MCP-native scientific skills for reproducible computational biology and AI-driven drug-discovery workflows. It combines deterministic scientific tools with an MCP server to give AI agents real computational capabilities.
Enables automating materials science research workflows through a multi-agent AI platform, including literature discovery, knowledge extraction, simulation, and document generation.
Enforces structured, evidence-guided software engineering tasks with cognitive actions (investigate, plan, verify, remember) and persistent state for LLM-based coding agents.
An autonomous multi-agent orchestration layer that generates a bespoke software engineering organization for a given objective, runs parallel OpenCode workers in isolated git worktrees, and integrates reviewed code through a replan loop.
Ontology-driven multi-agent platform that encodes business policy as code, enabling deterministic decision-making and multi-domain agent collaboration without code redeploy.
