Claude's multi-agent API enables defining an "orchestrator" agent to manage "delegate" agents, each with unique toolsets. This creates a programmable, specialized team that mirrors human organizational structures, providing a sophisticated model for tackling complex, multi-faceted problems programmatically.

Perplexity's agent, Computer, leverages a "multi-model orchestration" strategy. For a single user request, it might use Opus for planning, GPT for writing, and Gemini for audio. This model-agnostic approach allows it to always use the best-in-class model for each sub-task, a flexibility its larger competitors lack.

A powerful way to structure your AI agent system is to create a "PM agent" that acts purely as an orchestrator. It receives a task, then delegates to specialized agents (e.g., Designer, Engineer, Researcher), mimicking a real product manager's role.

Maintain a single, unified AI interface but give it the ability to invoke other models as specialized agents. For example, use a primary model like Claude for general tasks but have it automatically call a model like GPT-5.5, which excels at security analysis, to review its own code output.

Different LLMs have unique strengths and knowledge gaps. Instead of relying on one model, an "LLM Council" approach queries multiple models (e.g., Claude, Gemini) for the same prompt and then uses an agent to aggregate and synthesize the responses into one superior output.

An intelligent AI orchestration layer can achieve a cost-to-accuracy balance superior to any single model. By routing queries to a portfolio of different models (large, small, specialized), it creates a new Pareto frontier, delivering higher success rates at a lower average cost than relying on one "best" model.

Jerry Murdock predicts agents will use an orchestration layer to triage tasks, selecting the best LLM for each job—like expensive Claude for reasoning and cheap open-source models for simple tasks. This shifts value from the models themselves to the agent's intelligent orchestration capabilities.

To optimize costs, users configure powerful models like Claude Opus as the 'brain' to strategize and delegate execution tasks (e.g. coding) to cheaper, specialized models like ChatGPT's Codec, treating them as muscles.

Perplexity's core advantage is its model-agnostic orchestration. Unlike vertically integrated competitors (Google, OpenAI), it can select the best model for any task—whether from GPT, Claude, or open-source alternatives—to offer a superior, specialized "orchestra" of AI capabilities.