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Agents are the working units inside SketricGen. A Brand Agent may be a single customer-facing agent with curated tools. An AI Workforce may contain multiple agents that collaborate on internal work.

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Agent Configuration

Each agent can include:
  • Name and role
  • Instructions
  • Model selection
  • Tool access
  • Knowledge Base access
  • Structured output
  • Main-agent or sub-agent behavior

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Instructions

Write instructions like a job description:
  • What is the agent responsible for?
  • What should it avoid?
  • Which tools can it use?
  • When should it ask a question?
  • When should it call a sub agent?
  • What does a successful answer look like?

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Models

Agents use the supported model catalog in the product. Available models can include OpenAI, Claude, Gemini, DeepSeek, Qwen, and other approved models. The in-product model picker is the current source of truth. Start with the default model and settings. Lower randomness for customer-facing Brand Agents, and use stronger reasoning models for AI Workforce workflows that involve research, analysis, code, or larger files. Some models support settings such as temperature, top P, reasoning controls, verbosity, structured outputs, tool calling, file input, or image input. Other models do not. If a setting is not visible for the selected model, treat it as unsupported for that model. You can change the model per agent without rebuilding the workflow structure. Change one model setting at a time, then re-test with the same prompt and inspect traces before treating the result as production-ready.

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Tools

Only give agents the tools they need. Brand Agents should get a smaller, safer tool set. AI Workforce agents can use broader internal connectors when the workflow requires it. For AI Workforce, add marketplace skills when the agent needs an optimized task process. Add connectors when the agent needs to take action in a business app. The best workflows usually combine clear instructions, the right model, a small set of tools, and one or two relevant skills.

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Structured Outputs

Structured outputs are useful when an agent or downstream system needs predictable JSON. Use structured outputs for final API-like responses or focused sub-agent results, not for every normal human-facing answer.