Prisme.ai’s no-code capabilities empower users across your organization to create sophisticated AI agents without programming experience. This democratizes AI development while maintaining enterprise-grade security, governance, and performance.

​
Benefits of No-Code AI Development

Wider Access

Empower subject-matter experts to create AI solutions directly

Faster Deployment

Reduce development time from weeks to hours

Reduced Costs

Eliminate dependency on specialized AI development resources

Domain Expertise

Enable those with business knowledge to shape AI behavior

Experimentation

Facilitate rapid prototyping and iteration

Governance

Maintain control with centralized oversight

​
No-Code Agent Types

Prisme.ai supports several types of no-code agents, each with distinct capabilities:

Basic agents powered by foundation models with specialized instructions.

Key Features:

  • Custom system instructions
  • Persona definition
  • Response formatting
  • Specialized knowledge embedding

Best For:

  • Question answering
  • Content generation
  • Simple conversational assistants
  • Standard processes with clear instructions

Learn more about Simple Prompting Agents →

​
Available Built-in Tools

No-code agents can leverage several built-in tools without any programming:

Web Browsing

Access current information from the internet using Serper integration

Image Generation

Create images based on text descriptions

Code Interpreter

Run code to perform calculations, data analysis, and visualizations

PDF Analysis

Extract, summarize, and analyze information from PDF documents

Document Generation

Create formatted documents, reports, and presentations

Data Visualization

Generate charts and graphs from data

​
No-Code Development Process

Creating effective no-code agents involves several key steps:

1

Define Purpose and Requirements

Clearly articulate what your agent needs to accomplish.

Key questions to answer:

  • What specific problem will this agent solve?
  • Who are the intended users?
  • What information does it need access to?
  • What actions should it be able to perform?
  • How will success be measured?
2

Select Agent Type

Choose the appropriate agent architecture based on your needs.

Selection criteria:

  • Simple Prompting for basic interactions
  • RAG for knowledge-intensive applications
  • RAG with Built-in Tools for enhanced capabilities
3

Prepare Knowledge Resources

Gather and organize the information your agent needs.

For RAG agents:

  • Collect relevant documents
  • Organize content logically
  • Update outdated information
  • Remove sensitive or irrelevant content
4

Configure Agent Behavior

Define how your agent will interact with users.

Key configurations:

  • System instructions
  • Response style and format
  • Tool selection and usage criteria
  • Knowledge retrieval settings
5

Test and Refine

Validate performance and iteratively improve.

Testing approaches:

  • Sample conversations
  • Edge case scenarios
  • User feedback collection
  • Performance monitoring
6

Deploy and Monitor

Make your agent available to users and track its effectiveness.

Deployment considerations:

  • Access controls
  • Usage monitoring
  • Feedback mechanisms
  • Improvement workflows

​
Creating Agents with AI Store

Prisme.ai’s AI Store provides a user-friendly interface for creating no-code agents:

​
Key Features

Visual Agent Builder

Intuitive interface for agent configuration without coding

Template Library

Pre-built templates for common use cases

Knowledge Integration

Direct connection to organizational knowledge bases

Tool Configuration

Simple setup for built-in tool access

Testing Environment

Dedicated space to validate agent performance

Versioning

Track changes and manage agent versions

​
AI Store Workflow

1

Create New Agent

Start with a blank slate or select from templates.

Options include:

  • Blank agent
  • Industry-specific templates
  • Function-based templates
  • Duplicating existing agents
2

Define Basic Information

Set the agent’s identity and purpose.

Key information:

  • Agent name
  • Description
  • Category/tags
  • Access permissions
3

Configure Capabilities

Select and customize the agent’s features.

Available options:

  • Foundation model selection
  • Knowledge base connections
  • Built-in tool activation
  • Response parameters
4

Set System Instructions

Define the agent’s behavior through prompting.

Instruction components:

  • Role definition
  • Response guidelines
  • Knowledge utilization
  • Tool usage criteria
  • Limitations and constraints
5

Test and Preview

Validate agent performance in the testing environment.

Testing capabilities:

  • Interactive conversation
  • Sample queries
  • Tool execution preview
  • Knowledge retrieval verification
6

Publish and Share

Make the agent available to intended users.

Sharing options:

  • Organization-wide publication
  • Team-specific access
  • Individual user permissions
  • External link sharing (if permitted)

​
Best Practices for No-Code Agents

​
Common Challenges and Solutions

ChallengeDescriptionSolution
Inconsistent ResponsesAgent provides varying answers to similar questions
  • Refine system instructions with more specific guidance
  • Add example responses for common scenarios
  • Lower the temperature setting for more predictability
  • Create response templates for key question types
Poor Knowledge RetrievalAgent fails to find or use relevant information
  • Improve document chunking strategy
  • Enhance metadata for better filtering
  • Adjust retrieval settings (more results, higher relevance threshold)
  • Reorganize knowledge base structure
  • Add query reformulation instructions
Inappropriate Tool UsageAgent uses tools unnecessarily or incorrectly
  • Provide explicit criteria for when to use each tool
  • Include examples of appropriate tool usage
  • Add constraints for tool selection
  • Create step-by-step instructions for complex tools
  • Test with diverse scenarios to verify behavior
Scope ConfusionAgent struggles with determining boundaries of its role
  • Define clear in-scope and out-of-scope topics
  • Provide explicit handling instructions for edge cases
  • Include examples of appropriate redirects or escalations
  • Create specific guidance for handling ambiguous requests
  • Test boundary conditions regularly
User Adoption ResistanceEnd users hesitant to engage with AI agents
  • Create a transparent introduction explaining the agent’s purpose and limitations
  • Provide clear examples of how the agent can help
  • Establish easy escalation to human support when needed
  • Collect and address feedback proactively
  • Show concrete value through time savings or improved information access

​
Enterprise Governance Considerations

Access Control

Implement appropriate permissions for agent creation and usage

Key practices:

  • Role-based access for agent creation
  • Approval workflows for production deployment
  • User group definitions for agent access
  • Audit logs for agent modifications

Content Compliance

Ensure agents adhere to organizational policies

Key practices:

  • Pre-deployment review process
  • Compliance checklist verification
  • Sensitive information handling guidelines
  • Regular content audits

Performance Monitoring

Track agent effectiveness and usage

Key practices:

  • Usage metrics dashboard
  • User satisfaction tracking
  • Response quality evaluation
  • System performance monitoring

Information Security

Protect sensitive data in agent interactions

Key practices:

  • Data handling guidelines
  • PII protection measures
  • Conversation logging policies
  • Retention and purging schedules

​
Next Steps

Ready to create your first no-code agent? Explore these resources:

Simple Prompting Agent

Create basic agents with custom instructions

RAG Agent

Build knowledge-powered agents

RAG with Built-in Tools

Develop advanced agents with enhanced capabilities

AI Store Guide

Learn more about the AI Store platform

Was this page helpful?

Raise issue