AI Agents That Actually Work: Monitoring Your Business 24/7

The term "AI agent" is everywhere right now, usually accompanied by vague promises about autonomous systems that will replace entire departments. Let's skip the hype and talk about what AI agents actually do well today — and how we deploy them in production.

What an AI Agent Actually Is

An AI agent is a program that observes a system, makes decisions based on what it sees, and takes actions. That's it. No consciousness, no general intelligence. Just a loop:

1. Observe — check the state of something
2. Decide — determine if action is needed
3. Act — take the appropriate action
4. Report — log what happened

The magic isn't in any individual step. It's in running that loop continuously, 24/7, without getting tired, distracted, or forgetting.

Our Pipeline Monitor Agent

One of our clients runs a data pipeline that feeds operations across multiple locations. When it breaks, teams make wrong decisions. So we built an agent that watches it constantly. Every 15 minutes, it checks:

• Are all serverless functions healthy? (checking execution logs)
• Is the data warehouse fresh? (comparing timestamps against expected schedule)
• Is the operational database in sync? (checking row counts and latest records)
• Are there any error spikes in the monitoring platform?

When something is wrong, it doesn't just send an alert. It classifies the severity, identifies the likely root cause, and suggests a remediation step. A human still makes the final call — but they go from "something is broken, what do I do?" to "the warehouse refresh failed because of a schema change, here's the fix."

Our Email Monitor Agent

Another agent we've deployed watches a business email inbox on a regular interval. It categorizes incoming messages, extracts action items, and routes them appropriately. Not with complex NLP — with a language model that understands context.

A vendor invoice gets flagged for accounting. A customer complaint gets escalated to operations. A sales pitch gets archived. All without a human touching the inbox.

Why Most AI Agents Fail

The agents that don't work share common traits:

• Too autonomous too fast — giving an agent the ability to take high-stakes actions without guardrails. Start with read-only observation and manual approval for actions.
• No feedback loop — if you can't see what the agent decided and why, you can't trust it. Logging and observability are non-negotiable.
• Solving the wrong problem — agents are great at repetitive monitoring and triage. They're bad at creative strategy and nuanced judgment. Pick the right problems.
• No clean data foundation — an agent that monitors dirty data will give you confident, wrong answers. Fix the data first.

The Right Architecture

Production AI agents need three things:

• Scheduled execution — run on a cron schedule, not continuously. Most business processes don't need sub-second response times. 3-15 minute intervals work for monitoring. Daily for reporting.
• Structured outputs — the agent should produce structured data (JSON, database records), not just text. This makes it easy to build dashboards, trigger downstream actions, and audit decisions.
• Human-in-the-loop — for any action with consequences (sending a message, modifying data, triggering a process), require human approval. As trust builds, you can gradually expand the agent's autonomy.

Getting Started

Pick one monitoring task your team does manually. Something repetitive that someone checks daily — stale data, error logs, inventory levels, whatever. Build an agent that does that one check and sends a summary. Run it for a week. Iterate.

That's not glamorous. It's not autonomous AGI. But it's an AI agent that actually works — and that your team will actually trust.