Agent-native infrastructure where LLMs are the reasoning kernel, agents run as first-class processes, and enterprise systems become programmable substrates for autonomous work.
Trusted by engineering teams at
0.1 The problem
Teams deploying AI agents today face fragile orchestration, hidden failure modes, and no clear runtime architecture. The result: agents that can't be trusted in production.
Duct-taped pipelines break at scale. Ad hoc coordination between agents produces unpredictable failures.
Agents run as black boxes. No observability, no audit trail, no way to understand what went wrong.
No agent identity, no access controls, no sandboxing. Every deployment is a trust leap.
Agents that still require human review at every step defeat the purpose of autonomous software.
0.2 The architecture
0.3 Capabilities
LLM-as-OS architecture with pluggable models, dynamic routing, and sandboxed execution — agents run as first-class processes, not duct-taped scripts.
Long-horizon coordination, multi-agent kanban workflows, and execution loops eliminate fragile pipelines. Agents converge reliably.
Agent identity management, access controls, sandboxed execution, and full monitoring — observable and auditable from day one.
A non-interactive pipeline where specs drive development, agents write code, harnesses validate, and systems converge — no human bottleneck.
Native agents, third-party runtimes like Claude Code and OpenCode, and MCP-based tool integration — one platform, no vendor lock-in.
Trace every agent decision, execution path, and system interaction. Full audit trails and debugging tools for autonomous workflows.
0.4 Kairos
Build, deploy, and manage production-ready AI employees that automate real business workflows — not demos, not prototypes, but agents that operate reliably across your actual systems of record.
Connects siloed enterprise systems into a unified workspace for agents.
Native integration services for every enterprise tool and data source.
Full IAM for AI coworkers — roles, permissions, and continuous monitoring.
0.5 Early adopters
Engineering leaders who moved past demo-ready to production-ready.
"We went from babysitting three agents to deploying twelve that run autonomously. The orchestration layer changed everything for our release cycle."
"The agent identity model solved our biggest compliance concern overnight. Our security team finally signed off on autonomous deployments."
"Kairos connected our five siloed systems in a week. Our AI coworkers now resolve support tickets end-to-end without human handoff."
0.6 The thesis
0.7 Get started
Join the teams deploying AI coworkers that actually work in production. Request early access today.
Or schedule a demo with our team