AI Agents and Workflows are not the same. Here’s why

AI agents are one of the most exciting and evolving aspects of 2025. From assisting in customer support to automating workflows in enterprises, AI agents are increasingly integrated into real-world applications. However, despite their popularity, building effective and reliable AI agents is a challenging task. Many of the AI agent demonstrations we see online are just prototypes that struggle when deployed in real-world settings.

Even large companies such as Apple face difficulties integrating AI into their products effectively. Apple recently had to retract its AI-powered summarization feature due to hallucinations. So, how do developers build AI agents that are practical, scalable, and reliable?

This blog post will provide a structured approach to understanding AI agents, differentiating between AI workflows and agents, and exploring practical techniques for building robust AI systems.

Understanding AI Agents vs. AI Systems

Before diving into the technicalities, it’s crucial to define what an AI agent truly is. Many online tutorials misinterpret AI agents as simple systems making an API call to a Large Language Model (LLM). However, true AI agents go beyond this basic function.

Workflows vs. AI Agents

AI systems can be categorized into two primary types:

1. Workflows: These are predefined sequences where LLMs and other tools are orchestrated using explicit code paths. They follow structured logic and operate with a defined start and end point.
2. AI Agents: These are more dynamic, allowing LLMs to take control of their processes and tool usage, making autonomous decisions on how to accomplish a task.

While the term “AI agent” is often used interchangeably, many practical applications don’t need full agentic behavior. Instead, structured workflows are sufficient for most tasks, offering better control and predictability.

Building AI Systems: Key Strategies

Developers must use the right strategies and tools when building AI systems. Let’s break down the essential considerations.

1. Choosing the Right Tools

• For Developers with Coding Skills: Python, TypeScript, and JavaScript are excellent choices for building AI applications.
• For No-Code/Low-Code Developers: Platforms like Make.com, n8n, and Flowise provide visual workflow builders to create AI-powered automation without deep coding knowledge.

The key is not just the tool but the structured approach used to design AI-driven workflows.

2. Core Building Blocks of AI Systems

A well-designed AI system consists of fundamental building blocks that enhance its reliability and effectiveness. These include:

• Retrieval Augmented Generation (RAG): The AI system pulls relevant information from external sources (like databases or vector stores) to enhance its knowledge and responses.
• Tool Integration: Connecting the AI to APIs or external services (e.g., fetching real-time data like weather updates or order tracking).
• Memory Handling: Maintaining past interactions to create better contextual understanding in AI responses.

Effective AI Workflow Patterns

To create AI systems that are scalable and reliable, developers should adopt structured workflow patterns. Here are some of the most effective ones that I found on Anthropic’s blog post:

1. Prompt Chaining

Rather than relying on a single AI call, breaking tasks into sequential steps improves results.

Example:

If an AI is generating a blog post, instead of directly asking it to “write a blog post,” the workflow can be structured as:

1. Generate research and topic ideas.

2. Create an outline.

3. Write the first draft.

4. Review and refine each section.

5. Format the final version.

This approach ensures better control and output quality.

2. Routing

Routing allows the AI to categorize incoming requests and direct them to appropriate workflows. For instance, in customer service, AI can route inquiries into different categories, such as billing issues, technical support, or order tracking, and then execute the relevant workflow.

Example:

A banking AI chatbot that identifies whether a customer inquiry is about balance checks, loan applications, or fraud reporting and then directs the request to the appropriate department.

3. Parallelization

Some AI tasks can be executed simultaneously to improve efficiency. Instead of processing tasks sequentially, parallelization allows multiple AI operations to run at the same time, reducing latency.

Example:

An AI-powered moderation system evaluates a piece of text for correctness, harmfulness, and policy violations simultaneously.

4. Orchestrator-Worker Model

This model allows an AI orchestrator to decide which tasks need to be performed and delegate them to different worker models. This is particularly useful for complex workflows, such as analyzing customer service tickets and automating appropriate responses.

Example:

A logistics company uses an AI-powered orchestrator to assign delivery tasks to different worker models based on package size, location, and delivery urgency. The orchestrator ensures efficiency by dynamically distributing workloads among available delivery agents, optimizing routes, and updating customers with real-time tracking.

5. Evaluator-Optimizer Loop

In this pattern, the AI first generates output, another AI instance reviews it, and then another iteration improves it based on feedback.

Example:

AI writes a product description → AI evaluator checks for clarity → AI refines based on feedback.

The Challenge of Agentic Systems

Unlike structured workflows, agentic systems operate in a feedback loop, making them harder to control and predict. These systems attempt to dynamically solve problems, iterating through multiple attempts until they reach a goal.

Example of an AI Agent:

An AI-powered developer assistant that autonomously identifies a GitHub issue, generates a solution, creates a pull request, runs automated tests, and merges the fix into the production branch if all checks pass. While efficient for routine bug fixes, these systems often require human oversight to handle complex logic and unforeseen edge cases.

While AI agents offer promising capabilities, they often lack consistency, making structured workflows a more practical choice in most real-world applications.

Key Considerations for Developers

1. Avoid Unnecessary Complexity

• Many developers jump into agent frameworks without fully understanding their intricacies.
• Focus on building deterministic, rule-based workflows before introducing agentic behavior.

2. Be Cautious About Scaling

• An AI system working perfectly in a controlled demo may break when deployed at scale.
• Handling large datasets in RAG-based systems presents challenges as retrieval accuracy diminishes with scale.

3. Implement Strong Testing and Evaluation

• AI applications should be systematically tested before deployment.
• Always evaluate whether modifications genuinely improve the system instead of relying on assumptions.

4. Set Up Proper Guardrails

• AI models must have safety mechanisms to prevent harmful or misleading outputs.
• Before sending AI-generated responses to users, implement an additional verification layer.

Final Recommendations

1. Start Simple — Use predefined workflows before transitioning to AI-driven automation.
2. Understand Scaling Challenges — AI systems behave differently under real-world loads.
3. Ensure Robust Guardrails and Testing — Avoid hallucinations and unreliable outputs.
4. Choose the Right Tools & Frameworks — Understand their underlying mechanics instead of blindly adopting frameworks.

By applying these principles, developers can create AI systems that are practical, reliable, and scalable. While AI agents hold immense potential, they remain a work in progress, and structured workflows continue to be the foundation of effective AI automation.

References:

Building Effective AI Agents (Without the Hype) — Dave Ebbelaar

Building Effective Agents — Anthropic

This post was originally published on Medium