AI Agent Architectures for Coding

AI coding agents have evolved from simple autocomplete to autonomous systems that can plan, implement, test, and debug entire features. Understanding their architectures helps you choose the right tool and use it effectively.

The Tool-Calling Agent

The simplest agent architecture is a language model with access to tools: file read/write, terminal execution, web search, and code analysis. The model receives a task, decides which tools to call, observes the results, and iterates until the task is complete. Claude Code, aider, and most AI coding assistants use this pattern. It works well for tasks that can be decomposed into sequential steps.

The Planning Agent

Planning agents add an explicit planning phase before execution. They break the task into subtasks, estimate difficulty, identify dependencies, and create an execution plan. The plan is then executed step by step, with re-planning triggered when unexpected results occur. This architecture handles complex, multi-file tasks better than pure reactive agents.

Multi-Agent Systems

Multi-agent architectures use specialized agents that collaborate. A planner agent decomposes the task, a coder agent writes the implementation, a reviewer agent checks for issues, and a tester agent validates correctness. Each agent can use different models optimized for their role — a fast, cheap model for planning and a capable, expensive model for complex code generation.

Memory and Context Management

The biggest challenge for coding agents is context management. A real codebase has thousands of files, but models have finite context windows. Agents use retrieval (vector search over the codebase), summarization (compressed representations of code structure), and selective attention (reading only the files relevant to the current task) to work within context limits.

Where This Is Heading

The frontier is continuous integration agents that monitor your repository, understand your codebase, and autonomously handle routine tasks: updating dependencies, fixing CI failures, addressing code review feedback, and even implementing simple feature requests from issue trackers. The MCP protocol is the enabling infrastructure, providing standardized tool access that agents need to interact with development infrastructure.