What Are AI Agents?
An AI agent is an LLM that can use tools and take actions autonomously. Instead of just generating text, agents execute code, read files, search the web, and iterate on their results. They're the bridge between AI assistants (answer questions) and AI automation (complete tasks).
Agent Architecture
The core loop: Observe (read current state), Think (LLM reasons about next step), Act (execute a tool), Evaluate (check results). Tools include: file read/write, terminal execution, web search, API calls, and database queries. The LLM decides which tool to use and what arguments to pass.
Coding Agents in Practice
Coding agents can: implement features from issue descriptions, fix failing tests by reading errors and modifying code, refactor codebases following patterns, and generate documentation. GitHub Copilot Workspace and Devin are productized coding agents. Open-source alternatives like SWE-agent and Aider bring similar capabilities.
Agent Frameworks
LangChain/LangGraph: flexible agent graphs with tool integration. CrewAI: multi-agent collaboration (one agent writes, another reviews). AutoGen (Microsoft): conversational agents that work together. Custom: for simple agents, a loop with an LLM API and subprocess calls is often simpler than a framework.
Safety and Guardrails
Agents can delete files, execute arbitrary code, and make API calls. Implement: sandboxed execution environments (Docker), permission systems (read-only by default), cost limits (max API calls per run), human approval for destructive actions, and automatic rollback capabilities.
Conclusion
AI agents are the next evolution of developer tooling. They handle well-defined tasks autonomously while humans focus on architecture and requirements. Start with simple agents (test runner that fixes failures) before building complex multi-step workflows.