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Core Concepts Overview

Understanding the fundamental concepts behind CodeOptiX.

Agentic Code Optimization & Deep Evaluation for Superior Coding Agent Experience. CodeOptiX is the universal code optimization engine that improves coding agent experience with deep evaluations and optimization. When AI coding agents dazzle with impressive code but leave you wondering about quality, maintainability, security, and reliability, CodeOptiX ensures proper behavior through evaluations, reflection, and self-improvement.

What is CodeOptiX?

CodeOptiX is an advanced evaluation and optimization platform for AI coding agents. It provides comprehensive testing, analysis, and improvement capabilities to ensure your coding agents produce high-quality, reliable code.

Key Capabilities

  • 🔍 Deep Evaluation - Comprehensive behavioral testing of coding agents
  • 📊 Detailed Analysis - In-depth performance metrics and issue identification
  • 🧠 Smart Optimization - GEPA-powered prompt evolution and improvement
  • 🎯 Quality Assurance - Automated testing against security, reliability, and correctness behaviors

The Workflow

CodeOptiX follows a simple workflow:

graph LR
    A[Agent] --> B[Evaluation]
    B --> C[Reflection]
    C --> D[Evolution]
    D --> A

1. Evaluation

Test your agent against behavior specifications:

results = engine.evaluate_behaviors(
    behavior_names=["insecure-code", "vacuous-tests"]
)

2. Reflection

Understand why the agent behaved the way it did:

reflection = reflection_engine.reflect(results)

3. Evolution

Automatically improve agent prompts:

evolved = evolution_engine.evolve(results, reflection)

Key Components

Agent Adapters

What: Connect CodeOptiX to your coding agent

Why: CodeOptiX works with any agent through adapters

Example: 

adapter = create_adapter("codex", config)

Behavior Specifications

What: Define what behaviors to evaluate

Why: Modular, reusable behavior definitions

Example: 

behavior = create_behavior("insecure-code")
result = behavior.evaluate(agent_output)

Evaluation Engine

What: Orchestrates the evaluation process

Why: Handles scenario generation, execution, and scoring

Example: 

engine = EvaluationEngine(adapter, llm_client)
results = engine.evaluate_behaviors(["insecure-code"])

Reflection Engine

What: Analyzes evaluation results

Why: Provides insights and recommendations

Example: 

reflection = reflection_engine.reflect(results)

Evolution Engine

What: Optimizes agent prompts

Why: Automatically improves agent behavior

Example: 

evolved = evolution_engine.evolve(results, reflection)

ACP Integration

What: Agent Client Protocol integration for editor support

Why: Connect CodeOptiX to editors and orchestrate multiple agents

Example: 

from codeoptix.acp import ACPQualityBridge

bridge = ACPQualityBridge(agent_command=["python", "agent.py"], auto_eval=True)
await bridge.connect()

How It Works

Step 1: Scenario Generation

CodeOptiX generates test scenarios:

scenarios = generator.generate_scenarios(
    behavior_name="insecure-code",
    behavior_description="Detect insecure code"
)

Step 2: Agent Execution

Your agent runs on each scenario:

agent_output = adapter.execute(scenario["prompt"])

Step 3: Evaluation

CodeOptiX evaluates the output:

result = behavior.evaluate(agent_output)

Step 4: Aggregation

Results are aggregated:

overall_score = sum(scores) / len(scores)

Architecture

┌─────────────────────────────────────────┐
│           CodeOptiX Core                 │
├─────────────────────────────────────────┤
│  ┌──────────┐  ┌──────────┐  ┌────────┐│
│  │  Agent   │  │ Behavior │  │ Eval   ││
│  │ Adapters │  │   Specs  │  │ Engine ││
│  └──────────┘  └──────────┘  └────────┘│
│  ┌──────────┐  ┌──────────┐  ┌────────┐│
│  │Reflection│  │Evolution │  │Artifact││
│  │ Engine   │  │  Engine  │  │Manager ││
│  └──────────┘  └──────────┘  └────────┘│
└─────────────────────────────────────────┘

Next Steps

Learn more about each component: