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RAG (Retrieval Augmented Generation) agents with built-in tools represent the most powerful no-code agents available on the Prisme.ai platform. These agents combine access to your organization’s knowledge with specialized capabilities that extend beyond conversation, enabling them to perform complex tasks, access external information, generate media, and process data.

Understanding RAG + Tools Agents

These advanced agents integrate three key capabilities:

Foundation Models

Powerful language models providing reasoning and generation capabilities

Knowledge Retrieval

Access to your organization’s documents and information

Specialized Tools

Built-in capabilities that extend functionality beyond conversation
This combination creates versatile, capable agents that can:
  • Answer questions using your internal knowledge
  • Retrieve up-to-date information from the web
  • Generate visual content based on requirements
  • Perform data analysis and calculations
  • Create structured documents and outputs
  • Execute multi-step processes with diverse requirements

Available Built-in Tools

Prisme.ai provides several powerful tools that can be activated without writing code:
Description: Access current information from the internet using Serper integrationKey Capabilities:
  • Search for current information beyond the model’s knowledge cutoff
  • Access specific websites for reference
  • Verify facts and retrieve up-to-date data
  • Gather content from public sources
Best For:
  • Research and information gathering
  • Validating time-sensitive information
  • Supplementing internal knowledge with public data
  • Competitive research and market intelligence
Configuration Options:
  • Search domains to include/exclude
  • Number of results to retrieve
  • Safe search settings
  • Result depth control

When to Use RAG + Tools Agents

These advanced agents are ideal for scenarios that require a combination of:

Knowledge Access

Situations requiring specific organizational information

Current Information

Needs for up-to-date data beyond internal documentation

Visual Content

Requirements for custom images or visualizations

Data Processing

Calculations, analysis, or data transformation tasks

Multi-capability Workflows

Processes that combine different types of tasks

Complex Problem Solving

Scenarios requiring multiple approaches and resources

Example Use Cases

Purpose: Help users research topics by combining internal knowledge with current information and analysis.Tool Utilization:
  • Retrieves relevant internal research and documentation
  • Searches the web for current information and trends
  • Generates data visualizations to compare findings
  • Creates structured research summaries
Example Interaction:

Creating RAG + Tools Agents

Building these advanced agents involves a structured process:
1

Define Agent Purpose and Requirements

Clearly specify what your agent needs to accomplish.Key considerations:
  • Primary use cases and scenarios
  • Required knowledge sources
  • Necessary tools and capabilities
  • User expectations and needs
  • Success criteria and metrics
2

Prepare Knowledge Resources

Assemble and organize the information your agent needs.Activities include:
  • Document collection and curation
  • Content organization and structuring
  • Metadata creation for improved retrieval
  • Validation of information accuracy
  • Knowledge base configuration
3

Select and Configure Tools

Determine which built-in tools your agent requires.For each tool:
  • Enable required capabilities
  • Configure settings and parameters
  • Define usage boundaries and limitations
  • Set up authentication if required
  • Test individual tool functionality
4

Create System Instructions

Develop comprehensive guidance for your agent.Key components:
  • Agent role and purpose definition
  • Knowledge utilization guidelines
  • Tool selection and usage criteria
  • Response formatting requirements
  • Error handling and limitations
5

Develop Tool Usage Instructions

Provide specific guidance for when and how to use each tool.Include for each tool:
  • When to use (clear criteria)
  • How to use effectively
  • Result interpretation guidance
  • Error handling approaches
  • User communication during tool use
6

Test and Refine

Validate and iteratively improve your agent.Testing approach:
  • Scenario-based evaluation
  • Tool usage verification
  • Knowledge retrieval assessment
  • Edge case testing
  • Real user feedback collection

Implementation in AI Knowledge

Creating a RAG + Tools agent in Prisme.ai’s AI Knowledge is straightforward:
Start by creating a new agent in the AI Store.Steps:
  1. Navigate to AI Knowledge
  2. Click “Create New Agent”
  3. Provide name and basic description

Tool-Specific Instructions

To maximize the effectiveness of each built-in tool, include specific guidance in your system instructions:
When to Use:
How to Use Effectively:
Example Prompt Section:
When to Use:
How to Use Effectively:
Example Prompt Section:
When to Use:
How to Use Effectively:
Example Prompt Section:

