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.
Discover AI agent harness resources for tooling, evals, orchestration, and observability.
Build AI agents quickly with a model-driven approach and minimal code.
Expose internal agent tools as a standard MCP server for unified access.
Enable AI agents to run security tools for pentesting and vulnerability research.
Dynamically expose proxied server tools based on an agent persona.
Turn vague automation requests into tool stacks, prompts, QA checks, and human boundaries.