AI Agent Examples: 7 Real Business Workflows an Agent Can Run

An AI agent example is a specific business workflow that software now runs end to end: it watches for a trigger, makes the judgement calls a person used to make, carries the work through to a finished result, and only involves a human where it genuinely matters. The clearest way to understand what an AI agent is is to stop looking at the definition and look at the jobs one actually does.

This is no longer a fringe experiment. Gartner predicts that 40% of enterprise apps will feature task-specific AI agents by 2026, up from less than 5% in 2025. But the same firm expects over 40% of agentic AI projects to be cancelled by the end of 2027, mostly because they chase novelty instead of a workflow with a clear return. The examples worth copying are the unglamorous ones tied to a measurable outcome.

The seven below are exactly that. Five are drawn from systems CodeLeap has built and shipped for real UK businesses; two are common patterns we’ve flagged as generic so you can tell the difference. Each one describes the workflow, what the agent does, and the payoff.

AI agent or “just automation”? The honest distinction

The phrase “AI agent” gets stretched to cover almost anything automated, so it’s worth being precise. A true AI agent interprets an unstructured or ambiguous input—an email, a photo, a document—and decides what to do with it. Deterministic automation follows fixed rules: if a deal closes, raise the invoice.

Most of the workflows that actually pay off are a blend. An AI or computer-vision step handles the messy judgement at the edge—reading the test, classifying the ticket, parsing the contract—and hands a clean, structured result to ordinary automation to execute reliably. The label matters far less than whether the workflow runs itself and holds up. Several examples here are automations with one AI step, not fully autonomous agents, and we’ve said so where that’s the case.

Here are the seven at a glance:

#	Workflow	AI, automation, or blend	Payoff
1	Sales cycle, enquiry → signed contract	Automation, AI-assisted	5× lead capacity in 3 months (UK energy supplier)
2	Raising the month’s invoices from closed deals	Automation	~500 invoices/month, no re-keying
3	Routing site defects to the right contractor	Automation, rules-based	Faster fixes, fewer disputes (construction)
4	Reading and routing inbound support	AI (generic pattern)	The right message reaches the right person
5	Turning inbound paperwork into structured data	AI (generic pattern)	No manual data entry
6	Reading a medical test from a phone photo	AI / computer vision	An objective reader the test never had
7	Standardising and quality-checking product images	AI / computer vision	Consistent images at a volume no team matches

1. A sales agent that runs the cycle from enquiry to signed contract

In business energy, the deal usually goes to whoever quotes first and follows up hardest. One UK supplier was doing all of that by hand—copying leads between tools, writing every quote from scratch, chasing signatures over email—which capped how many deals the team could run at once.

CodeLeap automated the full cycle inside the tools they already used. Enquiries from every channel are captured, deduplicated and pushed straight into the pipeline. Quotes are generated from live pricing and the customer’s details in seconds. Everything writes back to the CRM, and signed contracts move themselves through review and into fulfilment, each stage triggering the next.

The payoff: within three months the team was generating five times the qualified leads it had handled before, without adding headcount. The full sales-cycle automation case study has the detail. This one is deterministic automation, not an LLM agent—proof that “runs itself” doesn’t always mean “artificially intelligent.”

2. An invoicing agent that raises the month’s invoices from closed deals

A media group running exhibitions across the UK and US sold stand space to hundreds of companies. Sales lived in Pipedrive, contracts went out through SmartDocs, and the finance team raised around 500 invoices a month by hand in Xero—re-keying the same figures across three systems and chasing paperwork in between.

CodeLeap connected the three tools into one flow. When a deal closes, the matching Xero invoice is raised from the deal’s own fields—value, currency, instalments and payment terms—and saved as a draft for finance to review. Signed contracts are detected and matched back to their deal so the CRM shows where each one stands, and standardised contract documents are parsed into pre-filled deal cards automatically.

The payoff: around 500 invoices a month the finance team no longer types, and the figures that reach the books are the ones agreed on the deal. See the CRM and invoicing automation case study. Note the drafts-for-review step—a deliberate human checkpoint on anything that touches the accounts.

3. A dispatch agent that routes site defects to the right contractor

A large London construction firm was managing major residential sites with dozens of contractors and thousands of snagging items—the defects that have to be fixed before a unit is signed off. Tracking who owed what fix, across spreadsheets and paper checklists, was close to impossible.

