Agent implementation starts when a configured assistant is no longer enough and the behavior needs to live in code. That usually means tool calling, approval paths, state handling, streaming output, and runtime rules strong enough that the system can survive real product use.
That matters even when the buyer is not technical. A founder, product owner, ops lead, or engineering lead can know the workflow needs stronger control long before they care which SDK sits underneath it. The useful question is not which library sounds advanced. The useful question is whether the agent can act, pause, explain itself, and fail safely inside the product or workflow that owns it.
Most agent work breaks at the implementation layer, not the demo layer
Many agent systems look convincing in a prototype, then get fragile as soon as they touch live tools or users. Tool inputs drift. Streaming output feels noisy. Session state gets muddy. One bad loop burns tokens. One missing approval path lets the model go further than the business intended. That is where implementation becomes the real work, not the model call itself.
Tool schemas, approvals, and state are where the real control starts
We have worked with strict tool definitions, schema validation, step limits, user-visible fallbacks, and approval checkpoints that keep agent behavior reviewable. That includes the point where an agent can call a tool, the point where it must stop and ask, and the point where the business needs a clear record of what happened. Without that layer, the system may still run, but it is harder to trust and harder to own.
Streaming and product UX need as much care as the model call
Agent implementation is not only backend orchestration. The user-facing layer matters too. We have used surfaces such as the Vercel AI SDK when the product needs strong streaming behavior, provider flexibility, and UI feedback around tool usage. The SDK is useful, but the harder part is still the surrounding implementation: auth boundaries, retention rules, partial failures, accessibility, and what the interface should do while the agent is still deciding.
SDK choice matters less than runtime discipline
Different SDKs help in different places. We have worked with OpenAI Agents SDK when the workflow needs handoffs, tracing, and more explicit multi-step runtime control. We have worked with Vercel AI SDK when the main need is a strong product surface around streaming and tool loops. The point is not to worship one stack. The point is to implement the agent layer so behavior, state, and operating rules stay clear even when the underlying SDK changes.
Strong fit, weak fit
The strongest fit is a team that already knows the workflow should live in code and needs the agent layer implemented with clearer boundaries, approvals, and runtime behavior. The weak fit is a team that only needs a configured assistant or a simpler automation path. In those cases, coded agents may be real later, but they are not the first layer to build.
