How AI Agents work (for you): A look behind the curtain.

Many of us are already using AI tools like ChatGPT regularly, asking questions about everything from daily tasks to work problems.

But AI agents can do much more than just answering questions. They can take actions, such as booking your meetings, analyzing data, managing entire workflows, and even making some decisions without needing constant input. However, it’s essential to keep humans in the loop for critical decisions and oversight.

Let's take a look at what these AI agents actually are, why they're so powerful, and what this means for the future of work.

How AI Agents evolved

Basic AI Applications:

Today it’s quite straightforward to create a simple AI implementation. It’s possible to build an application that calls an AI service like OpenAI or Claude to answer user queries. Some use cases for agents like these are the following:

Conversational AI
Generation of text and Images
Provide enriched responses by including relevant information in their prompts.

These systems can be customised through UX/UI design, careful prompting and fine-tuning, as well as techniques like Retrieval Augmented Generation (RAG) where the AI gets relevant information from the company's documentation or database before generating a response. This is specially useful for knowledge bases, customer support chatbots, or content generation tools.

Agentic AI:

The purpose of an agent is to achieve goals autonomously. This means that AI agents are a fundamental upgrade from simple LLM applications. Instead of responding to individual queries, agents can execute complex workflows, adapt when things don't go as planned, and work independently until objectives are completed.

To understand what makes AI agents so different, it’s important to identify four essential capabilities that allow them to work with agency:

Reasoning ability through access to large language models (LLM) that can understand context, break down problems, and plan solutions.
Defined role and objectives like "you are a customer research agent that gathers company intelligence to help sales teams personalize their outreach."
Memory systems that can remember past interactions, learn from experience, and maintain context across multiple tasks. This includes short-term memory for immediate tasks and long-term memory for building relationships over time. Moreover, tools like Mem0 make it possible to set up memory to work per-user, per-session, shared across agents, or kept separate for each agent depending on the system's needs and security requirements.
Tool usage that allows agents to access APIs, execute code, read databases, etc.

Multi-agent systems

Individual agents are powerful, but the real transformation happens when multiple specialized agents work together in coordinated systems.

There are different ways to organize these systems. We might have a coordinator agent that receives requests and distributes work to specialized agents:

Research agent that gathers information
Analysis agent that processes data
Communication agent that formats and delivers results.

Alternatively, agents can work in specialized pipelines where each handles a specific stage of the process. In our recent CRM agent implementation, we used this approach: one agent extracts contact information, another researches companies using web search, and a third generates personalized message drafts. Each agent specializes in what it does best, passing results to the next stage.

Just like human teams, these agent systems need coordination and information sharing. This is why agentic memory is crucial, agents can share relevant information and maintain collective knowledge about ongoing projects, customer relationships, or operational patterns.

Throughout these processes, human supervision remains critical for quality control, strategic decisions, and ensuring outputs align with business goals and values.

How organizations are using AI Agents today

We see that AI implementations typically fall into three categories within organizations:

Internal supporting tasks. Agents become more powerful when they have access to company context such as incident reports, software logs and documentation, and CRM data. A customer support agent can instantly pull up purchase history context to provide better help.

Software development acceleration. Development teams use agents to generate code faster while keeping humans in control for guidance, verification and oversight. Example of tools: Claude Code and Cursor.

Customer-facing applications. They require the highest quality standards. When done well, these agents can improve user experience and business scalability, but they demand careful attention to data privacy, security, and brand representation.

Implementation realities

AI agents are changing how we work with technology, but a successful implementation requires figuring out who gets access to what data, dealing with new technical setups, and finding the right balance between letting agents work independently while keeping humans in control.

If you're interested in exploring AI agents, the best approach is to start small:

Test basic functionality (like prompt evaluation tooling from Anthropic or OpenAI) before building complex systems
Use minimal infrastructure setup and libraries. Pydantic AI is a good option since it can be tested locally.
Focus on core decision-making patterns first, tool use and data flow.
Always build in proper monitoring from the beginning. You'll need to see what your agents are actually doing to make them work reliably.

Regarding the future of work, the good news is that successful companies aren't trying to replace their people. They're using agents to handle the boring, repetitive tasks so humans can focus on the work that actually requires creativity and judgment. Keeping humans involved in the important decisions is crucial to see real benefits.

At the end of the day, this isn't really only about the technology itself. It's about creating better ways to work and build tools that make people more effective.