Plano

Product Introduction

1. Definition: Plano is an AI-native proxy and dataplane infrastructure purpose-built for agentic applications. Technically categorized as delivery infrastructure for AI agents, it operates as a protocol-native sidecar that abstracts non-core functionalities like routing, observability, and policy enforcement.
2. Core Value Proposition: Plano eliminates redundant plumbing work in AI development, enabling teams to ship production-ready agents faster. Its primary keywords include agent delivery infrastructure, LLM orchestration, and centralized observability for agents.

Main Features

1. Unified /v1/responses API:

   • How it works: Plano’s API standardizes state management across agent interactions, handling prompt processing, response formatting, and error handling via a single endpoint. Built on Envoy Proxy, it integrates hooks for context engineering (e.g., prompt filtering) and real-time telemetry.
   • Technologies: RESTful architecture, Envoy Proxy extensions, OpenTelemetry for traces.

2. Agent Routing & Orchestration:

   • How it works: Dynamically routes tasks to optimal agents or LLMs based on predefined policies (e.g., cost, latency, accuracy). Supports multi-agent workflows without framework lock-in via a declarative YAML configuration.
   • Technologies: Weighted round-robin algorithms, gRPC for inter-agent communication, integration with Hugging Face and OpenAI APIs.

3. Guardrail Hooks & Centralized Security:

   • How it works: Intercepts all agent inputs/outputs to enforce safety policies (e.g., jailbreak detection, PII redaction). Policies are defined centrally and applied globally, enabling SOC2-compliant deployments.
   • Technologies: WebAssembly (Wasm) modules for custom hooks, regex-based pattern matching, OPA (Open Policy Agent).

Problems Solved

1. Pain Point: Fragmented agent development, where teams waste resources rebuilding boilerplate logic (e.g., routing, observability) for each project.
2. Target Audience:
   • AI Engineers: Accelerate prototyping-to-production cycles.
   • DevOps Teams: Standardize security/access controls across LLMs.
   • Product Managers: Enable reinforcement learning via production trace data.
3. Use Cases:
   • Deploying multi-agent customer support systems with failover routing.
   • Enforcing compliance in healthcare/finance apps via centralized guardrails.
   • Optimizing LLM costs by routing queries to cost-effective models (e.g., Mixtral vs. GPT-4).

Unique Advantages

1. Differentiation: Unlike siloed frameworks (e.g., LangChain), Plano is infrastructure-agnostic, working with any AI stack while abstracting non-differentiating complexities. Competitors lack its Envoy-based dataplane for granular traffic control.
2. Key Innovation: Protocol-native architecture that treats agents as network services, enabling features like zero-trust security policies and unified state management via a single config file.

Frequently Asked Questions (FAQ)

1. How does Plano simplify agent deployment?
   Plano’s unified API and declarative configuration abstract routing, observability, and security, reducing deployment time from weeks to hours.
2. Can Plano integrate with existing AI models?
   Yes, Plano supports any LLM (e.g., Hugging Face, OpenAI) and AI framework via REST/gRPC, ensuring no vendor lock-in.
3. What makes Plano suitable for regulated industries?
   On-premises deployment options and centralized guardrail hooks (e.g., PII masking, compliance audits) meet HIPAA/GDPR requirements.
4. How does Plano handle multi-agent orchestration?
   Its Envoy-based dataplane uses smart routing APIs to manage task delegation, retries, and load balancing across agent clusters.
5. Is Plano open source?
   Plano’s core is built on open-source Envoy Proxy, with extensions available on GitHub for custom integrations.