Mastering Multi-Agent AI Workflows: Optimize Costs & Logic with N's New AI Agent Tool

As a Senior Technical Consultant deeply immersed in AI and automation, I'm always on the lookout for innovations that truly empower developers and businesses. The N team has just delivered a game-changer: a hot new feature for building multi-agent AI workflows directly within a single canvas. This isn't just an incremental update; it's a significant leap forward in designing intelligent, cost-effective, and robust automation.

Evolving Multi-Agent Workflows: From Sub-Workflows to Integrated Agents

Traditionally, when I’ve wanted to orchestrate multi-agent interactions in N, I’ve often relied on the sub-workflow tool. This approach allows an agent (let's say, an "analyst agent") to call another N workflow as a discrete entity. While effective for certain scenarios—especially when the sub-workflow might involve traditional automation steps alongside AI tasks—it inherently creates two separate environments. Managing these distinct entities requires navigating between different canvases, which, while beneficial for strict separation of concerns in some production contexts, can add overhead.

Introducing the N AI Agent Tool: A Paradigm Shift

The exciting development is the introduction of the AI agent tool itself. This means you can now embed an existing N AI agent directly as a tool within another AI agent's workflow. Imagine an "analyst agent" overseeing the entire process, delegating specific, focused tasks to a "research agent" that acts as its integrated tool. This fundamentally changes how we can design and manage complex AI interactions.

The "Right Model for the Right Job": Unlocking Cost Efficiency and Specialization

One of the most compelling advantages of this new architecture is the ability to strategically assign the "right model for the right job," leading to significant cost optimizations. Consider a scenario where my primary "analyst agent" uses a powerful, albeit more expensive, model like Sonnet 4 for high-level synthesis and report generation. When this agent needs to perform real-world research—a task often involving ingesting a large volume of tokens from external sources like Perplexity—it can now delegate this to a specialized "research agent."

This "research agent," acting as a tool, can be configured to use a much cheaper model, such as GPT 4.1 Nano, which is roughly 37 times more cost-effective than Sonnet. The Nano model can efficiently query, process, and summarize the raw data, delivering a concise, relevant rollup back to the main "analyst agent." This way, the expensive Sonnet model is only engaged for its strengths—complex reasoning and synthesis—minimizing token consumption where a cheaper model suffices. Our testing showed that while total tokens remained similar, over half were processed by the 37x cheaper model, leading to substantial cost savings.

Enhanced Clarity and Scalability

Beyond cost, this integrated multi-agent approach significantly enhances workflow clarity and scalability. By dedicating agents to specific functions, such as research or complex tool calling (e.g., parsing large JSON objects for specific parameters), we can reduce the cognitive load on any single agent. This specialization means agents are less likely to "get confused," as their scope of responsibility is narrowly defined and their prompts can be highly focused.

The N team has rigorously tested this capability, confirming that nesting AI agents multiple layers deep introduces no degradation. This opens up possibilities for incredibly sophisticated, hierarchical AI workflows where different layers of agents handle increasingly specialized tasks.

Practical Implementation: Getting Started

Implementing this is straightforward. Within your existing AI agent, simply add a new tool and search for the "AI agent tool." You then configure it with a clear description (e.g., "Call this research AI agent when you need real-world research done") and define its user prompt—the input your parent agent will provide when calling this sub-agent. The sub-agent, in turn, performs its specific task (e.g., using a Perplexity tool to search) and returns a summarized output.

Observing the Impact: Efficiency in Action

During a live demonstration, we observed the analyst agent initiating the research agent, which then conducted multiple Perplexity searches. Crucially, while the overall token count remained comparable to a single-agent setup, the internal logging clearly showed where costs were saved. We had five calls to the Sonnet model for analysis and nine calls to the 37-times cheaper GPT 4.1 model for research and data ingestion. This granular visibility into agent interactions and token usage is invaluable for ongoing optimization.

The Road Ahead: Best Practices and New Possibilities

This feature is brand new, and the best practices for leveraging it—especially in comparison to traditional sub-workflows—are still evolving. I'm incredibly excited to see the innovative use cases our community will develop. Whether it's for intensive data gathering, specialized tool orchestration, or simply creating more modular and maintainable AI systems, the N AI agent tool represents a powerful new paradigm.

Conclusion

The ability to natively embed AI agents as tools within N workflows is a significant advancement for anyone building cloud-native and AI-driven systems. It empowers us to design more efficient, cost-effective, and intelligently structured automation, truly enabling the "right model for the right job." Happy flowgramming!