Plano

Plano serves as a models-native proxy and dataplane for AI agents, streamlining the transition from prototype to production. It handles complex underlying tasks in AI development, including efficient agent routing, multi-agent orchestration, comprehensive observability through rich agentic traces, robust guardrail hooks, and intelligent model routing APIs for Large Language Models (LLMs). By abstracting away this critical 'plumbing work,' Plano empowers developers to focus on the unique product logic of their agents, significantly accelerating development cycles and ensuring reliable, scalable deployments.

How to use Plano

Developers can leverage Plano by configuring a simple YAML file to describe prompt types, integrate necessary APIs for agentic scenarios (like retrieval queries), and select their preferred LLMs. This approach allows teams to quickly build and deploy agents by focusing on prompt engineering rather than complex infrastructure setup, enabling a rapid start within minutes. Plano is designed to be framework-friendly and supports any language, making integration seamless for diverse development environments.

Core Features of Plano

• Unified /v1/responses API with state management
• Agent Routing and Orchestration for multi-agent systems without framework lock-in
• Rich Agentic Traces for comprehensive observability across agentic interactions
• Built-in Guardrail Hooks and Centralized Policies for security and access control
• Smart Model Routing APIs for LLMs, including policy-based and performance-based routing
• Context Engineering with reusable filters for smarter, more accurate agents
• Production Signals for continuous improvement via reinforcement learning loops
• On-premises Deployment option for full data control in regulated environments
• Plano-Orchestrator models for advanced multi-agent orchestration, featuring multi-turn understanding, multi-intent detection, content-dependency routing, and conversational-flow handling.

Use Cases for Plano

Plano is ideal for engineering teams building and deploying complex AI agent systems. It enables the creation of multi-agent architectures without being locked into specific frameworks, facilitating flexible and scalable solutions. Product teams can utilize Plano's production signals to accelerate feedback loops for reinforcement learning, leading to continuous agent improvement. Furthermore, it supports the implementation of robust, centralized security policies and guardrails, crucial for ensuring the safe and reliable scaling of AI applications, especially in environments requiring full data control and on-premises deployment.

Advantages of Plano

Plano offers significant advantages by accelerating the delivery of AI agent prototypes to production environments with enhanced reliability. It drastically reduces development overhead by automating critical plumbing work, allowing developers to concentrate on the core product logic of their agents. The platform facilitates faster feedback loops for reinforcement learning, leading to more effective and continuously improving AI agents. It standardizes policies and access controls across agents and LLMs for scalable and secure operations, supporting any language and AI framework. Its models-native data plane, built on Envoy, ensures high performance and a delightful developer experience with simple configuration, while also providing robust governance and learning mechanisms for agent behavior.