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Overview

Agents are the core building blocks of Rowboat’s multi-agent system. Each agent carries out a specific part of a conversation, handles tasks via tools, and can collaborate with other agents to orchestrate complex workflows. They are powered by LLMs and can:
  • Respond to user input
  • Trigger tools or APIs
  • Pass control to other agents using @mentions
  • Fetch or process internal data
  • Execute RAG (Retrieval-Augmented Generation) queries
  • Participate in sequential pipeline workflows

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Agent Types

Rowboat supports several types of agents, each designed for specific use cases:
NamePurposeCharacteristics
Conversational Agents (conversation)Primary user-facing agents that interact directly with users and orchestrate workflows.• Can respond to users and orchestrate workflows
• Typically serve as the start agent (Hub Agent)
Task Agents (internal)Specialized agents that perform specific tasks without direct user interaction.• Focused on specific functions
• Return results to parent agents
Pipeline Agents (pipeline)Sequential workflow execution agents that process data in a chain.• Execute in sequence within a pipeline
• Cannot transfer to other agents directly

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Agent Configuration

Agents are configured through two main tabs in the Rowboat Studio interface:

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Instructions Tab

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Description

A clear description of the agent’s role and responsibilities

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Instructions

Instructions are the backbone of the agent’s behavior. Use the Copilot’s structured format for consistency: Recommended Structure: 
## 🧑‍💼 Role:
[Clear description of the agent's role]

## ⚙️ Steps to Follow:
1. [Step 1]
2. [Step 2]
3. [Step 3]

## 🎯 Scope:
✅ In Scope:
- [What the agent should handle]

❌ Out of Scope:
- [What the agent should NOT handle]

## 📋 Guidelines:
✔️ Dos:
- [Positive behaviors]

🚫 Don'ts:
- [Negative behaviors]

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Examples

These help agents behave correctly in specific situations. Each example can include:
  • A sample user message
  • The expected agent response
  • Any tool calls (if applicable)

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Configurations Tab

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Name

Name of the agent

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Behaviour

  • Agent Type: Choose from conversation, internal, or pipeline
  • Model: Select the LLM model (GPT-4.1, GPT-4o, google/gemini-2.5-flash, etc.)

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RAG

  • Add Source: Connect data sources to enable RAG capabilities for the agent

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Creating Your Initial Set of Agents

Let Copilot bootstrap your agent graph.

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Instruct Copilot

Start by telling Copilot what your assistant is meant to do — it’ll generate an initial set of agents with best-practice instructions, role definitions, and connected agents.
Creating agents with Copilot

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Inspect the Output

After applying the suggested agents, take a close look at each one’s:
  • Instructions: Define how the agent behaves
  • Examples: Guide agent responses and tool use
Inspect agent instructions

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Updating Agent Behavior

There are three ways to update an agent:

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1. With Copilot

Copilot understands the current chat context and can help rewrite or improve an agent’s behavior based on how it performed.
Update agent using Copilot

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2. Manual Edits

You can always manually edit the agent’s instructions.
Manually edit agent
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