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 integration

Key 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:

User: I need to understand the competitive landscape for cloud storage solutions, focusing on security features.

Agent: I'll help you research this topic. Let me check our internal knowledge first, then supplement with current market information.

[Accesses knowledge base for internal research on cloud storage security]

Based on our internal analysis from Q1 this year, the key security features in cloud storage include:
- End-to-end encryption
- Zero-knowledge architecture
- Multi-factor authentication
- SOC 2 compliance
- Data residency options

Let me check the current market landscape to see if there have been any recent developments.

[Uses web browsing to search for current information]

I've found some updated information. In the last three months, several major providers have introduced:
- Quantum-resistant encryption options
- Biometric authentication integration
- Enhanced ransomware protection features

Would you like me to create a visualization comparing the top competitors based on these security features?

User: Yes, please create a comparison chart.

[Uses code interpreter to generate a visual comparison]

Here's a visual comparison of the top cloud storage providers and their security features:
[Image: Comparative chart of cloud storage security features]

Would you like me to focus on any specific aspect of this competitive landscape?

​
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:

​
Best Practices for RAG + Tools Agents

Clear Tool Selection Logic

Provide explicit criteria for when to use each tool

Example:

Use web browsing when:
- Information needed is likely to change frequently
- The query explicitly mentions current events
- Internal knowledge is insufficient or outdated

Use code interpreter when:
- Calculations are required
- Data analysis is needed
- Visualization would enhance understanding

User Communication During Tool Use

Keep users informed about tool usage and processes

Example:

Before using a tool:
"I'll need to search the web to find the most current information on this topic."

During tool use:
"I'm analyzing the data you provided. This will take a moment."

After tool use:
"Based on the web search results from [sources], I found that..."

Integrate Knowledge and Tool Outputs

Combine information from different sources coherently

Example:

First present information from internal knowledge:
"According to our documentation, our current process is..."

Then supplement with web information:
"Based on recent industry updates from [source], there are new approaches..."

Finally, provide analysis:
"Analyzing these approaches against your requirements shows that..."

Handle Tool Failures Gracefully

Provide alternative approaches when tools don’t work as expected

Example:

"I tried to search for current information on this topic, but couldn't find specific results. Instead, let me:
1. Share what we know from our internal documentation
2. Suggest some alternative approaches
3. Recommend reliable sources you might check directly"

​
Troubleshooting Common Issues

​
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 workflows

Implementation:

For market analysis requests:
1. First use knowledge retrieval to access internal market research
2. Then use web browsing to gather current market trends
3. Next use code interpreter to analyze combined data
4. Finally use image generation to create visualizations of findings

Tool chaining enables sophisticated multi-step processes that combine different capabilities.

Contextual Tool Configuration

Adapt tool behavior based on user type or request context

Implementation:

When supporting technical users:
- Include more technical detail in responses
- Show code snippets and raw data when relevant
- Use technical terminology in explanations

When supporting business users:
- Focus on insights rather than technical details
- Emphasize visualizations over raw data
- Use business terminology in explanations

Contextual configuration improves relevance for different user groups.

Hybrid Knowledge Approach

Strategically combine internal knowledge with web information

Implementation:

For product information:
- Always use internal knowledge for specifications and features
- Use web browsing only for competitor comparisons
- Supplement with internal competitive analysis when available
- Clearly distinguish between internal and external information

This approach ensures accurate internal information while benefiting from external context.

Progressive Disclosure

Structure responses to present essential information first

Implementation:

When providing analysis results:
1. Start with key findings and recommendations
2. Follow with supporting evidence and context
3. Offer detailed analysis for those who want to explore further
4. Provide visualization options that users can request

Progressive disclosure improves usability by preventing information overload.

​
Enterprise Integration Considerations

​
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

Was this page helpful?

Raise issue