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In Day 15 of the Engineering AI Agents series, we upgrade our AI system with performance optimization techniques used in real production AI platforms. As AI agents grow more complex with multiple agents, tools, and LLM calls, performance becomes critical. Without optimization, systems suffer from slow responses, high token costs, and scalability issues. In this episode we introduce a complete AI agent performance architecture that includes: ✔ Prompt caching to avoid repeated LLM calls ✔ Parallel agent execution with controlled concurrency ✔ Streaming responses from the LLM ✔ Token optimization to reduce LLM cost and latency We also implement an Agent Runtime architecture that includes: • Task Planner • Agent Router • Parallel Executor • Prompt Cache • Result Aggregator This architecture is similar to the orchestration used in modern AI frameworks like LangChain, CrewAI, and Azure AI Foundry. What You Will Learn • How to optimize AI agent performance • Running multiple AI agents efficiently • Reducing LLM token usage • Implementing prompt caching • Parallel execution strategies for local LLMs • Designing production-grade AI systems Tech Stack .NET 8 C# Semantic Kernel Ollama (Local LLM) Engineering AI Agents Series Day 0 – Agentic AI Setup (.NET + Ollama + Semantic Kernel) Day 1 – Code Understanding Agent Day 2 – Code Review Agent Day 3 – Memory-Based Review Day 4 – Rule-Based Severity Engine Day 5 – CI/CD Automation Agent Day 6 – Repo-Wide Review Day 7 – Test Case Generator Agent Day 8 – DevOps Failure Analysis Agent Day 9 – Multi-Agent Systems Day 10 – Supervisor Pattern Day 11 – Agent-to-Agent Communication Day 12 – Tool Plugins Day 12B – Strict Function Calling Day 12C – Intelligent Tool Arbitration Day 13 – Structured Output Systems Day 14 – Observability & Telemetry Day 15 – AI Agent Performance Optimization #aiengineering #semantickernel #aioptimization #dotnetai #aiagents #agenticai #aiagents #dotnetai #semantickernel #ollama #aiengineering #genai #llm #softwareengineering #aiarchitecture