Reasoning engine

How Hyperstruck plans, executes, reflects, and revises to deliver higher quality agent outcomes.

In one line

Hyperstruck is a reasoning engine that plans multi-step work, executes with structured tool calls, reflects on output quality, and revises when needed.

Most agent failures are not caused by weak language generation. They come from poor sequencing, missed prerequisites, fragile tool decisions, and no ability to catch and fix mistakes mid-run. Hyperstruck addresses this with a structured reasoning loop that handles the full lifecycle of a task.

What happens during a run

When you dispatch a goal to an agent, Hyperstruck works through four phases:

Planning

The engine decomposes your goal into a structured, multi-step plan organised into milestones. Before building the plan, it retrieves relevant learnings and similar past plans so the approach is informed by prior experience, not just the current prompt.

Execution

Each step is executed with structured tool calls, tracked results, and cross-step data flow. If a step fails, the engine classifies the error and decides whether to retry, degrade gracefully, or fail. Side-effect tool calls are guarded against duplication, even if the engine retries.

Reflection

After execution, the engine evaluates the output against the original goal. It scores quality across multiple dimensions and determines whether the result is good enough to return or needs revision.

Revision

When reflection identifies a problem, the engine revises only the affected milestone rather than restarting the entire task. It uses learnings relevant to the failure to guide the fix, and detects when further revision would not improve the outcome.

What this means for your agents

Better sequencing. Multi-step work with dependencies, prerequisites, and ordering constraints is handled correctly more often.
Fewer silent failures. Problems are caught during reflection rather than silently returned as bad output.
Resilient execution. Transient errors, tool failures, and unexpected states are handled without losing progress.
Explainable outcomes. Every run produces a structured plan and execution trace, so you can see what the agent did and why.

Human-in-the-loop

Hyperstruck supports human approval gates at multiple levels: plan approval, milestone approval, step approval, tool approval, and escalation. You can start fully supervised and gradually increase autonomy as trust builds.

See Agents API for how to resume suspended runs.

Reasoning improves the quality of decisions now. To understand how Hyperstruck improves future decisions as well, see Learning.