AI agents are AI systems that can work independently to complete tasks without constant human direction. Unlike a chatbot that simply responds to what you ask, an AI agent can take action. It can read emails, extract information from documents, make decisions based on rules you set, and perform work across multiple systems.
Think of an AI agent as a digital employee. Just as you might hire someone to handle accounts payable, you can deploy an AI agent to do the same job. You give it instructions, set boundaries for what it should do, and it works through tasks on its own.
For example, an AI agent might receive an invoice via email, extract the vendor name and amount, match it to a purchase order in your ERP system, check if the amounts align, and either approve the invoice automatically or flag it for your review if something looks off.
The key difference between AI agents and traditional automation is adaptability. While workflow automation follows rigid if-then rules, AI agents can handle variations and edge cases.
They can read unstructured data like emails or PDFs, understand context, and make judgment calls based on the instructions you provide. This makes them particularly useful for business processes that involve documents, emails, and systems that don't talk to each other easily.
How is an AI agent different from RPA or workflow automation?
Traditional RPA (Robotic Process Automation) follows exact scripts. If an invoice format changes or a field moves, the automation breaks.
AI agents can adapt to variations. They understand that "Total Amount" and "Grand Total" mean the same thing, and they can extract information even when document formats change.
RPA works great for perfectly structured processes, but most business processes involve exceptions, variations, and unstructured data where AI agents excel.
What kinds of tasks can AI agents actually do?
AI agents handle tasks that combine reading, reasoning, and taking action. For example, they can process incoming invoices by extracting data from PDFs, matching line items to purchase orders, checking for discrepancies, applying your approval rules (like "auto-approve anything under $500"), and routing exceptions to the right person.
They can also handle accounts receivable by sending payment reminders, matching incoming payments to invoices, and following up on overdue accounts.
Other common use cases include reconciliation (matching transactions across systems), data entry, document processing, and coordinating between systems that don't integrate well.
How do you train or teach an AI agent what to do?
You don't write code or create flowcharts. Instead, you describe the job in plain language, similar to training a new employee.
You might say "When an invoice comes in, extract the vendor name, invoice number, and total amount. Check if we have a matching purchase order. If the amounts match and it's under $1,000, approve it. If something's off or it's over $1,000, flag it for Sarah's review."
You also provide examples of good and bad scenarios so the agent understands edge cases. Many platforms let you refine the agent's behavior over time by reviewing its work and adding clarifications.
Do AI agents make mistakes? What if they do something wrong?
Yes, AI agents can make mistakes, just like human employees. The difference is that you can configure oversight mechanisms. You might set rules that anything above a certain dollar threshold requires human approval.
You can review a sample of the agent's work periodically. And you can set up activity logs that track every action the agent takes, so you can audit decisions after the fact.
Zamp addresses this by building activity logs into every digital employee. Every action gets recorded with full transparency, so you can see exactly what the agent did and why. Zamp agents also use a "Needs Attention" status to flag items they're uncertain about instead of guessing. This means the agent will escalate edge cases to you rather than making risky decisions on its own.
How long does it take to set up an AI agent?
Setup time varies based on complexity. A simple agent that processes invoices and routes them based on amount might take a few hours to configure and test. More complex agents that work across multiple systems, handle various document types, or require nuanced decision-making might take several days.
The advantage is that once set up, the agent runs continuously. Compare this to hiring and onboarding a human employee, which typically takes weeks or months before they're fully productive.
Will an AI agent replace my team?
AI agents typically handle repetitive, high-volume tasks, which frees your team to focus on work that requires human judgment, relationship building, or strategic thinking.
For example, instead of having your AP team manually enter hundreds of invoices each week, an agent handles the routine ones while your team focuses on vendor negotiations, resolving discrepancies, and managing strategic supplier relationships.
Most companies use AI agents to increase capacity rather than reduce headcount. They can handle growth without proportionally growing the team.
Can AI agents integrate with our existing systems?
Most AI agent platforms can connect to common business systems through APIs or integrations. This includes ERPs like NetSuite or SAP, procurement tools, email systems, databases, and communication tools like Slack.
The agent doesn't replace your existing systems. Instead, it works across them, pulling data from one system, processing it, and updating another system. This is especially valuable when your systems don't integrate well with each other, because the agent acts as a bridge.
What happens if our process changes? Do we have to rebuild everything?
No. Since you define the agent's instructions in plain language rather than hard-coded rules, you can update them as your process evolves.
If you change your invoice approval thresholds from $500 to $1,000, you simply update the instructions. If you add a new field you want the agent to capture, you add that to the configuration.
The agent adapts to the new instructions without requiring technical re-engineering.
How much does an AI agent cost compared to hiring someone?
Costs vary by platform and complexity, but AI agents typically cost a fraction of a full-time employee.
A digital employee might cost $1,000 to $3,000 per month depending on volume and features, compared to $50,000+ annually for a human employee when you factor in salary, benefits, and overhead. The agent also works 24/7 without breaks or vacation.