AI agents: autonomous workflows, multi-agent systems and tool use

An AI agent combines language understanding with tool use and its own planning step: it receives a goal, chooses the next action, calls APIs, reads databases, writes results back—and checks whether the goal was reached. RPA follows fixed click scripts; an agent handles exceptions and decides. A chatbot answers questions; an agent executes tasks.

AI agents pay off when inputs are unstructured (emails, PDFs, free-text requests), exceptions are frequent or decision logic is complex. Classic automation (RPA, scripts) is better for fully stable, rule-based flows with structured inputs—there it is faster, cheaper and easier to test. The pragmatic mix: the agent handles hard exceptions, RPA handles volume.

We implement guardrails on three levels: a limited tool set (the agent may only call explicitly approved APIs), human-in-the-loop for actions with external impact (send email, overwrite files, trigger payments) and audit logging of every agent step. We also test with adversarial prompts before go-live. The goal: useful agents that are never autonomously destructive.

Document processing (invoices, contracts, forms), research agents (market and competitor briefs), code agents (review, tests, documentation), process agents (email → classify → ticket → draft reply) and on-demand reporting from databases. The key is a clearly bounded task with measurable output.

Financial transactions above thresholds, medical or legal advice with liability, HR decisions and critical infrastructure settings should always include human approval. Human-in-the-loop is also mandatory in high-risk EU AI Act areas. We build approval workflows into agents operating in those domains.

OpenAI GPT-4o for strong tool use and enterprise DPAs; Anthropic Claude for long documents and instruction following; Google Gemini for multimodal tasks; open models (Llama, Mistral) on-premise for highly sensitive data. We choose per use case, privacy and budget.

API cost depends on token volume—moderate use often €100–500/month, intensive use €2,000–5,000/month. Orchestration infrastructure €100–400/month; maintenance and prompt tuning €500–2,000/month. Compare against hours of manual work saved; payback often within 3–12 months on well-chosen use cases.

Observability means logging every step and tool call, latency and error monitoring, output quality checks, audit trails for regulators and alerts on unexpected behaviour. We use LangSmith, OpenTelemetry or custom logging—transparency is prerequisite for trust.

Agents suit multi-step tasks with tool access and approvals. RPA fits fixed UI steps. Chatbots answer questions. Agents usually need a knowledge base and clear escalation.