What is an AI agent?
An AI agent is a system built on an AI model that goes beyond text answers to actually get work done: it understands the goal, decides the next step, uses your connected business tools, and takes actions within clearly scoped permissions. On WKIL you build that agent, connect it to your data and tools, and run it without code.
How an AI agent works
An agent combines four elements on every task:
The model
The language model that interprets the request and shapes the decision. Pick the right one from the WKIL model catalog.
Instructions
The agent's role, responsibilities, tone and boundaries — briefed like a new team member.
Knowledge & memory
The files and sources the agent relies on so it answers from your business reality, not generic information.
Tools & actions
Connections to business systems such as messaging channels, email, spreadsheets and CRM so the agent performs real actions.
Browse the available AI models and supported integrations inside the platform.
Agent vs chatbot vs traditional automation
| Criteria | Chatbot | Traditional automation | AI agent |
|---|---|---|---|
| How it operates | Scripted replies | Fixed steps on a trigger | Chooses the next step from goal and context |
| Unexpected cases | Limited | Stops or fails | Reasons, retries, or escalates |
| Company data | Usually limited | Moves data between systems | Grounds itself in approved knowledge first |
| Outcome | An answer | A transferred record | A completed task inside your tools |
Practical agent use cases
Sales agent
Receives inbound leads, qualifies them with clear questions, and updates records in your CRM.
Support agent
Answers from your policies and documents on the channels your customers already use.
Marketing agent
Helps prepare content and organise campaigns based on your brand material.
HR agent
Answers recurring employee questions from approved internal policies.
Operations agent
Tracks daily requests and coordinates across connected systems.
Detailed example: AI sales agent. Or browse ready-made AI agents and pick the one closest to your role, then adapt it to your business.
How to build an agent on WKIL
- 1Describe the goal and the role you need in plain language.
- 2Add knowledge and documents, and scope the permissions.
- 3Connect the business tools and channels the agent will work on.
- 4Test the agent, launch it, then monitor its activity and results.
Full detail on the agent building workflow page, and operating and governance capabilities on the AI agent platform page.
Governance and permissions
An agent that takes real actions needs clear boundaries: which data it can reach, which tools it may use, and which actions require human review. WKIL lets you scope those boundaries per agent and follow activity from one console.
Frequently asked questions
What is an AI agent?
An AI agent is software built on a language model that goes beyond answering: it interprets a goal, plans steps, uses connected tools and systems, and takes real actions within the permissions you grant it.
How is an AI agent different from a chatbot?
A chatbot answers within predefined scripts. An agent decides the next step and uses tools such as your CRM, email or spreadsheets to complete the task end to end.
Do I need to code to build an AI agent?
No. On WKIL you describe the goal in plain language, add knowledge and permissions, connect tools, then test and launch — no code required.
How do I control what an agent can do?
Each agent's permissions are scoped to specific tools, data and allowed actions, and its activity can be reviewed from the platform console.
Ready to build your first agent?
Start by describing the task; WKIL handles the rest — no code.