Equip AI coding agents with grounded tools and strict engineering enforcement.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Hyperstack" yet — see the docs or source repo.
Use Hyperstack to enforce strict engineering guardrails for my AI coding agent: only modify the payment module based on real repository context, inspect relevant files and tests first, then generate a patch, and block any hallucinated APIs or unverified changes.
A safe change plan grounded in real code context, a patch, and notes on risks and validation steps.
With Hyperstack, require the AI agent to diagnose the root cause, read tests and logs, propose verification hypotheses, then apply a minimal fix for this login issue, and explain why this is better than rewriting directly.
An engineering-grade result including root cause analysis, verification steps, a minimal fix patch, and rationale.
Use Hyperstack to turn a general LLM into a high-precision engineering agent: configure grounded MCP tool access, failure interception rules, and adversarial checks for a refactoring task so outputs follow repository conventions, test requirements, and dependency constraints.
A reliable agent workflow with tool-use constraints, enforcement checks, and a compliant refactoring result.
Build AI agents quickly with a model-driven approach and minimal code.
Expose internal agent tools as a standard MCP server for unified access.
Build and iterate meta-harness scaffolding for fixed models via propose-score-Pareto loops.
Scaffold a branded AI agent harness with CLI, MCP, memory, and learning.
Give AI coding agents persistent memory and enforced rules to avoid repeated mistakes.
Let AI manage Harness CI/CD, GitOps, feature flags, and cost data.