Build long-horizon agents with Grok 4.7 on Amazon Bedrock

SpaceXAI’s Grok 4.7 is now available on Amazon Bedrock, which offers a choice of foundation models (FMs) from multiple providers. According to SpaceXAI, Grok 4.7 is trained to work longer on more difficult tasks and knows when to check its own work. That matters most for agents, which carry a task through many steps and must verify what they find along the way.

An agent that works this way has to do more than look things up. It has to form a candidate answer, check it against what it has learned, and refine it until it fits. Grok 4.7 exposes a reasoning effort setting that controls how much deliberation happens before each response, and for agent workloads a practical question is which level to run.

In this post, we build a tool-calling agent on Grok 4.7 and evaluate it on three tasks that require search rather than lookup. The tasks are diagnosing a two-service outage from its alerts, scheduling maintenance under interacting constraints, and choosing an optimal set of migrations under a budget. Each task has exactly one correct answer, verified by brute force. We ran all three at every reasoning effort level, six times each, for 72 agent runs.

According to SpaceXAI, Grok 4.7 uses a larger base model than Grok 4.6 and was trained with a longer reinforcement learning run on a harder mix of tasks. This mix is weighted toward problems that take many hours to complete. SpaceXAI reports that the model is better at verifying its own work and at managing longer context.

SpaceXAI lists a 500K token context window for Grok 4.7, with text and image input and text output. On Amazon Bedrock, the model supports configurable reasoning effort, tool calling, and structured output.

The model supports the OpenAI-compatible Chat Completions and Responses APIs, and the Amazon Bedrock Converse API. On Converse, reasoning is returned as a reasoningContent block alongside the text block. In inferenceConfig , set maxTokens only, and put model-specific parameters, such as reasoning effort, in additionalModelRequestFields .

For the current list of supported Regions, service tiers, and token prices, see the Grok 4.7 model card in the Amazon Bedrock User Guide and the Amazon Bedrock pricing page ( https://aws.amazon.com/bedrock/pricing/ ).

Track format separately from correctness. A missing final line is a different signal from an incorrect one. In our runs, all 72 responses included a gradeable final line.

The agent answered 67 of the 72 runs correctly. At xhigh it answered all 18 correctly, and at low it answered 15 of 18 while using about an eighth of the output tokens. That gives a clear trade-off you can tune per request, and we show how to measure it on your own tasks and how to monitor it in production.

We report token counts rather than currency throughout. Token prices change, and the counts are what your own costs are derived from, so a measurement expressed in tokens stays valid.

SpaceXAI’s published benchmark results follow that emphasis. The largest reported gains over Grok 4.6 appear in the categories that measure sustained work rather than single-turn answers.