Core Components Every Agent Needs
Every AI agent requires a language model to interpret goals and generate plans. Beyond the model, agents need a tool layer that defines what external capabilities the agent can invoke—web search, code execution, database queries, API calls, file operations. Memory systems let agents retain context across steps within a session or across sessions over time. The orchestration loop connects these components: the agent receives a goal, produces a plan or next action, calls a tool, processes the result, and decides what to do next. How this loop is designed—how errors are handled, when the agent asks for clarification, and what triggers termination—determines the agent's reliability in practice more than the underlying model does.
The Development Lifecycle
Agent development typically begins with task scoping: defining precisely what the agent needs to accomplish, which tools it requires, what its boundaries are, and what happens when it fails. Developers then select or build an orchestration framework, define tool schemas with clear input and output specifications, and write system prompts that govern agent behavior. Evaluation pipelines run the agent against representative tasks to surface failure modes before deployment. Production readiness requires observability infrastructure—logging each reasoning step, tool call, and output—and human-in-the-loop mechanisms for tasks where autonomous errors carry meaningful cost. The cycle then continues: monitor production behavior, identify failure patterns, update prompts or tool definitions, and re-evaluate.