Qualifire AI Open-Sources Rogue: An Finish-to-Finish Agentic AI Testing Framework Designed to Consider the Efficiency, Compliance, and Reliability of AI Brokers

Agentic methods are stochastic, context-dependent, and policy-bounded. Standard QA—unit checks, static prompts, or scalar “LLM-as-a-judge” scores—fails to reveal multi-turn vulnerabilities and supplies weak audit trails. Developer groups want protocol-accurate conversations, specific coverage checks, and machine-readable proof that may gate releases with confidence.

Qualifire AI has open-sourced Rogue, a Python framework that evaluates AI brokers over the Agent-to-Agent (A2A) protocol. Rogue converts enterprise insurance policies into executable eventualities, drives multi-turn interactions in opposition to a goal agent, and outputs deterministic studies appropriate for CI/CD and compliance critiques.

Fast Begin

Stipulations

• uvx – If not put in, observe uv set up information
• Python 3.10+
• An API key for an LLM supplier (e.g., OpenAI, Google, Anthropic).

Set up

Choice 1: Fast Set up (Really useful)

Use our automated set up script to rise up and working shortly:

# TUI
uvx rogue-ai
# Internet UI
uvx rogue-ai ui
# CLI / CI/CD
uvx rogue-ai cli

Choice 2: Guide Set up

(a) Clone the repository:

git clone https://github.com/qualifire-dev/rogue.git
cd rogue

(b) Set up dependencies:

If you’re utilizing uv:

Or, in case you are utilizing pip:

(c) OPTIONALLY: Arrange your atmosphere variables: Create a .env file within the root listing and add your API keys. Rogue makes use of LiteLLM, so you may set keys for numerous suppliers.

OPENAI_API_KEY="sk-..."
ANTHROPIC_API_KEY="sk-..."
GOOGLE_API_KEY="..."

Working Rogue

Rogue operates on a client-server structure the place the core analysis logic runs in a backend server, and numerous purchasers connect with it for various interfaces.

Default Habits

While you run uvx rogue-ai with none mode specified, it:

1. Begins the Rogue server within the background
2. Launches the TUI (Terminal Person Interface) shopper

Obtainable Modes

• Default (Server + TUI): uvx rogue-ai – Begins server in background + TUI shopper
• Server: uvx rogue-ai server – Runs solely the backend server
• TUI: uvx rogue-ai tui – Runs solely the TUI shopper (requires server working)
• Internet UI: uvx rogue-ai ui – Runs solely the Gradio net interface shopper (requires server working)
• CLI: uvx rogue-ai cli – Runs non-interactive command-line analysis (requires server working, best for CI/CD)

Mode Arguments

Server Mode

uvx rogue-ai server [OPTIONS]

Choices:

• –host HOST – Host to run the server on (default: 127.0.0.1 or HOST env var)
• –port PORT – Port to run the server on (default: 8000 or PORT env var)
• –debug – Allow debug logging

TUI Mode

uvx rogue-ai tui [OPTIONS]
Internet UI Mode
uvx rogue-ai ui [OPTIONS]

Choices:

• –rogue-server-url URL – Rogue server URL (default: http://localhost:8000)
• –port PORT – Port to run the UI on
• –workdir WORKDIR – Working listing (default: ./.rogue)
• –debug – Allow debug logging

Instance: Testing the T-Shirt Retailer Agent

This repository features a easy instance agent that sells T-shirts. You need to use it to see Rogue in motion.

Set up instance dependencies:

If you’re utilizing uv:

or, in case you are utilizing pip:

pip set up -e .[examples]

(a) Begin the instance agent server in a separate terminal:

If you’re utilizing uv:

uv run examples/tshirt_store_agent

If not:

python examples/tshirt_store_agent

This may begin the agent on http://localhost:10001.

(b) Configure Rogue within the UI to level to the instance agent:

• Agent URL: http://localhost:10001
• Authentication: no-auth

(c) Run the analysis and watch Rogue check the T-Shirt agent’s insurance policies!

You need to use both the TUI (uvx rogue-ai) or Internet UI (uvx rogue-ai ui) mode.

The place Rogue Matches: Sensible Use Circumstances

• Security & Compliance Hardening: Validate PII/PHI dealing with, refusal habits, secret-leak prevention, and regulated-domain insurance policies with transcript-anchored proof.
• E-Commerce & Help Brokers: Implement OTP-gated reductions, refund guidelines, SLA-aware escalation, and tool-use correctness (order lookup, ticketing) beneath adversarial and failure circumstances.
• Developer/DevOps Brokers: Assess code-mod and CLI copilots for workspace confinement, rollback semantics, rate-limit/backoff habits, and unsafe command prevention.
• Multi-Agent Programs: Confirm planner↔executor contracts, functionality negotiation, and schema conformance over A2A; consider interoperability throughout heterogeneous frameworks.
• Regression & Drift Monitoring: Nightly suites in opposition to new mannequin variations or immediate adjustments; detect behavioral drift and implement policy-critical go standards earlier than launch.

What Precisely Is Rogue—and Why Ought to Agent Dev Groups Care?

Rogue is an end-to-end testing framework designed to judge the efficiency, compliance, and reliability of AI brokers. Rogue synthesizes enterprise context and threat into structured checks with clear goals, techniques and success standards. The EvaluatorAgent runs protocol right conversations in quick single flip or deep multi flip adversarial modes. Carry your personal mannequin, or let Rogue use Qualifire’s bespoke SLM judges to drive the checks. Streaming observability and deterministic artifacts: reside transcripts,go/fail verdicts, rationales tied to transcript spans, timing and mannequin/model lineage.

Below the Hood: How Rogue Is Constructed

Rogue operates on a client-server structure:

• Rogue Server: Incorporates the core analysis logic
• Shopper Interfaces: A number of interfaces that connect with the server:
  • TUI (Terminal UI): Fashionable terminal interface constructed with Go and Bubble Tea
  • Internet UI: Gradio-based net interface
  • CLI: Command-line interface for automated analysis and CI/CD

This structure permits for versatile deployment and utilization patterns, the place the server can run independently and a number of purchasers can connect with it concurrently.

Abstract

Rogue helps developer groups check agent habits the best way it truly runs in manufacturing. It turns written insurance policies into concrete eventualities, workout routines these eventualities over A2A, and information what occurred with transcripts you may audit. The result’s a transparent, repeatable sign you should use in CI/CD to catch coverage breaks and regressions earlier than they ship.

Because of the Qualifire group for the thought management/ Sources for this text. Qualifire group has supported this content material/article.

Asif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is dedicated to harnessing the potential of Synthetic Intelligence for social good. His most up-to-date endeavor is the launch of an Synthetic Intelligence Media Platform, Marktechpost, which stands out for its in-depth protection of machine studying and deep studying information that’s each technically sound and simply comprehensible by a large viewers. The platform boasts of over 2 million month-to-month views, illustrating its reputation amongst audiences.