Short answer
An AI agent API should represent a work lifecycle, not only a model response. Product teams need a task or session they can stream, observe, continue, and turn into reviewable artifacts.
- Use a model API when the product needs inference or a conversational response.
- Use an agent SDK when the team wants to operate the agent loop in its own infrastructure.
- Use an agent backend when the product needs hosted runs, durable sessions, files, artifacts, routing, and a control plane.
Represent work, not just messages
- Input
- A product task, selected configured agent, files, authorized context, and execution policy.
- Lifecycle
- A durable task, run, or session with status, streaming events, retries, and continuation.
- Output
- Structured results, generated files, diffs, artifacts, and metadata the product UI can render.
Choose the layer that matches the product job
| Question | Model API or agent SDK | Agent backend API |
|---|---|---|
| Who operates the loop? | Your application and infrastructure. | The hosted agent backend or harness layer. |
| What comes back? | Messages, tool calls, or SDK events. | A run with state, files, artifacts, and continuation. |
| Best fit | Custom loops and infrastructure control. | Shipping agent-backed product features without rebuilding the execution backend. |
What to verify before choosing an AI agent API
- Confirm how sessions persist and how a user continues or revises prior work.
- Verify streaming semantics, idempotency, concurrency, cancellation, and retry behavior.
- Check how files enter the run and how artifacts return to the product UI.
- Keep enough execution evidence to explain harness choice, model choice, tool activity, latency, failure state, and cost where billing records expose it.
- Keep harness-specific behavior behind a stable product contract.
FAQ
- Is an AI agent API the same as an LLM API?
- No. An LLM API returns model inference. An AI agent API can also manage tools, sessions, execution state, files, and artifacts produced across a longer-running task.
- Is an AI agent API the same as an agent SDK?
- No. An agent SDK usually runs inside infrastructure you operate. A hosted agent API can operate the run lifecycle and expose it to your product as a service.
- When does a product need multiple agent harnesses?
- When different tasks benefit from different agent environments, or when the team wants to change harnesses without rewriting its product integration and output handling.
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