AI Agent Examples: What Shipped, What Stalled, and Why

Examples are only useful with the guardrail attached. Here are AI agent deployments described the way a buying committee needs to read them.

August 1, 2026 · 8 min read

Ask for AI agent examples and you usually get a list of categories. That is not what a buying committee needs. They need the workflow, the boundary the agent operates inside, the metric it moved, and the reason the ones that failed failed. Here are the deployment patterns we see most often, described that way.

Support: tier-1 resolution inside a fixed intent set

The agent handles a defined list of intents end to end — order status, password resets, returns below a threshold — and escalates everything else with full context. The guardrail is the intent list itself: anything unrecognised goes to a human immediately rather than being improvised. Typical measured outcomes are deflection in the 30–50% range on in-scope volume and a sharp drop in first-response time. What stalls this pattern: teams that let the agent attempt everything on day one and then cannot explain a bad answer.

Finance: invoice intake and exception routing

The agent extracts, validates against the purchase order, codes to the right account, and routes only exceptions to a human. The guardrail is a hard approval gate above a dollar threshold and a full audit trail per document. The measurable number is cost per invoice and exception rate. What stalls it: no clean baseline. If nobody knew the pre-agent cost per invoice, the finance seat has nothing to approve against.

Legal: first-pass contract review

The agent reads inbound agreements against an internal clause library, flags deviations, and produces a summary counsel reads in minutes rather than an hour. It never sends, signs, or negotiates. The guardrail is that the output is advisory only. This is one of the highest-satisfaction deployments precisely because the ceiling on damage is a lawyer disagreeing with a note.

The deployments that succeed all share one trait: the worst-case failure is visible and cheap.

Operations: runbook remediation

The agent detects a known condition and executes a documented runbook — restart a service, scale a pool, rotate a credential — logging every step. The guardrail is the runbook library: no improvised remediation, ever. The measured result is mean time to recovery. What stalls it: an audit team that cannot reconstruct what the agent did at 3am, which is a logging problem rather than an AI problem.

Sales: research, briefing, and CRM hygiene

The agent prepares account briefs before calls and turns transcripts into CRM records after. It writes to the CRM but nothing customer-facing. The metric is seller hours reclaimed and forecast accuracy. What stalls it: pitching it as revenue lift. Reclaimed hours are provable; attributed revenue is argued about for a quarter.

Why examples stall — three recurring reasons

1. 1No baseline. The workflow was never measured, so improvement cannot be proven.
2. 2Unbounded scope. The pilot tried to cover every case and produced an embarrassing failure with no containment story.
3. 3No owner. IT ran the pilot, no business leader had a number at stake, and it quietly expired at renewal.

When you write these up for buyers, keep the shape: workflow, guardrail, metric, failure mode. Three examples in that format do more for a deal than a page of logos.