# Research

Our applied research focuses on how to deliver agents safely, efficiently, and with improved real-world performance — critical for any AI application, but work that sits outside of any agent's core product logic.

[Available on Hugging Face](https://huggingface.co/katanemo)

### Model Routing

Great agent UX starts with using the best model for a task — fast and cheap when it can be, smarter when it needs to be — and our routing research gives developers the controls to do exactly that. Our policy-based router captures your evals and preferences, while our performance-based router learns from real traffic over time, so you can evolve model choices without retraining.

### Governance & Learning

Building an agent is easy — knowing what it does in production and how to improve it is very hard. Our research focuses on making agent behavior observable and governable: studying how agents respond to real and adversarial traffic, policy changes, and turning signals into learning loops that make agents safer and more effective over time.

### Agentic Performance

Better system performance comes from directing traffic to the right agents for each task or workflow. We build compact orchestration models that manage traffic between agents — ensuring clean handoffs, preserved context, and reliable multi-agent collaboration across distributed systems.

# Meet Plano-Orchestrator. Our latest models.

Plano-Orchestrator is a family of state-of-the-art routing and orchestration models that decides which agent(s) or LLM(s) should handle each request, and in what sequence. Built for multi-agent orchestration systems, Plano-Orchestrator excels at analyzing user intent and conversation context to make precise routing and orchestration decisions.

[Download Plano models](https://huggingface.co/katanemo) [Get Started with Plano](https://docs.planoai.dev/)

## PLANO-ORCHESTRATOR CAPABILITIES

### Accurately route with confidence with no compromise

Designed for real-world deployments, it delivers strong performance across general conversations, coding tasks, and long-context multi-turn conversations, while remaining efficient enough for low-latency production environments.

### Multi-turn Understanding

Makes routing decisions based on full conversation history, maintaining contextual awareness across extended dialogues with evolving user needs.

### Multi-Intent Detection

Identifies when a single user message requires multiple agents simultaneously, enabling parallel/sequential routing to fulfill complex requests.

### Content-Dependency Routing

Correctly interprets ambiguous or referential messages by leveraging prior conversation context for accurate routing decisions.

### Conversational-Flow Handling

Understands diverse interaction patterns including follow-ups, clarifications, confirmations, and corrections within ongoing conversations.

## BENCHMARKS

### Production excellence, outperforming frontier LLMs

## Focus on prompting, not plumbing. Build with **plano**, get started in less than a minute.

[Deploy today](https://docs.planoai.dev/get_started/quickstart) [Documentation](https://docs.planoai.dev/get_started/quickstart)