plurai-ai/intellagent

A framework for comprehensive diagnosis and optimization of agents using simulated, realistic synthetic interactions

What it solves

IntellAgent addresses the difficulty of identifying "blind spots" and failure points in conversational AI agents before they are deployed. It prevents unreliable real-world performance by stress-testing agents through the simulation of thousands of realistic, challenging, and edge-case interactions.

How it works

The framework operates in a three-step process:

  1. Scenario Generation: It takes a user prompt and optional data (like tool definitions or database schemas) to create a policy graph. It then samples policies based on real conversation distributions to generate a specific interaction scenario, including necessary system databases.
  2. Simulation: A dedicated user agent simulates the interaction between a human user and the chatbot.
  3. Critique: The system analyzes the conversation and provides feedback on the policies being tested.

Who it’s for

Developers and researchers building conversational AI agents who need to evaluate, stress-test, and optimize their agents' reliability and performance through automated simulation.

Highlights

  • Automated Edge-Case Generation: Creates thousands of realistic scenarios tailored to the specific agent.
  • Diverse Interaction Simulation: Tests agents across various complexity levels.
  • Performance Analytics: Provides detailed analysis to identify gaps and prioritize improvements via a Streamlit dashboard.
  • Broad LLM Support: Integrates with OpenAI, Azure, Vertex, and Anthropic.