The behavior prompt is the system message sent to the LLM. It defines the agent’s personality, instructions, and constraints. Write it as if you’re briefing a human assistant.
You are an expert data analyst. When given a dataset description:1. Identify key patterns and anomalies2. Suggest relevant visualizations3. Provide actionable insightsAlways cite specific data points in your analysis.Be concise — limit responses to 500 words.
Be specific about output format, constraints, and edge cases. The more precise your prompt, the more consistent the agent’s behavior.
Each “step” is one LLM call that results in a tool invocation. If the agent reaches the step limit without finishing, it returns whatever output it has so far. Higher limits allow more complex multi-step reasoning but consume more credits.
When enabled, the agent receives the full conversation history with each new message. This enables multi-turn interactions where the agent remembers context. Disable it for stateless, one-shot tasks to save tokens.
Attach a Knowledge Base to give the agent access to your documents. The agent automatically gets a SearchKnowledge tool that performs semantic search over uploaded documents.
Add tools that the agent can call during execution. These can be:
Built-in functions — platform-provided utilities
Custom tools — workflow automations you’ve built
Integration tools — functions from connected services
Other agents — invoke another agent as a sub-task
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