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Agentic Intelligence

Developer guide for configuring and using Agentic Intelligence—multi-agent coordination for intelligent strategy selection.

Overview

Agentic Intelligence uses a multi-agent system where research agents, validation engineers, and performance analysts collaborate to select the optimal optimization strategy for your problem. The system requires consensus (default 67%) before a strategy is approved.

How It Works

  1. 1.Research Agent analyzes your problem and suggests strategies based on literature and best practices
  2. 2.Validation Engineer tests strategies against constraints and risk models
  3. 3.Performance Analyst reviews historical data to predict performance
  4. 4.Consensus Engine requires agreement before strategy is approved

Simple Configuration

Enable Agentic Intelligence with a simple boolean flag:

Enable Agentic Intelligence
from sematryx import Sematryx

client = Sematryx(api_key="sk-your-api-key")

# Enable Agentic Intelligence
result = client.optimize(
    objective="minimize",
    variables=[{"name": "x", "bounds": (-5, 5)}, {"name": "y", "bounds": (-5, 5)}],
    objective_function=sphere,
    use_agentic_intelligence=True
)

Advanced Configuration

Fine-tune Agentic Intelligence behavior with advanced options:

Advanced Agentic Configuration
from sematryx import Sematryx

client = Sematryx(api_key="sk-your-api-key")

# Advanced configuration: AI reasoning + automatic strategy selection
result = client.optimize(
    objective="minimize",
    variables=[{"name": "x", "bounds": (-5, 5)}, {"name": "y", "bounds": (-5, 5)}],
    objective_function=sphere,
    intelligence_config={
        "use_agentic_intelligence": True,
        "use_ai_reasoning": True,   # Enable AI-driven strategy selection
        "strategy": "auto"          # Let the system choose the best strategy
    }
)

Configuration Options

  • use_agentic_intelligence (bool, default: false)

    Enable AI-assisted strategy selection for your optimization problem.

  • use_ai_reasoning (bool, default: false)

    Enable deeper AI reasoning about problem structure to guide strategy selection.

  • strategy (string, default: "auto")

    Set to "auto" to let the system choose the best strategy, or specify a strategy name directly.

REST API Configuration

Configure Agentic Intelligence via REST API:

REST API - Agentic Intelligence
curl -X POST https://api.sematryx.com/v1/optimize \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "objective_function": "sphere",
    "variables": ["x", "y"],
    "bounds": [[-10, 10], [-10, 10]],
    "max_evaluations": 2000,
    "strategy": "auto",
    "use_ai_reasoning": true
  }'

JavaScript SDK Configuration

Configure Agentic Intelligence using the JavaScript SDK:

JavaScript SDK - Agentic Intelligence
// REST API via fetch (JavaScript SDK coming soon)
const response = await fetch('https://api.sematryx.com/v1/optimize', {
  method: 'POST',
  headers: {
    'Authorization': `Bearer ${apiKey}`,
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    objective_function: 'sphere',
    variables: ['x', 'y'],
    bounds: [[-5, 5], [-5, 5]],
    max_evaluations: 2000,
    strategy: 'auto',
    use_ai_reasoning: true
  })
})
const result = await response.json()
console.log(result.optimal_value, result.optimal_solution)

Best Practices

  • •Use for complex problems: Agentic Intelligence is most valuable when you're unsure which algorithm to use or when problems have unusual characteristics.
  • •Use strategy=auto: Let the system select the best algorithm for your problem type—it uses learned heuristics from thousands of problems.
  • •Enable for complex problems: Agentic Intelligence is most valuable for high-dimensional or multimodal problems where manual strategy selection is difficult.
  • •Combine with private learning: Use read_from_private: True to benefit from your organization's historical optimization data alongside AI reasoning.