# AI agent API for product developers

> What an AI agent API needs beyond a model response: sessions, tools, streaming, files, artifacts, routing, and production controls.

Canonical URL: https://harnessrouter.ai/guides/ai-agent-api
Published: 2026-07-22
Updated: 2026-07-22
Category: Architecture
Keywords: AI agent API, agent API, AI agent platform, agent runtime, agent orchestration, AI agent backend

## Short Answer

An AI agent API should expose a complete work lifecycle, not only a model response. Product teams need a task or session they can stream, observe, continue, and turn into reviewable files or artifacts.

## Takeaways

- 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 product-facing controls.

## The API should represent work, not just messages



- Input: A product task, selected harness, files, user 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, tool outcomes, 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 product-ready 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.
- Require traceability for harness choice, tool actions, cost, latency, and failure state.
- 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.

## Sources

- [OpenAI Codex cloud](https://developers.openai.com/codex/cloud): External source.
- [Claude Agent SDK overview](https://code.claude.com/docs/en/agent-sdk/overview): External source.
- [Claude Managed Agents quickstart](https://platform.claude.com/docs/en/managed-agents/quickstart): External source.