AI agents: architecture patterns, tools, and orchestration

AI agents are autonomous systems that use LLMs to reason, make decisions, and take actions. An agent observes its environment, plans a course of action, executes tool calls, and reflects on results. Agent architecture is one of the fastest-evolving areas in AI engineering.

The core agent loop is: receive a task, reason about it, decide on an action, execute the action using a tool, observe the result, and repeat until the task is complete. This loop replaces hardcoded workflows with dynamic, LLM-driven decision-making.

Tools are the agent's interface to the world. Each tool is a function with a description, parameters, and return value. The LLM decides which tool to use based on the task and the current state. Common tools include web search, code execution, file reading, database queries, and API calls.

Memory gives agents context and continuity. Short-term memory within a session keeps the conversation history. Long-term memory stores information across sessions, typically in a vector database. Agents that can remember past interactions provide much more coherent experiences.

Orchestration frameworks like LangChain, CrewAI, and AutoGen provide abstractions for building agents. They handle the agent loop, tool calling, and memory. Choose a framework that matches your complexity needs. For simple agents, a direct API call with a tool-description prompt may be sufficient.

ReAct (Reasoning and Acting) is the most common agent pattern. The agent outputs a reasoning step, then an action step, then observes the result, and continues. This chain-of-thought approach produces better results than acting without reasoning. The reasoning trace is also useful for debugging.

Planning agents break complex tasks into subtasks and execute them in order. This allows agents to handle long-running, multi-step tasks that would exceed context windows. The plan can be dynamic the agent can adjust based on intermediate results.

Agent reliability is the biggest challenge. Agents can get stuck in loops, make incorrect tool calls, or take actions with unintended consequences. Implement human-in-the-loop approval for high-risk actions. Set maximum iteration limits. Log all agent actions for debugging.

-

Rizwan Saleem | https://rizwansaleem.co