AI Agent TechnologyStack

Comprehensive overview of our production-ready AI agent technology stack, featuring the Open Agent Stack (OAS), DACP workflow orchestration, behavioral contracts, and multi-LLM engine support.

Explore Technologies Get Technical Consultation

LLM Engines

100%

Contract Coverage

47+

Framework Tests

85%

Faster Development

Multi-LLM Engine Support

Build agents that work across multiple LLM providers with unified specifications. Choose the best engine for each use case while maintaining consistent behavior.

OpenAI

Supported Models:

GPT-4GPT-4 TurboGPT-3.5 Turbo

Key Features:

Industry-leading performance
Function calling support
High rate limits
Reliable API uptime

Best For:

Production applications requiring high performance and reliability

Anthropic Claude

Supported Models:

Claude 3.5 SonnetClaude 3 OpusClaude 3 Haiku

Key Features:

Large context windows
Strong reasoning capabilities
Constitutional AI training
Safety-focused design

Best For:

Complex analysis tasks requiring deep reasoning and safety

xAI Grok

Supported Models:

Grok-3 LatestGrok-4 (Coming Soon)

Key Features:

Advanced reasoning capabilities
Real-time knowledge integration
Enhanced context understanding
OpenAI-compatible API

Best For:

Complex analysis requiring cutting-edge reasoning and real-time knowledge

Local Models

Supported Models:

Llama 2/3Code LlamaMistralCustom Models

Key Features:

No API costs
Complete data privacy
Offline capability
Custom model support

Best For:

Privacy-sensitive applications and cost-controlled environments

Custom Engines

Supported Models:

Azure OpenAIAWS BedrockGoogle VertexCustom APIs

Key Features:

Enterprise integrations
Custom routing logic
Multiple model ensembles
Vendor-specific features

Best For:

Enterprise environments with specific compliance requirements

Why Multi-Engine Support Matters

Different tasks require different capabilities. Our framework lets you choose the optimal engine for each use case.

Cost Optimization

Use local models for development and less critical tasks to reduce API costs

Privacy Control

Keep sensitive data local with on-premise models or choose privacy-focused providers

Performance Tuning

Match engine capabilities to task requirements for optimal results

Engine Configuration Examples

OpenAI Configuration

intelligence:
  type: "llm"
  engine: "openai"
  model: "gpt-4"
  config:
    temperature: 0.3
    max_tokens: 1000

Claude Configuration

intelligence:
  type: "llm"
  engine: "anthropic"
  model: "claude-3-5-sonnet-20241022"
  config:
    temperature: 0.3
    max_tokens: 1000

Grok Configuration

intelligence:
  type: "llm"
  engine: "grok"
  model: "grok-3-latest"
  config:
    temperature: 0.7
    max_tokens: 1500

Engine Types

20+

Model Options

100%

API Compatibility

Unified Interface

Architecture Overview

Comprehensive architecture supporting the full lifecycle from agent specification to production deployment with enterprise-grade reliability and security.

System Architecture Layers

Agent Specifications

Configuration Layer

YAML-based agent definitions with behavioral contracts and engine configuration

Agent YAMLBehavioral ContractsTask DefinitionsI/O Schemas

Framework Layer

Processing Layer

Open Agent Stack with multi-engine support and automated code generation

OAS FrameworkCode GeneratorEngine AdaptersValidation Layer

Runtime Layer

Execution Layer

DACP orchestration with workflow management and inter-agent communication

DACP RuntimeWorkflow EngineMessage RouterSession Manager

Infrastructure

Deployment Layer

Production deployment with monitoring, scaling, and enterprise integrations

Container RuntimeAPI GatewayMonitoringSecurity Layer

Development & Deployment Flow

Agent Definition

Define agent specifications in YAML with behavioral contracts

Input:Business Requirements

Output:Agent Specification

Code Generation

OAS generates production-ready Python code with API integrations

Input:Agent Specification

Output:Python Agent Code

Workflow Orchestration

DACP coordinates multiple agents with automatic routing

Input:Multiple Agents

Output:Orchestrated Workflow

Production Deployment

Deploy with monitoring, scaling, and enterprise security

Input:Workflow System

Output:Production Service

Enterprise Integration Points

Cloud Platforms

AWS, Azure, GCP integration with managed services and enterprise security

Data Systems

Database integration, data lakes, and real-time analytics platforms

Security & Compliance

SOC2, GDPR, HIPAA compliance with audit trails and encryption

Architecture Performance

99.9%

System Uptime

<100ms

Response Latency

1000+

Concurrent Agents

Auto

Scaling

Open Agent Stack (OAS) Framework

Our extended OAS framework provides a complete solution for building, testing, and deploying AI agents across multiple LLM engines with behavioral contract enforcement.

Agent Specifications

YAML-based agent definitions that describe what agents do, their inputs/outputs, and behavioral requirements.

Declarative agent configuration
Input/output schema validation
Task definition and constraints
Engine-agnostic specifications

Code Generation

Automatic generation of production-ready Python agents with proper error handling and validation.

Production-ready Python code
Proper error handling
API integration layer
Testing framework included

Multi-Engine Support

Support for OpenAI, Anthropic/Claude, Local models, and Custom LLM implementations.

