Hooksbase

Getting started / April 25, 2026

AI agent examples (with the event triggers behind them)

A grid of AI agent examples grouped by domain, each with its event trigger annotated

When people ask "what does an AI agent actually do," the honest answer is: respond to a real-world event by reasoning, acting, and either finishing or escalating. The interesting part isn't the model — it's the event that triggers it and the chain of actions that follow.

This guide collects production-shaped agent examples by domain. Each one names the trigger, the action, the success condition, and the event-layer features the agent depends on to stay reliable.

Customer support agents

Email triage agent

  • Trigger: Inbound email to support@your-app.com (parsed via email-to-webhook).
  • Action: Classify intent, look up the customer, draft a reply, and either send it or hand off to a human in a Slack channel.
  • Stops when: A reply is sent or a human picks it up.
  • Event layer needs: Email ingest with attachment relay; downstream dedupe on the message ID; retained replay when a prompt change deserves re-runs of last week's misclassifications.

Ticket-deflection agent

  • Trigger: Form submission from your help center contact page.
  • Action: Search docs, propose a candidate solution, and either auto-resolve or escalate with the proposed solution attached.
  • Stops when: The user confirms resolution or escalation lands in the queue.
  • Event layer needs: Form ingest with file uploads; routing rules that send "billing" topics to one agent and "technical" to another.

Revenue agents

Failed payment recovery agent

  • Trigger: Stripe invoice.payment_failed webhook.
  • Action: Look up the customer's payment history, attempt a card-update flow, notify the account owner if the customer churns.
  • Stops when: Payment retries succeed or the subscription is cancelled.
  • Event layer needs: Starter+ verified Stripe inbound; deterministic replay while payloads are retained (because retries hit Stripe APIs that have side effects); Pro+ alerts on terminal-failure spikes.

Lead enrichment agent

  • Trigger: Form submission from a "request a demo" page.
  • Action: Hit enrichment APIs, score the lead, route to the right SDR, write the decision back to the CRM.
  • Stops when: Lead is in the CRM with an owner.
  • Event layer needs: Form ingest; Pro+ throttling so the agent doesn't burn through enrichment-API rate limits during traffic spikes.

Subscription change handler agent

  • Trigger: Stripe customer.subscription.updated.
  • Action: Reconcile the change against your billing database, update entitlements, notify CS if downgrade volume spikes.
  • Stops when: Database is consistent and notifications sent.
  • Event layer needs: Pro+ strict FIFO ordering so subscription event sequences process in order; queryable provider.eventType from a Starter+ provider pack for routing.

Engineering and ops agents

Release notes agent

  • Trigger: GitHub release event.
  • Action: Pull every PR merged into the release tag, generate a changelog draft, post to Slack for review.
  • Stops when: Draft is posted.
  • Event layer needs: Starter+ verified GitHub inbound and payload transforms to extract just the release tag and PR numbers.

Incident triage agent

  • Trigger: Webhook from your monitoring stack (Datadog, Grafana, custom metrics) when an alert fires.
  • Action: Pull the runbook for the alert, check recent deploys, propose a root cause, page on-call only if the agent's confidence is low.
  • Stops when: The alert resolves automatically or on-call acknowledges.
  • Event layer needs: HTTP ingest from the alerting webhook; routing rules by alert severity; DLQ for terminal failures so unhandled alerts don't get lost.

PR review agent

  • Trigger: GitHub pull_request.opened or pull_request.synchronize.
  • Action: Run a first-pass review (style, common bugs, test coverage), post comments via the GitHub API, request changes if anything is critical.
  • Stops when: A comment is posted or the PR is approved.
  • Event layer needs: Starter+ verified GitHub inbound; Pro+ throttling so a 200-PR migration doesn't burn the model budget; retained replay so prompt changes can re-run yesterday's reviews.

Vulnerability response agent

  • Trigger: GitHub repository_vulnerability_alert (or a third-party scanner webhook).
  • Action: Check affected versions in your dependency graph, classify severity, page on-call for criticals, file a tracking issue otherwise.
  • Stops when: The vulnerability is acknowledged or remediated.
  • Event layer needs: Starter+ verified GitHub inbound when the source is GitHub, destination-side verification for other scanners, and Pro+ alerts on the agent's own DLQ so unhandled vulnerabilities don't sit silently.

Background and scheduled agents

Reconciliation agent

  • Trigger: Cron (every 15 minutes).
  • Action: Compare expected and actual state across systems (e.g. Stripe vs your DB), find drift, attempt auto-correction or alert.
  • Stops when: State is consistent or a delta is reported.
  • Event layer needs: Starter+ scheduled triggers with static payload templates so every tick has a consistent shape; an agent-level guard in case the previous run is still working.

Cost monitoring agent

  • Trigger: Cron (daily).
  • Action: Pull yesterday's cloud spend, compare against budget, flag anomalies, post to a Slack channel.
  • Stops when: Report is posted.
  • Event layer needs: Starter+ scheduled triggers; Pro+ event drains so daily reports flow into your observability stack alongside other metrics.

Inventory monitoring agent

  • Trigger: Cron (hourly) or a Shopify products/update webhook.
  • Action: Check inventory levels, compare to reorder thresholds, ping the buyer if action is needed.
  • Stops when: A reorder is placed or the level is acknowledged.
  • Event layer needs: Either ingest channel works; routing rules that suppress noisy non-actionable updates.

Product and onboarding agents

Onboarding agent

  • Trigger: Clerk user.created event.
  • Action: Enrich the user's profile, personalize the first email, schedule a guided setup task.
  • Stops when: The first email is sent and a task is scheduled.
  • Event layer needs: Verified Clerk inbound; payload transforms to flatten the Clerk user object to what your downstream expects.

Activation agent

  • Trigger: Cron (daily) or a product-event webhook (e.g. feature_used from your analytics stack).
  • Action: Identify users at risk of churning before activation, schedule a personalized nudge.
  • Stops when: The nudge is sent or the user activates on their own.
  • Event layer needs: HTTP ingest; throttling on the nudge channel to respect quiet hours.

Multi-agent systems

Coordinator + specialists

  • Trigger: Any of the above.
  • Action: A coordinator agent receives the event, decides which specialist agent to delegate to, and waits for results.
  • Stops when: All specialists have responded.
  • Event layer needs: A coordinator destination selected by routing rules; if the same event must reach multiple specialists, hand it to a downstream queue or coordinator service that owns the fan-out. Queryable delivery history lets you trace which path was selected.

What every example has in common

Look across the list and the same patterns show up:

  1. A single, well-named trigger. Not "the agent runs when something happens" — this event from this source.
  2. A small, bounded action. Five tools beat fifty.
  3. A clear stop condition. The agent knows when it's done; it doesn't run forever.
  4. An event-layer dependency. Verification, retries, replay, idempotency, observability — every example needs at least three of these.

The model and framework are commodities now. The differentiator is the event layer underneath: how reliably the trigger reaches the agent, how cleanly the result ships out, and how recoverable the system is when something fails.

Hooksbase is the event layer for these agents — HTTP, email, and form ingest on every tier; scheduled ingest, verified provider packs, and transforms on Starter+; programmable routing; retries with backoff; Pro+ strict FIFO where needed; deterministic replay while payloads are retained; DLQ; and delivery history. Pick a workflow blueprint, point it at your agent, and skip the channel-specific work.

Where to go next

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