@UnTalNixon_exe: The Definitive Map of AI Agents: 35 Agentic Architectures with Comparative Benchmarks Building an AI agent isn’t just a…

The Definitive Map of AI Agents: 35 Agentic Architectures with Comparative Benchmarks Building an AI agent isn’t just about firing off a prompt and hoping for the best. The real challenge in production is choosing the right control structure to avoid infinite loops, hallucinations, and massive token consumption. The all-agentic-architectures repository systematically gathers and breaks down 35 AI agent design patterns, evaluating the performance, latency, and success rate of each one through clear metrics. . The key architectures included in the analysis: • Core Reasoning Patterns: Detailed implementations of ReAct (Reasoning and Action), Plan-and-Solve, and Chain-of-Thought (CoT) to analyze how they structure thought before executing tools. • Multi-Agent Structures: Breakdown of systems based on Orchestrator-Worker, multi-agent debates, and hierarchical voting flows to solve complex tasks in parallel. • Autonomous Refinement Loops: Analysis of Self-Correction and Self-Reflection architectures, where the agent evaluates its own output against a criterion before delivering the final result. • Evaluation and Benchmarks: Direct comparisons of token costs, response times, and accuracy in problem-solving based on task type (code, data analysis, or logic). . Stop improvising with your agents’ logic and start designing based on established engineering patterns—that’s what separates a toy prototype from a production-ready system. I’ll leave the link to the official repository in the comments section. Bookmark this post to check which architecture best fits your next AI system before writing your first line of code