OpenAI GPT-4 integration
Anthropic Claude support
Local model inference
Custom LLM routers

PyPI Distribution

Production-ready package distribution with CI/CD pipeline and comprehensive testing.

Automated PyPI publishing
GitHub Actions CI/CD
Matrix testing (Python 3.10-3.12)
Comprehensive test suite

Example Agent Generation

# Generate OpenAI agent
oas init --spec openai_security_agent.yaml --output my_openai_agent/

# Generate Claude agent  
oas init --spec claude_security_agent.yaml --output my_claude_agent/

# Generate Local agent (no API key needed)
oas init --spec local_agent.yaml --output my_local_agent/

# Install and run
cd my_claude_agent/
pip install -r requirements.txt
python agent.py

LLM Engines Supported

38+

Framework Tests

100%

Production Ready

DACP Workflow Orchestration

Distributed Agent Communication Protocol (DACP) enables sophisticated multi-agent workflows with automatic routing, session management, and real-time coordination.

Agent Orchestration

Coordinate multiple AI agents with automatic message routing and session management.

Event-Driven Architecture

Reactive workflow execution based on events, conditions, and dynamic routing logic.

YAML Configuration

Define complex workflows using simple YAML configuration files with conditional logic.

Production Monitoring

Real-time workflow monitoring with performance metrics and error tracking.

Real-World Example: Security Operations Workflow

3-stage security pipeline with conditional escalation and multi-engine coordination

1. Threat Analysis

Claude Security Agent

Analyzes security events for potential threats and identifies threat types, severity, and indicators

Output: Structured threat assessment with risk indicators

2. Risk Assessment

Claude Risk Agent

Evaluates business impact and generates risk scores (0.0-10.0) with actionable recommendations

Output: Risk score and escalation decision

3. Incident Response

OpenAI Response Agent

Coordinates incident response activities when risk score >= 7.0 or escalation required

Output: Response plan with team assignments

DACP Workflow Configuration

# workflow_runtime.py
from dacp.orchestrator import Orchestrator
from dacp.workflow_runtime import WorkflowRuntime

# Initialize orchestrator
orchestrator = Orchestrator()

# Register security agents
orchestrator.register_agent("threat-analyzer", threat_agent)
orchestrator.register_agent("risk-assessor", risk_agent) 
orchestrator.register_agent("incident-responder", incident_agent)

# Execute workflow with automatic routing
runtime = WorkflowRuntime(orchestrator)
runtime.load_workflow_config("security-workflow.yaml")
workflow_id = runtime.execute_workflow("security_analysis", incident_data)

DACP Benefits

90%

Process Automation

Real-time

Event Processing

Multi-LLM

Engine Coordination

Behavioral Contracts

Enforce safety, reliability, and compliance in AI agent behavior with comprehensive behavioral contracts that work across all LLM engines.

Safety Enforcement

Automatic validation of AI agent responses with harmful content detection and PII protection.

Harmful content filtering
PII and sensitive data protection
Response safety validation
Automatic fallback mechanisms

Temperature Control

Strict temperature ranges for consistent, reliable AI agent behavior in production environments.

Temperature range enforcement [0.1, 0.5]
Consistent response quality
Production stability
Behavioral predictability

Response Validation

Enforce required output fields and data structures with automatic schema validation.

Required field validation
JSON schema enforcement
Data type checking
Confidence level validation

Audit Trails

Complete logging and tracking of all agent interactions with contract compliance monitoring.

Complete interaction logging
Contract compliance tracking
Performance metrics
Error rate monitoring

Security Agent Contract Example

YAML Configuration

behavioural_contract:
  version: "0.1.2"
  description: "Security-focused contract"
  behavioural_flags:
    conservatism: "high"
    temperature_control:
      mode: "strict"
      range: [0.1, 0.5]
  response_contract:
    output_format:
      required_fields: 
        - risk_assessment
        - recommendations
        - confidence_level
    safety_checks:
      harmful_content: true
      pii_protection: true

Python Implementation

from behavioural_contracts import behavioural_contract

@behavioural_contract(
    temperature_control={
        "mode": "strict", 
        "range": [0.1, 0.5]
    },
    response_contract={
        "output_format": {
            "required_fields": [
                "risk_assessment",
                "recommendations", 
                "confidence_level"
            ]
        }
    }
)
def analyze_threat(threat_description: str) -> SecurityOutput:
    # Framework handles validation automatically
    pass

Contract Enforcement Flow

Input Validation

Validate inputs against schema and safety checks

LLM Processing

Execute with temperature control and behavioral flags

Response Validation

Check output format and required fields

Audit Logging

Log compliance metrics and performance data

100%

Contract Coverage

99.9%

Validation Accuracy

Engine Compatibility

Ready to Build with Our Technology Stack?

Leverage our proven technology stack to build production-ready AI agents with enterprise-grade reliability and security.

Start Technical Discussion View Our Services

Technical Deep Dive

Schedule a technical session to explore our architecture and implementation approach

Technology Assessment

Evaluate how our technology stack fits with your existing infrastructure

Proof of Concept

Build a working prototype using our technology stack with your specific requirements

Explore Our Open Source Frameworks

Open Agent Stack DACP Workflows Behavioral Contracts