AI agents: architecture patterns, tools, and orchestration

Dev.to / 6/6/2026

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Key Points

AI agents are autonomous systems that use LLMs to observe their environment, plan actions, call tools, and iteratively repeat reasoning and execution until the task is complete.
The core agent loop (task → reason → decide → tool execution → observe → repeat) replaces rigid, hardcoded workflows with dynamic, LLM-driven decision-making.
Tools act as the agent’s interface to the outside world (e.g., web search, code execution, file/database access, and API calls), with the LLM selecting tools based on context.
Memory and orchestration frameworks are key building blocks: short-term session context and long-term storage (often via vector databases) improve continuity, while frameworks like LangChain, CrewAI, and AutoGen simplify loop/tool/memory management.
Common architecture patterns include ReAct and planning agents, but reliability is the biggest challenge, requiring safeguards such as human-in-the-loop approvals, iteration limits, and comprehensive logging for debugging.

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