Interactive / build an agent

Assemble an AI agent. See exactly what it would do.

Every working agent is a pipeline: a trigger that wakes it, steps it works through with tools, and an output a human actually wants. Build one below by clicking blocks. The builder explains your agent back to you in plain language, and it will call you out if you give it power without oversight.

Start from an example

The canvas is empty. Pick a trigger below, add steps, choose an output, and watch the agent take shape.

What this agent does

Assemble a flow and the plain-language description appears here.

agent.config

# illustrative agent config (pseudo-yaml)
agent:
  name: my-first-agent
  on: <choose-a-trigger>
  pipeline:
    - <add-steps>
  out: <choose-an-output>
  guardrails:
    least_privilege: true
    log_every_action: true
    human_approval: not_required_for_selected_actions

1 / Trigger

What wakes the agent up. Pick one.

2 / Steps

Up to 5 working steps. Approval does not use a working slot. Use the canvas to remove a step.

3 / Output

What comes out the other end. Pick one.

Everything on this page runs in your browser. Nothing you build here is saved or sent anywhere. The config is illustrative pseudo-code, not a runnable file.

Method note: this is an original educational model for sketching execution order. It is not a security review, risk assessment or production configuration. Last reviewed: 2026-07-18.

Frameworks behind the guardrails

AI Risk Management Framework National Institute of Standards and Technology, 2023Govern, map, measure and manage are the broader risk functions behind this simplified builder.
OWASP Top 10 for Agentic Applications OWASP Foundation, 2026Useful context for tool misuse, identity, privilege and human oversight risks.
Model Context Protocol architecture Model Context Protocol, 2025The protocol specification stresses explicit user consent, control and access boundaries.

Last reviewed: 2026-07-18