AI Agents and Workflow Automation
Traditional RPA automates processes that are predictable and rule-based. AI agents go further — they can handle processes where data varies, language needs to be understood, or decisions depend on context.
How AI agents differ from RPA
| RPA robot | AI agent | |
|---|---|---|
| Best for | Stable, rules-based processes | Variable processes requiring contextual understanding |
| Data | Structured | Structured and unstructured |
| Decisions | Fixed rules | Can analyse and evaluate |
| Language understanding | No | Yes |
| Typical use | Invoices, reports, data transfers | Email analysis, document classification, complex workflows |
When to consider an AI agent
- The process involves understanding or classifying text
- Data is unstructured (emails, free-form documents, conversations)
- Decisions depend on context, not just fixed rules
- You want to automate customer communication or internal workflows
When AI agents are not the right fit
- The process is simple and always identical — RPA is better
- There's insufficient data to configure the agent
- 100% accuracy is required with zero tolerance for any error margin
Typical AI agent use cases
- Email content analysis and automatic routing
- Document classification and data extraction
- More complex workflow automation
- Enquiry triage: sorting incoming requests and preparing draft replies for human review
- Collecting and summarising information from several systems into a single report
What we build
An AI agent project follows the same practical path as our other automation work:
- Process discovery — we map the workflow, the data it uses, and where human judgement is genuinely needed.
- Agent design and configuration — instructions, rules, and guardrails tailored to your process, with clear limits on what the agent may decide on its own.
- Integrations — connecting the agent to the systems the process lives in: email, document storage, ERP/CRM, internal databases.
- Testing with human oversight — the agent is verified on real historical cases before go-live, and critical decisions are routed to a person.
- Deployment and support — monitoring, accuracy reviews, and adjustments as your process evolves.
Technologies
We choose tools to fit your process and infrastructure — not the other way around. Depending on the project this can include workflow platforms such as n8n, Make, or Microsoft Power Automate, large language model APIs (such as OpenAI or Anthropic Claude), and UiPath where classic RPA is part of the solution. We discuss the options openly during discovery.
Frequently asked questions
- No — they complement each other. Many projects use both: RPA for stable parts, AI for the more complex ones.
- Yes, integration options depend on the platform. We'll discuss your specific requirements.
- Accuracy depends on the process type and data quality. We always build in human oversight capability for critical decisions.
Related services:
Interested in AI agent capabilities?
Get in touch and we'll discuss whether AI solutions are a good fit for your situation.