Overview - ARTEMIS Agents
Author: Subhadip Mitra
Description: Adaptive Reasoning Through Evaluation of Multi-agent Intelligent Systems

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# Core Concepts Overview

ARTEMIS implements a structured approach to multi-agent debates based on three core innovations from the research paper.

## The ARTEMIS Architecture

    ┌────────────────────────────────────────────────────────────────┐
    │                        ARTEMIS Core                            │
    ├────────────────────────────────────────────────────────────────┤
    │  ┌─────────────┐  ┌─────────────┐  ┌─────────────┐             │
    │  │   H-L-DAG   │  │   L-AE-CR   │  │    Jury     │             │
    │  │  Argument   │──│  Adaptive   │──│   Scoring   │             │
    │  │ Generation  │  │ Evaluation  │  │  Mechanism  │             │
    │  └─────────────┘  └─────────────┘  └─────────────┘             │
    │         │                │                │                    │
    │         └────────────────┴────────────────┘                    │
    │                          │                                     │
    │  ┌─────────────────────────────────────────────────────────┐   │
    │  │                   Safety Layer                          │   │
    │  │  ┌───────────┐  ┌──────────┐  ┌──────────┐  ┌─────────┐ │   │
    │  │  │Sandbagging│  │Deception │  │ Behavior │  │ Ethics  │ │   │
    │  │  │ Detector  │  │ Monitor  │  │ Tracker  │  │ Guard   │ │   │
    │  │  └───────────┘  └──────────┘  └──────────┘  └─────────┘ │   │
    │  └─────────────────────────────────────────────────────────┘   │
    └────────────────────────────────────────────────────────────────┘

## Key Innovations

### 1. H-L-DAG: Hierarchical Argument Generation

Unlike simple chat-based exchanges, ARTEMIS generates arguments at three levels:

- Strategic Level: High-level thesis and position
- Tactical Level: Supporting points and evidence chains
- Operational Level: Specific facts, quotes, and examples

This hierarchical approach ensures arguments are well-structured and comprehensive.

Learn more about H-L-DAG →

### 2. L-AE-CR: Adaptive Evaluation with Causal Reasoning

Traditional evaluation uses fixed criteria. ARTEMIS dynamically adjusts evaluation based on:

- Topic Domain: Technical topics weight evidence differently than ethical ones
- Round Context: Opening arguments vs. rebuttals have different expectations
- Causal Relationships: Arguments with strong causal chains score higher

Learn more about L-AE-CR →

### 3. Jury Mechanism

Instead of a single evaluator, ARTEMIS uses a multi-perspective jury:

- Multiple jury members with different perspectives
- Deliberation process for consensus building
- Transparent verdict with confidence scores

Learn more about the Jury →

## Debate Flow

A typical ARTEMIS debate follows this flow:

    graph TD
        A[Topic Announced] --> B[Opening Statements]
        B --> C[Round 1: Arguments]
        C --> D[Evaluation]
        D --> E{More Rounds?}
        E -->|Yes| F[Round N: Rebuttals]
        F --> D
        E -->|No| G[Jury Deliberation]
        G --> H[Verdict]

### Phases

1. Initialization

   - Topic is set
   - Agents are assigned positions
   - Jury is configured

2. Opening Statements

   - Each agent presents their initial position
   - No rebuttals yet

3. Argumentation Rounds

   - Agents take turns presenting arguments
   - Each argument can include rebuttals
   - Arguments are evaluated after each turn

4. Jury Deliberation

   - All arguments are considered
   - Jury members vote
   - Consensus is reached

5. Verdict

   - Winner is declared (or tie)
   - Confidence score provided
   - Reasoning explained

## Core Components

### Agent

The `Agent` class represents a debate participant:

    from artemis.core.agent import Agent

    agent = Agent(
        name="analyst",
        role="Domain expert analyzing the topic",
        model="gpt-4o",
    )

### Debate

The `Debate` class orchestrates the entire debate:

    from artemis.core.debate import Debate

    debate = Debate(
        topic="Your debate topic",
        agents=[agent1, agent2],
        rounds=3,
    )

    result = await debate.run()

### Argument

Arguments are structured data with hierarchy:

    from artemis.core.types import Argument, ArgumentLevel

    argument = Argument(
        agent="pro_agent",
        content="The main argument text...",
        level=ArgumentLevel.STRATEGIC,
        evidence=[...],
        causal_links=[...],
    )

### Verdict

The final verdict includes:

    result.verdict.decision    # "pro", "con", or "tie"
    result.verdict.confidence  # 0.0 to 1.0
    result.verdict.reasoning   # Explanation

## Ethical Considerations

ARTEMIS includes built-in ethical considerations at every stage:

- Generation: Arguments are filtered for ethical content
- Evaluation: Ethical criteria are weighted appropriately
- Monitoring: Ethics guard detects boundary violations

Learn more about Ethics →

## Next Steps

Dive deeper into each core concept:

- H-L-DAG Argument Generation
- L-AE-CR Adaptive Evaluation
- Jury Mechanism
- Ethics Module

Or explore practical applications:

- Safety Monitoring
- Framework Integrations
- Examples