CodeLeap built WeSnag, a mobile-first snagging platform. Site staff log a defect from the unit in a few taps—photo, location, trade. Each snag is then routed automatically to the responsible contractor based on trade type and building, contractors are alerted immediately, and anything not addressed within a defined timeframe escalates on its own.

The payoff: hundreds of hours of manual coordination replaced by a single system of record that keeps every contractor accountable at a scale no manager could chase by hand. The routing here is rule-based—trade plus building decides the recipient—which is exactly why it’s reliable enough to trust on a live site.

4. A triage agent that reads and routes inbound support

(Generic pattern.) Most support inboxes are still sorted by a person skim-reading every message to decide what it is and who should handle it. It’s slow, and it’s the first thing to fall behind when volume spikes.

A support-triage agent reads each inbound email or ticket as it arrives, classifies it by topic and urgency, drafts a first response for an agent to approve, routes it to the right team, and escalates anything at risk of breaching an SLA. This is genuine AI territory: the input is unstructured natural language, and the useful judgement—“is this an angry churn risk or a routine password reset?”—is one that rules struggle with.

The payoff: faster first responses, consistent prioritisation, and human agents spending their time replying rather than sorting. This is the kind of workflow our AI agents and automation work is built around, and a sensible first agent for most teams.

5. A document agent that turns inbound paperwork into structured data

(Generic pattern.) Invoices, purchase orders, application forms and contracts arrive as PDFs and scans in no fixed format, and someone re-types the important fields into a system by hand—slow, and a reliable source of errors.

A document-processing agent reads each incoming document, extracts the fields that matter—supplier, amount, dates, line items—validates them against existing records, and either files the result or routes it for approval. Because the input is unstructured and every supplier’s layout is different, this needs real extraction and judgement, not a template. It’s the same capability behind the contract-parsing step in the events example above, generalised.

The payoff: no manual re-keying, fewer transcription errors, and a clean audit trail. This is the core of intelligent document processing, and it pairs naturally with the invoicing and support agents—one agent turns the document into data, another acts on it.

6. A computer-vision agent that reads a medical test from a phone photo

A UK medical diagnostics company had a screening test that shows its result as a colour change—dependable in a clinic, but almost impossible for a patient to judge by eye at home, or to standardise across thousands of different phone cameras and badly lit bathrooms.

CodeLeap made the phone do the reading. A two-step computer-vision algorithm first uses shape detection to find the test in the photo, then colour detection to measure the result inside it—engineered to cope with varied lighting, camera quality and stray objects in frame. A patient app for iOS and Android guides the capture and uploads the result to a clinician dashboard, where results are tracked over time.

The payoff: an objective reading with no clinic visit, delivered in a first version in around ten weeks. The strongest signal came later—a UK university adopted the product into a multi-year clinical study. The client stays unnamed under NDA; the medical test scanning case study covers what we can share. This is an AI agent in the truest sense: it makes a perceptual judgement a person otherwise had to.

7. A vision agent that standardises and quality-checks every product image

A company producing vehicle photography at scale needed every image to look consistent, but was checking each one by hand—so inconsistent framing, stray backgrounds and missed angles kept slipping through to live listings.

Over a multi-year engagement, CodeLeap built the vehicle imaging platform: guided capture apps plus a computer-vision pipeline. The vision models separate each vehicle from its surroundings and drop in a clean, standard backdrop, assemble approved images into interactive 360-degree spins, and check every shot automatically for framing, blur and missing angles—flagging the problems instead of letting them through.

The payoff: quality that no longer depends on who happens to be checking. Every image is captured, cleaned and inspected to the same standard at a volume no team could review by hand, and staff spend their time on the exceptions the agent flags rather than inspecting each shot. It’s a clear illustration of AI handling the judgement and normal software handling the rest.

Where to start with your own AI agent

The pattern across all seven is the same: pick one high-volume, repetitive workflow where the outcome is measurable, then let software carry it from trigger to finished result—using an AI or vision step only where the input is genuinely messy. Start narrow. The energy supplier didn’t rebuild their business; they automated the busywork between “interested” and “signed,” and the fivefold result followed.

Choose a process you can put a number on today—hours spent, invoices typed, tickets sorted, images checked—so you can prove the value before you expand. If you want to see the range of what that looks like in practice, browse the full portfolio, or read how we approach AI process automation end to end. The best first agent is rarely the most impressive one. It’s the dull, expensive workflow nobody enjoys, running itself by next quarter.