AIAny
AI Agent2024
Icon for item

Kortix (suna)

Runs autonomous AI-agent workforces where each agent, skill, and company process lives as version-controlled code you own. Agents act in isolated sandboxes and submit deliverables for human review, with 3,000+ connectors plus MCP support.

Introduction

Most "AI agent" products hand you a chat box and hope the magic happens inside a black box you can't inspect. Kortix flips that: it treats an autonomous workforce the way engineering treats software — every agent persona, skill, and piece of institutional knowledge is plain text under version control, reviewed and merged like a pull request. The bet is that durable AI automation needs auditability and ownership more than it needs a slicker chat UI.

What Sets It Apart
  • Agents as code, not dashboards: personas are markdown with scoped tool access, so behavior is diff-able, reviewable, and portable instead of trapped in a proprietary SaaS console.
  • Sandbox-per-session with a merge gate: each run executes in an isolated Linux sandbox on its own branch, and output becomes a change request a human approves before it touches main — the same safety model engineers already trust.
  • Reusable skills over one-off prompts: business procedures are encoded once and shared across agents, so institutional knowledge compounds rather than scattering across throwaway prompts.
  • Breadth without lock-in: 3,000+ connectors plus MCP, OpenAPI, and GraphQL, with self-hosting and microVM isolation for teams that cannot ship data to someone else's cloud.
Who It's For

Great fit if you're an engineering-minded team that wants automation you can audit, version, and self-host, and you're comfortable thinking in branches, reviews, and scoped credentials. Look elsewhere if you just want one assistant to answer questions: the code-ownership model and review workflow are overhead you won't need, and a lighter chat-first agent will get you there faster.

Information

  • Websitegithub.com
  • OrganizationsKortix AI
  • AuthorsKortix (kortix-ai)
  • Published date2024/10/05

More Items

Enables RL post-training with million-token prompts under a fixed GPU budget by evaluating shared prompt state without autograd, retaining only minimal model state, and replaying short response branches; instantiated as GRPO and demonstrated on Qwen3.6-27B and GLM-5.2 up to multi-million token execution.

GitHub
AI Infra2026

Defines OpenTelemetry semantic conventions for generative AI telemetry — spans, metrics, and events for GenAI clients, the Model Context Protocol (MCP), and provider-specific integrations. Includes YAML models, human-readable docs, and reference implementations to standardize observability across GenAI deployments.

GitHub
AI Agent2025

Autonomous Red Team agent that plans and executes realistic attack chains (reconnaissance, exploitation, pivoting, C2) while producing Rules of Engagement and an OPPLAN and running actions inside a hardened sandbox; intended for authorized red-team engagements and defensive verification.