AI agent architectures are evolving rapidly, driven by the need to balance efficiency, scalability, and reliability in real-world applications. Three core models have emerged: Monolithic Single Agents with Tools, Agentic Workflows, and LLM-based Skills. Each offers distinct advantages depending on task complexity, cost constraints, and system requirements. Monolithic Single Agents with Tools feature a single powerful LLM acting as the central decision-maker, augmented with external tools like web search or code execution. These systems excel in rapid prototyping and simple, sequential tasks—especially when limited to 10–20 tools. They deliver faster execution and lower latency due to reduced communication overhead. However, their performance degrades sharply as the number of available tools grows beyond 50–100, due to semantic confusion and cognitive overload in skill selection. Agentic Workflows represent a hybrid approach, using multiple lightweight, specialized agents arranged in a directed graph. Each agent handles a specific subtask—such as planning, execution, or critique—enabling parallel processing and better error isolation. Frameworks like LangGraph and OpenAI’s AgentKit support visual composition, conditional logic, and debugging. These systems are ideal for enterprise-grade production, offering predictability, cost control through smaller models per node, and robustness in complex workflows. LLM Skills introduce a new paradigm where a core large language model dynamically loads reusable, modular capabilities—like templates, scripts, or instruction sets—based on task needs. Anthropic’s implementation exemplifies this, using schema-bound skills that combine tool-like behavior with agent-like reasoning. Skills are selected via semantic descriptors, executed under defined policies, and can run internally or externally. This approach enables universal agents capable of handling diverse tasks, especially in coding and open-ended problem-solving, while maintaining low context usage when idle. Recent research shows that compiling multi-agent systems into Single-Agent with Skills (SAS) architectures yields significant gains: 54% reduction in token usage and 50% lower latency, with accuracy preserved or slightly improved. This is due to unified context and eliminated inter-agent communication. However, skill selection accuracy drops sharply beyond 50–100 skills, not because of size alone, but due to semantic overlap and confusion—mirroring human cognitive limits. To address this, hierarchical routing organizes skills into coarse categories (e.g., math, retrieval, coding) before fine-grained selection. This method restores accuracy by up to 40% in large libraries, aligning with how humans manage complex decisions through chunking. The broader implications highlight a clear trend: large language models thrive in open-ended, flexible scenarios, while small language models (SLMs) offer speed and efficiency in narrow, well-defined tasks. Multi-agent setups boost breadth-first exploration by 90%, but SLMs can match them in specialized domains at lower cost. For practitioners, the key is to assess task decomposability, baseline difficulty, and required reliability. In production environments, success depends on verification loops, domain constraints, and seamless human handoff mechanisms to prevent infinite loops or output variance. In 2025–2026, hybrids—especially Agentic Workflows enhanced with hierarchical skill routing—dominate. They combine orchestration, modularity, and dynamic capability selection, offering the best balance of control, scalability, and efficiency. The future of AI agents lies not in choosing one architecture, but in intelligently combining them to meet the demands of real-world complexity.