Plano provides an AI‑native proxy and dataplane that abstracts the plumbing for agentic applications, handling routing, orchestration, observability, and policy enforcement for LLM‑backed agents. By acting as a sidecar, it lets developers use any language or framework and focus on core agent logic, accelerating the move from prototype to production.
Funding
Funding not disclosed
Founders
Product
Problem
Developing and deploying AI agents that use large language models requires extensive plumbing such as routing, orchestration, observability, and policy enforcement, which adds complexity and slows time‑to‑market. Teams must build custom glue code to manage multi‑agent workflows, enforce security guardrails, and handle model selection, diverting resources from core product logic.
Solution
Plano offers an AI‑native proxy and dataplane that abstracts the plumbing for agentic applications. It provides built‑in routing and orchestration of multiple agents, rich tracing for observability, and extensible guardrail hooks for centralized security and policy enforcement. The platform exposes smart model‑routing APIs that enable safe, scalable selection of LLMs without vendor lock‑in. By acting as a sidecar, Plano lets developers use any programming language or AI framework and focus on the agents’ core functionality, accelerating the move from prototype to production.
Target Audience
Primary customers are engineering teams building AI‑driven products—such as SaaS platforms, enterprise automation solutions, and research groups—that need reliable, secure infrastructure for multi‑agent LLM applications.
Features
- AI‑native proxy that handles request routing, load balancing, and failover for LLM‑backed agents
- Orchestration layer supporting multi‑agent workflows without requiring a specific framework
- Rich, structured tracing and observability hooks for debugging and performance monitoring
- Guardrail integration points for centralized security policies, access controls, and compliance checks
- Smart model‑routing APIs that dynamically select the optimal LLM based on cost, latency, or capability
- Framework‑agnostic design allowing use of any language or AI library
- Optional on‑premises deployment for full data control in regulated environments