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That separation is a sound architecture to evaluate for high-load AdTech, not a proven capacity recipe: the reviewed documentation describes SDK behavior and protocol options, but does not establish AdTech-specific latency, throughput, concurrency, availability, or cost targets.
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Why an agent run needs a different front end
A conventional request/response interface often treats a server call as a single unit: submit, wait, display the result. An agent run is different. The runtime may iterate through model responses and tool calls, transfer work to another agent, and continue before it produces a final answer. The browser therefore needs to communicate the state of work in progress, not just render a final block of text.
The OpenAI Agents SDK documents separate streamed model and runtime events, including message output, tool calls and results, handoff requests and completions, reasoning items, and approval requests. Its documentation says, “Streaming keeps your UI responsive and avoids waiting for the entire final result before updating the user.” OpenAI Agents SDK streaming guide
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Those event categories are inputs to an interface, not a mandate to expose every internal detail. Decide deliberately which events have user value and can safely be shown. In particular, the presence of a reasoning event does not make raw internal reasoning appropriate to display.
Define the UI contract before choosing a transport
Keep three responsibilities distinct. The agent runtime owns the execution loop, tool dispatch, and handoffs. The UI contract defines the states and events the client understands. A transport delivers those events. This separation is an architectural recommendation inferred from the documented event interfaces and protocol discussion; it lets a team change runtime or wire protocol without binding every UI component to runtime internals.
Use explicit run states
At the application level, a useful state vocabulary might include queued, running, waiting for tool, waiting for approval, resumed, completed, failed, and cancelled. These are proposed UI states, not a universal SDK enum. Bind each visible transition to a stable event or authoritative server-side state update so the browser does not infer completion from a quiet stream or mistake a dropped connection for a finished run.
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Make events typed and intentional
A contract should distinguish user-facing output from lifecycle events and actions. For example, a message delta can update a response view; a tool-start event can show that work is underway; an approval-required event can open a decision control; and a terminal event can mark a run complete, failed, or cancelled. Include enough stable identity and ordering information for the client to associate events with the right run and reconcile updates. The exact fields and names belong to the application contract; the cited SDK documentation does not prescribe a universal AdTech schema.
Keep internal orchestration details behind the contract unless the interface has a clear, safe use for them. This avoids making UI components depend on a particular tool implementation or exposing internal content simply because it arrived on the stream.
Represent tools, handoffs, and completion as different states
A tool call is not the same thing as a user-visible answer. Treat it as an activity with a start and a result, and make clear when an agent is waiting for a tool rather than waiting for the user. Likewise, a handoff indicates that work is being transferred or continued elsewhere in the run; it is not necessarily completion. The event categories documented by the Agents SDK can support these distinctions, but the application must decide how much detail to render.
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- Progress: Show meaningful progress updates or streamed message output when available. Do not imply a percentage or time remaining unless the system can substantiate it.
- Tool activity: Indicate that a tool is running and, where useful, show a concise outcome. Avoid presenting internal payloads as user-facing copy by default.
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Make approval and cancellation part of the lifecycle
Approval pauses a run; it does not finish it
When an action needs human approval, show what action is pending in terms the user can assess, then provide an explicit approve or reject path. The documented SDK flow can interrupt a stream for approval and resume after the application records the decision and continues from run state. The interface should reflect that pause rather than leaving the user with a spinner that gives no indication of what is needed.
Cancellation needs a durable outcome
Cancellation is a lifecycle event, not merely a browser button or closed connection. The SDK guide describes awaiting stream completion and cleanup; it also describes resuming an unfinished turn from stored state where appropriate. A client should not label an incomplete response as final just because the user navigated away or the network failed. Keep browser connection status distinct from server-side run status, and reconcile with the server before offering resume. That reconciliation pattern is an application design recommendation, not a complete recovery architecture specified by the SDK documentation.
Choose the runtime, UI protocol, and transport on separate grounds
These choices solve different problems. A runtime determines how agent work is executed. An interface protocol standardizes how agent state is represented for a UI. A transport carries updates. Avoid selecting one layer as if it settles the others.
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| Approach | What the cited material describes | Questions to evaluate |
|---|---|---|
| Application-owned runtime | OpenAI describes its Agents SDK as running in the application, where the server can own deployment, tools, state, and approval decisions. | Does the application need direct control of runtime and storage? How well does the SDK fit its language, existing services, and observability? |
| Framework plus routing gateway | A Vercel guide dated June 17, 2026 describes AI SDK functions such as streamText and generateObject, and a Gateway for authentication, routing, usage tracking, failover, and billing. The guide says the SDK and Gateway are loosely coupled and can be used independently. |
How much provider flexibility and billing visibility are needed? Which operational responsibilities remain with the application, and does the framework fit the existing stack? |
| Separate interface protocol | Thoughtworks Technology Radar Volume 34 describes AG-UI as an event-driven protocol for synchronizing agent and interface state over transports including SSE and WebSockets. The Radar lists it at Trial in April 2026. | Is interoperability worth maintaining a translation layer? Does the design need tools to ship their own UI, and is the protocol’s current maintenance and adoption sufficient? |
These are documented capabilities and decision axes, not an independent endorsement or performance comparison. Thoughtworks also notes that MCP approaches embedding UI alongside tools may reduce the need for a separate UI protocol in some designs. Its statement is: “AG-UI is an open protocol and library designed to standardize communication between rich user interfaces and back-end AI agents.” Thoughtworks Technology Radar Volume 34, April 2026. Trial is the Radar’s maturity classification, not a standards-body certification; confirm current maintenance, compatibility, and adoption before choosing it.
Pick SSE or WebSockets for the actual interaction pattern
OpenAI’s JavaScript SDK documents ordinary streamed runs and an optional Responses WebSocket transport. It describes connection reuse as available when desired, while ordinary streaming remains an option if reconnecting between runs is acceptable. Thoughtworks describes AG-UI support for SSE and WebSockets. These documented options do not show that WebSockets outperform SSE or are preferable for a particular traffic pattern. Choose based on the deployment’s connection lifecycle, infrastructure constraints, and measured behavior rather than treating a transport as a capacity guarantee.
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Plan for high load with workload-specific evidence
“High-load AdTech” is not a workload definition. Capacity depends on such factors as run duration, model and tool latency, event volume, network behavior, persistence, hosting, and traffic shape. The reviewed sources establish no suitable p95 latency, concurrency, events-per-second, tokens-per-second, availability, or cost target for an AdTech deployment.
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Investigate the operational controls that fit the actual system, including connection management, admission control, queueing, backpressure, isolation, and visibility into run lifecycle and failures. Set targets only after defining representative workloads and measuring the chosen deployment. The OpenAI running-agents guide documents a JavaScript SDK default of maxTurns set to 10; that is a per-run safety default, not a concurrency limit, throughput figure, or AdTech benchmark. Vercel’s Gateway guide describes usage tracking and failover, but does not establish AdTech-grade capacity or comparative performance.
Test lifecycle behavior as well as volume
- Exercise long-running runs with multiple tool calls and handoffs, not only short successful completions.
- Verify that approval pauses and resumes the intended run, and that rejection has an unambiguous outcome.
- Test cancellation, stream interruption, reconnect, and server-side state reconciliation so a client cannot mistake partial output for completion.
- Measure the behaviors that matter to the product under representative traffic and infrastructure conditions; do not import generic numeric targets from unrelated systems.
The cited materials explain SDK behavior and vendor capabilities; they do not report a finished high-load AdTech product or comparative production results.
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