Agents that carry
the work forward.

OpenAI released the Agents API in public beta on 10 September 2026. It manages durable sessions, orchestration, context compaction and recovery, with hosted or connected sandbox environments. Official release changelog.

The runtime can execute code, work with files, connect to Model Context Protocol (MCP) servers and return artefacts. A session can continue across turns, so an application can show progress and let a user steer the work. Agents API documentation.

The business value to test is reduced engineering effort around a task that genuinely needs several steps. Your team still defines what success means, which data is available and which actions require review. A managed runtime does not make an incomplete brief or unreliable source data disappear.

Proposed pilot: an operations manager supplies a date range and asks why ticket volume increased. The agent reads permitted ticket summaries and product-release notes, groups the changes, and produces a source-linked explanation. Customer identifiers are removed unless needed for the approved question.

Verification runs outside the narrative: ticket counts must match the reporting system, categories must sum correctly, and every claimed release-related cause needs supporting evidence. Correlation is presented as a hypothesis until checked. Missing data produces an explicit limitation and a request for the missing source.

Evaluate time to a reviewed answer, arithmetic errors, unsupported explanations and total cost. Compare with a straightforward saved query and dashboard. The agent earns its place when the question varies enough that the fixed workflow becomes burdensome.

OpenAI’s documentation separates the managed session from the execution environment. A self-hosted sandbox is not a self-hosted model or control plane. Map which prompts, tool results and artefacts cross each boundary before selecting the deployment. Runtime and environment overview.

Model tokens, tools and hosted containers are separate cost lines. Price a complete job, including waiting time, retries and review. Set time limits, stop abandoned environments and record spend by task. Published billing categories. Our AI observability guide explains the wider operational measurement.

Use a conventional worker for deterministic jobs. For cross-platform work owned by another agent, consider an explicit agent handoff. Multiple agents are useful when independent work can be verified and combined; adding them to a simple sequence introduces coordination overhead.