The shift from traditional software to agent-augmented systems requires rethinking architecture patterns. How do AI agents integrate with your existing services? Where do they run? How are they coordinated? What happens when they fail?
I help organizations design robust architectures that leverage Claude, GPT, Gemini, and emerging platforms. This includes API integration patterns, context management strategies, error handling, cost optimization, and security considerations specific to AI agent deployments.
We analyze your business processes and data landscape to identify high-value opportunities for generative AI implementation, focusing on areas with the greatest potential ROI and transformative impact.
We guide you through the ecosystem of generative AI models, platforms, and tools, helping you select the optimal combination of technologies based on your specific requirements, constraints, and long-term objectives.
We create comprehensive architectural blueprints that outline how generative AI systems will integrate with your existing infrastructure, detailing data flows, API connections, security measures, and scalability considerations.
We develop phased implementation plans that prioritize quick wins while building toward your long-term vision, ensuring each stage delivers tangible value while establishing the foundation for future capabilities.
• Multi-agent orchestration architectures
• Context window management strategies
• Agent-to-agent communication protocols
• Fallback and error recovery patterns
• Claude API / OpenAI / Gemini integration
• Hybrid cloud and on-premise deployment
• RAG and vector database architecture
• Cost optimization and rate limiting
• CI/CD pipeline agent integration
• Code review automation architecture
• Testing agent deployment
• Documentation generation systems
• Data sanitization gateways
• Audit logging for AI interactions
• Access control for agent capabilities
• Compliance boundary architecture