Best Practices for RAG + Tools Agents

Clear Tool Selection Logic

Provide explicit criteria for when to use each toolExample:

User Communication During Tool Use

Keep users informed about tool usage and processesExample:

Integrate Knowledge and Tool Outputs

Combine information from different sources coherentlyExample:

Handle Tool Failures Gracefully

Provide alternative approaches when tools don’t work as expectedExample:

Troubleshooting Common Issues

Symptoms:
  • Agent fails to find relevant information
  • Responses ignore available knowledge
  • Retrieved information is incorrect or outdated
Solutions:
  • Verify knowledge base configuration and content
  • Improve document chunking and organization
  • Enhance retrieval instructions in system prompt
  • Add more specific examples of knowledge utilization
  • Test with focused queries to validate retrieval
Symptoms:
  • Agent uses tools when unnecessary
  • Agent fails to use tools when needed
  • Consistent selection of suboptimal tools
Solutions:
  • Refine tool selection criteria in system instructions
  • Provide explicit examples of when to use each tool
  • Add clear guidance for choosing between similar tools
  • Include decision tree for tool selection logic
  • Test with scenarios that require tool selection decisions
Symptoms:
  • Ineffective web searches returning irrelevant results
  • Low-quality image generation
  • Code that errors or produces incorrect results
Solutions:
  • Improve tool-specific instructions and examples
  • Add more detailed guidance on effective tool usage
  • Provide templates for common tool operations
  • Include error handling instructions for each tool
  • Test each tool independently with focused scenarios
Symptoms:
  • Disjointed responses that fail to synthesize information
  • Tool outputs presented without context or interpretation
  • Failure to connect retrieved knowledge with tool results
Solutions:
  • Enhance instructions for information synthesis
  • Add examples of well-integrated responses
  • Include specific guidance on combining knowledge and tool outputs
  • Provide templates for presenting multi-source information
  • Test with scenarios requiring information integration
Symptoms:
  • Slow response times
  • Tool execution timeouts
  • Memory errors during complex operations
Solutions:
  • Optimize tool usage guidelines for efficiency
  • Add instructions for breaking complex tasks into manageable steps
  • Provide fallback options for resource-intensive operations
  • Include guidelines for managing large datasets
  • Test with progressively complex scenarios to identify limits

Advanced Configuration Techniques

For organizations seeking to maximize the effectiveness of their RAG + Tools agents, consider these advanced approaches:

Tool Chaining

Configure agents to use tools in sequence for complex workflowsImplementation:
Tool chaining enables sophisticated multi-step processes that combine different capabilities.

Contextual Tool Configuration

Adapt tool behavior based on user type or request contextImplementation:
Contextual configuration improves relevance for different user groups.

Hybrid Knowledge Approach

Strategically combine internal knowledge with web informationImplementation:
This approach ensures accurate internal information while benefiting from external context.

Progressive Disclosure

Structure responses to present essential information firstImplementation:
Progressive disclosure improves usability by preventing information overload.

Enterprise Integration Considerations

When deploying RAG + Tools agents in enterprise environments, consider:
  • Usage Policies: Define clear guidelines for appropriate tool usage
  • Content Review: Establish processes for reviewing agent outputs
  • Audit Trails: Implement logging for tool usage and information sources
  • Access Controls: Limit specific tools to appropriate user groups
  • Compliance Checks: Ensure tool outputs meet regulatory requirements
Implementation Example:
Protect sensitive information when using tools:
  • Data Handling: Establish clear rules for handling sensitive information
  • Tool Boundaries: Define what data can be processed by each tool
  • Output Scrubbing: Implement checks for sensitive information in outputs
  • Authentication: Manage service credentials securely
  • User Permissions: Align tool access with user authorization levels
Implementation Example:
Prepare for widespread adoption and usage:
  • Resource Management: Monitor and optimize tool usage patterns
  • Usage Quotas: Implement appropriate limits for resource-intensive tools
  • Caching Strategies: Cache common tool results when appropriate
  • Load Balancing: Distribute tool processing across resources
  • Performance Monitoring: Track response times and resource utilization
Implementation Example:

Next Steps

Ready to create your own RAG + Tools agent? Continue with these resources:

AI Store Guide

Learn how to use the AI Store platform effectively

Testing Best Practices

Learn how to thoroughly test and validate your agents