Most teams come to AI agent development with a clear sense that they want autonomy, tools, and memory, and a much fuzzier sense of what that actually demands of an engineering team. Building a production-grade agent is closer in spirit to designing a distributed system than to writing a prompt. The framing matters because the same problem is sometimes scoped as straightforward custom software development until the agent layer reveals the iceberg underneath.

A focused single-agent product, say a customer-support copilot or a research assistant, often starts life as an AI app development project. The UI is conventional, the backend is conventional, and the only unusual part is the model call and its surrounding evals. That framing scales for a while. Once you introduce planning, memory across sessions, or tool orchestration, you have crossed into LLM application development territory, where the rules about latency, cost, and failure recovery start to dominate the design.

The next jump is to a multi-agent system: several agents specialized by role, coordinating through messages, shared state, or a router. The engineering challenge there is less about prompting and more about isolation, retries, and the question of who owns the truth when agents disagree. Underneath those agents you almost always need a strong retrieval layer, which is why RAG development and MCP server development frequently sit inside the same engagement.

Whatever the label on the proposal, the delivered system is the same kind of object: code your team can read, infrastructure your team can deploy, evaluations your team can rerun, and documentation that explains why each decision was made. The agent is the easy part. The discipline around it is the work.