Plano is a models-native proxy and dataplane for AI agents that serves as delivery infrastructure for agentic applications. It centralizes functionality that shouldn't be bespoke in every codebase, allowing developers to focus on core product logic while Plano handles the critical plumbing work.

Plano provides agent routing and orchestration capabilities, guardrail hooks for security, learning signals and traces for observability, and smart routing APIs across LLMs. The platform supports multi-agent systems without framework lock-in, reusable filters for context engineering, production signals for reinforcement learning, built-in guardrails and centralized policies for security, and on-premises deployment options for regulated environments.

Plano works as a framework-friendly, protocol-native fabric that takes over plumbing work including detecting and blocking jailbreaks, routing tasks to the right model or agent for better accuracy, applying context engineering hooks, and centralizing observability across agentic interactions. It offers a simple configuration file that describes the types of prompts your agentic app supports, APIs needed for agentic scenarios including retrieval queries, and your choice of LLMs.

Benefits include faster delivery of prototypes to production, accelerated feedback loops for reinforcement learning, standardized policies and access controls across agents and LLMs, and safer, more reliable scaling of AI applications. Developers can use any language and AI framework while maintaining focus on core objectives rather than infrastructure concerns.

Plano targets developers building agentic applications, product teams working with AI agents, and engineering teams needing to standardize policies across AI systems. The platform is built on Envoy proxy technology and offers programmable architecture suitable for various deployment scenarios including regulated environments requiring full data control.