AI Agents are here! But what are they exactly?

As of 2025, AI agents remain a relatively new concept in the field (see our blog post on the evolution of AI). Emerging from Generative AI, these agents encompass various system types collectively known as Agentic Systems. As the technology evolves, so does the definition. Here’s how we define them::

“Agentic systems interpret a user’s goal, devise a plan to achieve it, and execute tasks independently. They consult tools or real-world data as needed, can pause for human input, and stop once the tasks are complete.”

Anyone who has used ChatGPT knows that large language models (LLMs) can interpret a user’s goal and outline a plan.. at least, they try to. As many have also experienced, AI can sometimes be weird or unhelpful. But this often isn’t the AI’s fault; it simply lacks the necessary context to be truly effective.

This is where agents come in. They go beyond just planning, they execute the plan. That’s what makes them a game-changer for reducing workloads in organizations. If this all sounds too good to be true, let’s break down how agents actually work. Spoiler: it’s not magic.

General purpose Agents

Before we dive into the good stuff, let’s take a moment to look at Big Tech’s attempts at building general-purpose AI agents.

Apple recently had to roll back its Apple Intelligence feature due to AI hallucinations, and Amazon is still struggling to integrate AI into Alexa without reliability issues​. While YouTube demos and blog posts make it look easy to build AI-powered systems, the reality is far more complex. The issue is a lack of context. Without context like: reliable data, tools, documentation and guidance, these systems can find themselves like fish out of water. General purpose AI will improve over time and maybe someday, we will reach a point where it can do it all. But for now, effective AI agents need to be custom designed and applied in a specific context.

Building agents

So, how do you build AI systems that actually work in production? Some believe an agent must be fully autonomous, operating independently over extended periods. Others see them as more prescriptive, following predefined workflows. We find it helpful to categorize all these variations as agentic systems but draw an important architectural distinction between workflows and agents.

• Workflows use LLMs within predefined, structured sequences.

• Agents make independent decisions, dynamically choosing tools and actions​.

This distinction is crucial because not every AI-powered system needs to be a fully autonomous agent. In fact, for many applications, optimizing a simple workflow is often more effective than attempting to build a complex agent​.

Building AI agents presents a tradeoff: increasing autonomy improves task flexibility but often comes with higher costs, longer response times, and potential reliability issues.

“Agentic systems often trade latency and cost for better task performance, and you should consider when this tradeoff makes sense.”

For instance, a customer service chatbot could benefit from agent behavior if it needs to handle complex queries dynamically. But for a straightforward task, like package tracking, a simple rule-based workflow might be more efficient.

This raises the question: When should we use workflows, and when do we need full-fledged agents?

Choosing the Right Approach

To decide between workflows and agents, we need to assess the task at hand:

• Workflows: Ideal for well-defined, predictable processes where steps can be predetermined.

• Agents: Suitable for open-ended tasks that require decision-making and adaptability.

Simple workflow structure to prepair email drafts based on the context of the organisation.

A hybrid approach is often the best solution, combining workflows for structured tasks and agents for handling complexity. For example, AI-powered customer support systems integrate structured workflows (e.g., routing queries) with agentic behavior (e.g., adapting responses based on the conversation).

Practical Strategies for Building Effective Agents

A structured approach, such as prompt chaining, routing, or parallelization, can further enhance efficiency without unnecessary complexity​.

How can organizations successfully implement AI agents and ensure they generate real value? The answer lies in adopting a pragmatic, structured approach that focuses on the following key principles:

1. Start Simple, Then Scale
   Instead of jumping straight into complex multi-agent architectures, companies should begin with straightforward LLM-based workflows. Using basic retrieval-augmented generation (RAG) models or simple prompt chaining can significantly enhance AI agent performance without unnecessary complications.

2. Define a Clear Business Objective
   Before deploying an AI agent, businesses must identify a specific problem it will solve. Whether it’s automating customer inquiries, generating code snippets, or optimizing logistics, the AI’s purpose must be well-defined. This approach prevents wasted resources on vague, underutilized AI deployments.

3. Ensure Robust Tool Integrations
   AI agents need access to the right tools to function effectively. Companies should invest time in optimizing API connections, refining tool documentation, and testing integrations rigorously. A well-designed tool ecosystem allows AI agents to execute tasks reliably and adapt to different workflows.

4. Maintain Human Oversight & Transparency
   AI agents should not operate in complete isolation. Implementing clear checkpoints for human review, transparent decision logs, and easy debugging mechanisms ensures that businesses retain control. Instead of replacing humans, AI should work alongside them, augmenting their capabilities.

5. Measure, Iterate, and Optimize
   The deployment of AI agents should be treated as an ongoing process, not a one-time implementation. Businesses should continuously monitor performance, gather feedback, and make adjustments based on real-world usage. Small iterative improvements often yield the best long-term results.

The Future of AI Agents

As Generative AI continue to improve, AI agents will become more capable, but that doesn't mean every system should aim for full autonomy. The most successful implementations focus on the right balance of automation, adaptability, and control.

By starting simple, testing rigorously, and iterating thoughtfully, businesses can build AI agents that are not only powerful but also reliable, transparent, and aligned with real-world needs.

Final Thoughts: Build Smart, Not Complex

The AI industry is in an exploration phase, with many testing different approaches. However, most real-world applications don’t need full autonomy, they need structured, controlled workflows that deliver reliable results.

We build Agentic systems by starting simple, validating thoroughly, and scaling thoughtfully. AI agents are powerful, but effectiveness always beats complexity.

We follow structured workflows, adding autonomy only when necessary, and testing rigorously. We build AI systems that truly work in the real world.