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What is an AI Agent?

An AI assistant answers when asked. An AI agent acts without being asked. It runs a sequence of steps, calls tools, makes decisions, and delivers a result — automatically. Here is what that means in practice and why it matters for your business.

The Core Distinction: Reactive vs Proactive

The most useful way to understand AI agents is by contrast with AI assistants. An AI assistant is reactive: a person asks a question, the AI retrieves relevant information and generates an answer. The human initiates every interaction.

An AI agent is proactive. It operates on a trigger — a schedule, an event, or a condition — and executes a predefined sequence of steps without waiting for a human to prompt it. It can call external APIs, query databases, read and write files, send messages, and make decisions along the way. When it is done, it delivers a result.

Think of it as the difference between a search engine and an employee. A search engine gives you information when you look for it. An employee you have briefed properly does the work and brings you the output.

How an AI Agent Works

Under the hood, an AI agent is an LLM combined with tools and a defined workflow. When the agent runs, it:

1. Receives its input (from a schedule, trigger, or human request)
2. Decides which steps to execute and in what order, based on its configuration
3. Calls tools — connectors to databases, APIs, or external services — to gather or act on information
4. Generates outputs — a report, an email draft, a database record, a Slack message
5. Optionally, hands off to a human for review before completing a high-stakes action

In Open Enterprise, agents are defined in YAML. You describe the steps, the connectors to use, and the logic — no Python or LangChain required. The platform handles execution, scheduling, and logging.

Types of AI Agents by Trigger

AI agents fall into three categories based on what starts them running:

• Scheduled agents — run on a cron schedule (every morning at 9am, every Monday, the first of each month). Useful for recurring reports, digests, and monitoring tasks.
• Event-triggered agents — run when something happens: a new form submission, a webhook from an external system, a change in a database record.
• Manual agents — run when a human explicitly triggers them, often for tasks that need to happen irregularly but follow a consistent process.

Real Business Examples

The abstraction becomes clearer with concrete examples. Here is what AI agents look like in practice:

• A sales research agent runs every morning, pulls a list of target accounts from the CRM, researches each company for buying signals, and adds a summary note to each record
• A support triage agent runs when a new ticket arrives, classifies its urgency and topic, pulls relevant documentation, and drafts a first-line response for the support agent to send or edit
• A DevOps monitoring agent runs every 15 minutes, queries infrastructure logs, and sends a Slack alert only when anomalies above a defined threshold are detected
• A weekly report agent runs every Friday at 4pm, queries the database for the week's key metrics, generates a formatted narrative, and emails it to the leadership team

What Makes a Good Agent

Well-designed agents share three properties: they have a narrow, well-defined scope; they operate on reliable, structured inputs; and they produce outputs that are easy for a human to verify. The most common mistake is building an agent that tries to do too much at once. An agent that does one thing well is far more reliable than one that attempts an entire workflow end-to-end without checkpoints.

This is why multi-step workflows are often composed of several focused agents working in sequence rather than a single monolithic agent. Each step is testable and auditable on its own.

Agents vs RPA vs Traditional Automation

AI agents are often compared to RPA (robotic process automation) tools like UiPath or Automation Anywhere. The key difference is how they handle variation. RPA works well when inputs are perfectly consistent — it breaks when the format changes. AI agents handle natural language, messy data, and unstructured inputs because the LLM can interpret context and adapt.

AI agents also differ from traditional scheduled scripts because they can reason. A script executes a fixed sequence; an agent can decide which path to take based on what it finds.

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