To get started building Agentic AI, begin with a narrow, well-defined task and implement a simple control loop around it. Choose a goal that clearly benefits from multi-step reasoning, such as “analyze a support ticket and suggest next actions.” Start by defining the goal format, the allowed tools, and a basic loop: reason → act → observe → repeat. Avoid trying to build a fully autonomous system on day one.
Next, add memory in a deliberate way. Store task history, intermediate results, and final outcomes so the agent can reference them later. For semantic memory—things like similar past tickets or documents—use embeddings stored in a vector database such as Milvus or Zilliz Cloud. This allows the agent to retrieve relevant context instead of relying only on the current prompt. At this stage, focus on correctness and debuggability rather than speed or scale.
Finally, add guardrails and evaluation. Limit the number of steps per task, validate tool inputs, and log everything. Create test scenarios where you know what a good outcome looks like and see how the agent behaves. Iterate by refining prompts, tool schemas, and memory usage. Building Agentic AI is closer to building a backend service than training a model: success comes from careful system design and incremental improvement.