AI agents need a way into your product. There are four common doors: the screen in a browser, a command-line tool (CLI), an MCP server and an API. They are not four rivals on the same layer. Knowing how they differ helps you pick what to build first.
Agents that use screens struggle with long jobs
OSWorld 2.0, first submitted in June 2026 and revised in July, tests 108 real workflows that take people a median of about 1.6 hours. Under its primary binary-completion measure with a 500-step limit, the best agent finished 20.6% of tasks. The authors describe forgotten constraints, missed updates, guessing instead of asking and skipped verification. These tests mix desktop and browser work. They do not measure how often a cloud browser crashes.
Running many browser sessions also needs monitoring, capacity limits and cleanup. Browserless's scaling guide describes browser pools, recycling and recovery as ongoing infrastructure work. It is a hosting vendor's engineering guidance, not proof that browsers cannot scale.
Shorter tasks score much higher
Browser agents can perform well on shorter tasks. The Steel leaderboard lists a WebVoyager score above 99%, but that is a builder-reported benchmark result, not a promise for your business's workflow.
Scores need care too. A research audit of 150 failure-scored trajectories found 15.3% of those failure verdicts were wrong. The sample spans five web, enterprise and desktop benchmarks; it does not mean 15.3% of every benchmark's failures are false.
For a business, the useful question is not whether agents can click. It is whether an agent can complete your whole job, keep the customer's constraints and verify the result.
How CLI, MCP and API compare
An API can provide the underlying operations. A CLI or an MCP server can expose those operations through an interface an agent can use. A business may need more than one surface, rather than choosing a universal winner.
In Scalekit's March 2026 benchmark, five read-only GitHub tasks ran on one model. Its token table reports MCP using 4 to 32 times as many tokens as CLI. CLI completed 25 of 25 runs; MCP completed 18 of 25. All seven MCP failures were connection timeouts to the remote server, not MCP protocol errors. The MCP configuration loaded 43 tool descriptions, including many irrelevant to each task. This is evidence about that setup, not a general reliability rate for MCP.
Anthropic's engineering guidance describes ways to reduce context costs through code execution and loading tools when needed. A later controlled comparison found that the software around the agent mattered more than the interface. It tested seven agent setups, five models and one software task; MCP and CLI failures were equally common.
These CLI/MCP comparisons concern developer work. They do not prove that a CLI is the best route for booking, payments or every customer-facing task.
A simple plan for your business
- Start with a few useful operations. Document the jobs customers ask for most, their inputs, outputs and error states. If you expose an API, make its contract clear.
- Choose the surface for the user and job. A CLI may fit a developer workflow. An MCP server may fit an agent integration. In either case, keep permissions scoped and avoid loading a large irrelevant tool list.
- Keep the website working for people. A structured agent route does not remove the human customer's need to browse, review or get help.
- Test a complete workflow before expanding. Check the result, the authorization boundaries and the recovery path, not only whether a call succeeded.
Commerce protocols are emerging, but they do not establish one winning interface. Google's Universal Commerce Protocol supports APIs, A2A and MCP. It gives businesses several ways to integrate. OpenAI's March 2026 product-discovery update also shows that platforms can change direction: merchants use their own checkout experiences while OpenAI focuses on discovery.
Build a route that works for a concrete job today. Keep the human route, and revisit the agent interface as real use and the standards change.