What is an AI agent? Build agents that do real work
An agent is the reasoning layer: it understands a goal, decides which approved tools to use, and works toward an outcome. AI Agent gives that reasoning a clear operating contract instead of relying on one giant prompt.
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- Test
The concept
A plain-language definition before the product implementation.
An AI agent is a model with a job, context, and tools
A useful agent is more than a chatbot. It has instructions that define its role, trusted context it can retrieve, memory it can carry forward, and tools it can call. The model can choose a path within those boundaries, making agents a strong fit for open-ended work such as investigation, analysis, drafting, and triage.
How AI Agent uses it
Build the operating contract visually
AI Agent's builder uses the same blocks as the live product. Start with the agent's role and model, then attach knowledge, memory, reusable skills, connections, and workflows. Each block stays inspectable, so the agent's capabilities never disappear inside a prompt.
Choose the reasoning model
Set the model and effort level that match the work instead of hard-coding one model everywhere.
Give it bounded context
Attach only the knowledge, memory, skills, and connections this agent should use.
Test through real work
Chat with the agent, inspect tool activity, and place it inside repeatable workflows when the path needs control.
From idea to a working system.
- 01
Define
Name the role, outcome, instructions, and retry behavior.
- 02
Equip
Attach trusted context, reusable skills, and approved tool access.
- 03
Run
Use the agent in chat, a team, or a durable workflow.
Use agent judgment where the path is open
Best for
Investigation, analysis, drafting, and triage where the model needs to choose among approved context and tools.
Choose another pattern when
A fixed sequence with required branches or approvals. Put that control in a workflow and use an agent only inside the steps that need judgment.
Included in AI Agent
The product capabilities behind the idea.
- Visual block-based agent builder
- Multiple model providers and reasoning settings
- Knowledge, memory, skills, and connection attachments
- Tool activity and run traces
Frequently asked questions
Keep exploring
Related product guides
Workflows
A workflow turns a business process into an explicit graph. The model can reason inside an agent step, while the surrounding sequence keeps triggers, tools, branches, approvals, and outputs predictable.
Knowledge
Knowledge gives an agent a trusted source set beyond its model training. AI Agent turns approved documents and websites into retrievable context, then records what the run actually used.
Memory
Memory gives an agent continuity without forcing a person to repeat the same context in every prompt. AI Agent lets each agent use recent messages, semantic recall, structured working memory, and observations where they help.
Build the smallest useful version first.
Start with the agent and one real task. Add workflow control, context, and approvals when the work shows you where they matter.