AI agents — programs that use large language models to plan and execute multi-step tasks autonomously — have moved from research concept to deployable reality in 2024–2025. Here is a practical orientation for developers.
What Makes Something an Agent
An agent differs from a simple LLM call in three ways: it can take actions (call tools, APIs, read/write files), it operates across multiple steps (the output of one step informs the next), and it can make decisions about what to do next based on results. A chatbot that answers questions is not an agent. A system that reads your email, decides which emails need responses, drafts those responses, and sends them is an agent. The distinction is whether the LLM is directing a workflow, not just answering a question.
Core Building Blocks
Tool definitions: Functions the agent can call (search the web, read a file, call an API, execute code). Tools have typed parameters and return structured results. The planning loop: LLM receives task + available tools → decides what to do → calls a tool → receives result → decides next action → repeats until done. Memory: Short-term (conversation history in context), long-term (database of past results, user facts), and working memory (the current task state).
Frameworks
LangChain (Python) — largest ecosystem, complex but powerful. LangGraph (from LangChain) — better for multi-step stateful workflows. CrewAI — multi-agent coordination (teams of agents). AutoGen (Microsoft) — multi-agent conversations. Claude’s built-in tool use API — simpler for building Claude-specific agents without framework overhead. The choice: start with the Anthropic API’s native tool use for simple agents, move to LangGraph for complex stateful workflows.
The Reliability Challenge
Agents fail in ways that are different from simpler programs — they can take wrong actions, get stuck in loops, or hallucinate tool results. Production-ready agents require: clear tool error handling, human-in-the-loop checkpoints for irreversible actions, logging of every agent step for debugging, timeouts and step limits, and careful prompt engineering of when to stop and ask for help. Start simpler than you think you need and add autonomy incrementally once reliability is established.
