Summary: An AI agent is an LLM-based system that can plan steps, use tools (APIs, databases, browsers), and complete tasks under supervision. Unlike a chatbot that only answers, an agent acts.

Chatbot vs Workflow vs Agent

Chatbot Workflow automation AI agent
Flow Conversation Fixed (if/else) Dynamic, chooses steps
Tools Few Many, predefined Chooses its own
Predictability Medium High Lower
Best for FAQ Deterministic processes Open-ended, highly varied tasks

Practical principle: use deterministic workflows wherever possible and an agent only where reasoning is truly needed.

Example Use Cases

  • Support: read tickets, search the knowledge base, draft replies for approval.
  • Sales: research prospects, summarize company profiles, draft emails.
  • Operations: email triage, extraction from documents, CRM updates.
  • Internal analyst: Q&A over company data with controlled SQL.
  • Developer: summarize logs, help triage bugs.

Basic Architecture

  1. LLM as the reasoner.
  2. Tools: functions it may call (look up data, send email, create ticket).
  3. Memory/context: history and knowledge base (RAG).
  4. Orchestration: n8n or custom code.
  5. Guardrails: output validation, action limits, human approval.
  6. Observability: log every step and token cost.

Mandatory Guardrails

  • Least privilege for each tool (read-only first).
  • Human-in-the-loop for risky actions (sending money, deleting data, emailing customers).
  • Cost and step limits so the agent cannot loop forever.
  • Prompt-injection protection: treat email and web content as untrusted data.
  • Data privacy: do not send sensitive data to a model without a clear policy; consider controlled processing options.

How to Start (5 Steps)

  1. Pick one narrow task with easy-to-check results.
  2. Define tools and their limits.
  3. Build a "draft for human approval" version.
  4. Evaluate with 30-50 real examples.
  5. Raise autonomy gradually once accuracy is proven.

Measuring Success

Share of tasks done correctly, time saved, human intervention rate, cost per task.

FAQ

Do AI agents replace RPA?

No. RPA excels at deterministic processes; agents at unstructured data and contextual decisions. They are often combined (RPA).

Is it safe for company data?

Yes, with a data policy, access control, and anonymization where needed.

Next steps

Have a task that fits an AI agent? Talk to Oneflux; we start with a controlled prototype.