AI Agents for Workflow Automation

AI agent workflows, what it actually means

Many business processes consist of several steps across different systems: checking a new order, reconciling stock, generating an invoice, notifying the customer. Classic automation tools like Zapier or Make chain these steps together with rigid rules. An AI agent goes a step further: it interprets unstructured input such as an email, a PDF or a chat message, makes decisions within defined rules, and chooses the right next step, instead of failing whenever a case deviates from the standard.

Technically, we combine a language model with existing automation platforms such as n8n or Make, or a dedicated agent framework like LangChain, connected to your existing systems via APIs. The agent gets clearly defined tools, such as create invoice, send email or update record, and a scope within which it acts independently but under control.

Typical use cases

We most often build agents for automatically processing incoming documents such as invoices, orders and contracts with subsequent system updates, for coordinating multi-step approval processes, and for automated research and data enrichment, such as completing CRM records from publicly available sources. Monitoring systems with automatic responses to defined events is another common case.

Where human oversight stays in place

For financially or legally significant actions such as payment approvals, sending contracts or legally binding customer communication, we always build in an approval point where a human confirms the action prepared by the agent before it executes. We only recommend fully autonomous agents without any oversight for low-risk, well-safeguarded processes.

How an agent project runs at Großartiger

We start with a detailed process mapping: which steps, which systems, which decision points, where manual intervention happens today. This produces a process map that clearly marks which steps can be safely automated and where an approval point stays necessary. The first agent for a scoped sub-process is usually ready for testing within two to three weeks.

The agent initially runs in shadow mode, meaning it suggests actions without executing them independently, so your team can review the quality of its decisions. After five to seven weeks, once the error rate is low enough, non-critical steps go live, critical steps permanently keep the approval point.

Cost and engagement model

An agent workflow project typically runs between EUR 5,000 and 15,000 per project, depending on process complexity and system integrations. An agent for a clearly scoped sub-process with one or two system connections sits at the lower end, an agent spanning multiple systems with complex decision logic at the upper end.

Ongoing operation, meaning monitoring, model costs and process adjustments, is billed as a monthly fee. Since agent workflows usually need fine-tuning after launch, we recommend close monitoring for the first three months, after which effort drops significantly.