Do you really need AI Agents?

The rise of AI agents has introduced a groundbreaking way to handle complex workflows. These systems allow Large Language Models (LLMs) to determine workflows dynamically, offering flexibility where traditional, pre-determined processes fall short. However, agents aren’t always the right choice. Sometimes, simplicity and deterministic workflows deliver better results. So, how do you decide when to use agents and when to avoid them? Let’s explore.

The Case for Deterministic Workflows

In many cases, pre-determined workflows provide all the functionality needed to address a specific problem. These workflows are tightly defined and coded, offering simplicity, reliability, and robustness.

Take the example of a surfing trip website. Imagine your app handles two primary customer requests:

1. Searching for trip details — You could offer a search bar that connects users to your knowledge base.
2. Speaking to sales — A simple contact form could direct inquiries to your sales team.

If your users’ needs are confined to these two buckets, deterministic workflows are your best bet. Coding these flows ensures a 100% reliable system with no risk of error introduced by an LLM interpreting a user’s intent incorrectly.

By avoiding agentic behaviour in such scenarios, you benefit from:

• Simplicity — No need to manage the complexity of multi-step AI decision-making.
• Reliability — The system always follows a predictable path.
• Cost-Efficiency — Pre-determined workflows are resource-light compared to the computational requirements of agents.

If your workflow can handle all reasonable queries without deviation, there’s no need to complicate things with agents.

When Deterministic Workflows Fall Short

Now, let’s look at situations where a rigid, pre-determined approach won’t cut it. Real-world problems are rarely so neatly defined. When workflows involve ambiguous, multi-faceted requests that can’t be easily categorized, you may need the flexibility of an agentic system.

Consider this example:

A user asks, “I can arrive Monday but might be delayed until Wednesday. Can I surf on Tuesday morning, and can I add cancellation insurance?”

This request involves multiple layers:

• Checking availability on specific days.
• Evaluating cancellation insurance options.
• Understanding personal schedules and logistical details.

A deterministic workflow would struggle to handle such nuanced and variable queries. You’d have to pile on endless “if/else” conditions, making the system cumbersome and prone to errors.

Where Agents Shine

In situations where workflows can’t be neatly defined in advance, agents offer the flexibility needed to respond to complex requests. An agentic system could handle the surfing trip example above by dynamically accessing various tools and APIs:

• Weather API — To provide accurate forecasts.
• Google Maps API — For calculating travel distances.
• Employee Availability Dashboard — To check staff schedules.
• RAG System — To retrieve relevant answers from your knowledge base.

Agents allow systems to break free from rigid workflows, adapting to user requests on the fly. This makes them ideal for solving problems in unpredictable or high-variability domains.

The Evolution from Static to Dynamic Systems

Before agents, computer programs relied heavily on pre-determined workflows and logic gates. They were effective for narrow tasks — like summing numbers or finding the shortest path — but lacked the adaptability to handle complex, real-world scenarios.

Agents, powered by LLMs, are changing the game by opening up new possibilities. Instead of piling on conditionals, they can autonomously navigate complex environments and integrate multiple data sources to find optimal solutions.

For example, in the surfing trip case, the agent wouldn’t simply follow a static path. It would:

1. Analyze the user’s intent.
2. Gather relevant data from APIs.
3. Dynamically generate a solution.

This level of adaptability isn’t just convenient; it’s transformative. It allows software to tackle tasks previously reserved for human judgment.

When Not to Use Agents

While agents can handle complexity, they’re not always the best choice. Overusing them can introduce unnecessary risks and costs. You should avoid agents when:

1. The workflow is well-defined
   If the task can be broken into predictable steps, a deterministic system is more efficient and reliable.

2. Error tolerance is low
   Agents, powered by LLMs, are probabilistic systems, meaning they may occasionally make incorrect decisions. For applications where accuracy is critical, avoid relying on agents.

3. Cost and performance are priorities
   Running an agent system requires more computational resources, which can increase costs and latency. In contrast, deterministic workflows are lightweight and fast.

Finding the Right Balance

Ultimately, the decision to use agents depends on your application’s needs. Here’s a quick guide:

Use Deterministic Workflows for tasks that are repetitive, predictable, and require high reliability. Examples include:

• Basic customer support systems.
• Simple e-commerce workflows.
• Data entry applications.

Use Agents for tasks involving ambiguity, variability, or high complexity. Examples include:

• Dynamic customer support with highly personalized queries.
• Workflow orchestration in domains like logistics or healthcare.
• Research tasks requiring integration of diverse data sources.

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Agents are a powerful tool, but like any tool, they have their place. When workflows are straightforward, stick to deterministic systems for simplicity, reliability, and cost-efficiency. However, when you face unpredictable, multi-step tasks, agents shine by providing the flexibility and adaptability you need.

This is where platforms like mkinf come in. mkinf makes it easy to integrate agents into your workflows while giving you the tools to optimize performance, adapt to complex demands, and maintain production-ready reliability. Whether you need deterministic workflows or cutting-edge agentic solutions, mkinf helps you navigate the balance to build effective and efficient systems.

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