When designing agents, defining their scope and the bounds of their autonomy is critical. This involves deciding what responsibilities an agent should handle and what should be left out, as well as determining how rigid or flexible its design should be. A well-defined scope ensures efficiency, clarity, and scalability. Here’s how to think about these aspects.
Not every task belongs in a single agent. Overloading an agent with too many responsibilities can lead to complexity, inefficiency, or failure to handle edge cases well. Instead, consider splitting tasks across multiple agents, each with a clear scope. For example, imagine a customer support system:
By dividing responsibilities, each agent can specialize, making them easier to design, test, and maintain. The key is to identify natural boundaries in the workflow—where tasks require distinct skills, data, or decision-making logic—and assign them accordingly. Avoid overlap unless collaboration between agents is explicitly designed (e.g., through shared protocols or a coordinating agent).
When deciding what goes into an agent, ask:
If a task feels too broad—like handling all customer support in one agent—it’s a sign to break it down. Conversely, don’t over-fragment; too many agents can complicate orchestration.
An agent’s purpose also dictates how much of its behavior is hardcoded at the initial design. There are three main approaches, each suited to different needs:
Single-Purpose Agents
These agents are built for one specific task with little room for deviation. Their scope is narrow, and their logic is tightly defined. For example, a weather agent that fetches and reports forecasts based on a user’s location is single-purpose. It’s hardcoded to query weather APIs, parse data, and present results in a fixed format.
When to use: For predictable, repetitive tasks where efficiency and reliability are paramount.
Pros: Fast, lightweight, and easy to debug.
Cons: Inflexible; changes in requirements often demand a redesign.
Role-Oriented Agents
These agents are designed for a broader role within a domain, with some flexibility to adapt. For instance, a “marketing assistant” agent might handle tasks like drafting emails, scheduling posts, or analyzing campaign metrics. Its scope is constrained to marketing but allows for varied tasks within that role. The initial design includes guidelines (e.g., brand voice) but leaves room for dynamic decision-making.
When to use: When tasks vary but stay within a clear domain, requiring some adaptability.
Pros: Balances structure with flexibility; reusable across related tasks.
Cons: More complex to design and test than single-purpose agents.
General-Purpose Agents
These agents have a wide scope and are designed to handle diverse, unpredictable tasks. Think of a personal assistant agent that can book flights, summarize articles, or troubleshoot tech issues. Their initial design focuses on reasoning, tool selection, and learning rather than hardcoded workflows.
When to use: For complex, open-ended problems where tasks can’t be fully predicted.
Pros: Highly adaptable; can evolve with new tools or goals.
Cons: Resource-intensive, harder to optimize, and prone to errors in niche tasks.
When choosing the level of hardcoding, consider the trade-off between control and adaptability. Single-purpose agents are like specialized tools—great for one job but useless for others. Role-oriented agents are like skilled workers, versatile within their expertise. General-purpose agents are like problem-solvers, tackling anything but requiring more oversight to stay on track.