's live case page, not from an average in a sales deck. That is the difference between an example and a proof. If you want the concept clear first, read [what an AI agent is](https://www.whatsnext-ai.com/blog/what-is-an-ai-agent). ### What is the difference between an AI agent and agentic AI? "Agentic AI" describes the property that a system independently plans and executes multiple steps to reach a goal; an AI agent is the concrete system that does it. The examples above are agents with agentic behaviour: they reason, call tools and act. More on this in [agentic AI explained](https://www.whatsnext-ai.com/blog/agentic-ai). ### What does it cost to have an AI agent built? That depends on the scope, the systems the agent has to talk to and the degree of autonomy. The Strix engine runs for €1 a day; a customer service system like Just Carpets' is a larger build. We break down the cost structure in [how much does an AI agent cost](https://www.whatsnext-ai.com/blog/how-much-does-an-ai-agent-cost). ### Is ChatGPT an AI agent? Not on its own. ChatGPT is a language model that generates text; it becomes an agent only when it can call tools and take actions inside your systems, with rules and escalation. The distinction is worked out in [AI agent vs chatbot](https://www.whatsnext-ai.com/blog/ai-agent-vs-chatbot). ### Want an example like this for your own process? Each of these seven systems started with one bounded process that cost too much time or money. If something in the examples above resembles your situation, take a look at our [AI agents](https://www.whatsnext-ai.com/ai-agents) or [book a free consultation](https://www.whatsnext-ai.com/contact). We will work out which process delivers your first proof. *Sources: every figure above comes from What's Next's own client case studies (Just Carpets, Backstage IT, Strix, iClicks, CIRFOOD, Traffic Today and More2Win), each linked inline to its case page. These are our own project results, not third-party estimates.*