The anatomy of AI agents

Discussions around AI often swing to extremes: either they're too technical to apply in everyday work or overly ambitious, portraying AI agents as miracle solutions without explaining how they actually function.

This blog takes a more grounded approach. We examine the anatomy of AI agents through the lens of real-world business applications and end-user experiences. Let's take a closer look at what makes up an AI agent, one building block at a time.

How the user interface and short-term memory work together

User interface

The user interface (UI) is the visible part of the AI agent, the layer users directly interact with. While often a chat window, it could just as easily be a button embedded in a CRM form, triggering the agent based on context without requiring text input.

Importantly, the AI model doesn’t “see” this interface. The UI is built using traditional code and simply delivers user prompts to the language model for processing.

Short-term memory

Short-term memory allows the agent to maintain context over the course of a conversation. This makes interactions more coherent and relevant by helping the agent understand new input in relation to prior messages.

Each user interaction spawns a separate session with its own memory. Even if multiple users or agents are using the same model, their interactions remain independent and contextually rich.

When AI agents talk to each other, they can also share a memory session. This enables multi-agent collaboration, where agents exchange information and coordinate tasks while maintaining contextual awareness.

This also shows how a single agent can operate through multiple interfaces, one for humans, another for machines, and perhaps a background process running on a schedule.

Process instructions: giving agents their purpose

Rather than using hard-coded logic, AI agents are guided by process instructions written in natural language. These documents define what an agent should do and how it should behave.

Recent examples I’ve worked on include:

• Analysts based on purchasing behaviour from multiple companies
• Agents specialised in segmentation
• Customer service teams are made up of networked agents

Each of these was built on the same underlying platform, configured through shared natural language instruction files, often editable via tools like Google Docs.

These instructions can also tell agents how and when to ask other agents for help. This creates a collaborative system where AI agents work as a coordinated team, making better use of internal data and capabilities.

Connecting agents to real systems with tools

AI agents can independently access real-time company data, as long as they’re equipped with the right tools. These tools are defined in the agent’s setup and serve as extensions of its capabilities.

Tool descriptions usually include:

• The tool's name
• What the tool can do
• The parameters for invoking it
• The format of the tool's output

This setup allows language models to call tools autonomously. If the reasoning isn't enough, the agent's process instructions can specify how and when to use certain tools, all in natural language.

An important design choice arises here: how much logic should be encoded in tools, and how much left to the AI's reasoning? The most flexible systems allow AI agents to dynamically generate tool parameters, responding fluidly to changing needs.

An interesting consideration arises when developing and evolving tools: How much of the tool logic should be handled by the AI's reasoning and natural language instructions, versus how much should be strictly controlled through hard-coded logic?

In the most flexible solutions, the AI dynamically generates its own tool parameters on the fly, allowing it to flexibly respond to business needs, which can be trained via natural language.

This is a critical question for the flexibility of AI agents and their business potential.

Using unstructured data: the case for RAG

Roughly 80% of a company’s data is unstructured, things like contracts, PDFs, emails, Slack threads, and presentations. These assets are critical for onboarding, compliance, and decision-making.

That’s where RAG (Retrieval-Augmented Generation) comes in. RAG lets an AI agent search your internal data in natural language and bring back the most relevant information for its current task.

With RAG, the AI doesn’t rely only on its training data. Instead, it can pull in live insights from company archives and adjust its answers accordingly, even retrying if the first result isn’t quite right.

Properly structuring and maintaining unstructured data for AI use isn’t just helpful, it’s foundational. As AI becomes more integral to operations, having searchable, structured archives will be a major competitive advantage.

Monitoring performance: how do we measure what matters?

Tracking an AI agent’s performance is key to scaling it effectively. Fortunately, agents can be equipped to log their own activity, much like Google Analytics logs website behaviour.

Typical logging might include:

• Timestamps
• Session IDs
• Original prompts
• Tool inputs
• Tool outputs
• Reasoning paths

This data forms a kind of long-term memory, enabling the agent to learn and improve over time. It also provides valuable insights for human trainers to review and refine.

Business outcomes: measuring the impact

These metrics might already be part of everyday operations in some companies this year:

• The conversion rate of AI agent X assisting customers, and how that changes with training
• Average order value through agent Y, and how it evolves over time
• AB test results of agents X and Y measured by customer satisfaction

When AI agents are tied directly to business metrics, it becomes easier to justify investments, make improvements, and demonstrate ROI.

Getting started and scaling up

Technically, implementing AI agents and using language models is becoming the easy part.
There are off-the-shelf tools, frameworks, and cloud-based models available. With a few configurations and well-written instructions, a company can launch a useful agent quickly.

The bigger challenge is operational: aligning data, IT infrastructure, and business processes to make agents truly impactful. Key questions include:

• How do we scale AI agents as part of the company's IT architecture and business processes?
• What kind of operational model allows AI agents to be adopted and evolved as part of the business?
• These are fascinating questions, and I hope this article provides some good food for thought.

These are the conversations forward-thinking companies are already having. AI is not just a side project; it’s becoming a strategic enabler. The real advantage lies in how fast and effectively your organisation can put AI agents to work.

Ready to explore what AI agents could do for your business? Get in touch to discuss how we can help you get started.

This article was originally written in Finnish by our Principal of Data, Juha Saarinen and published on Digital Commerce Finland’s blog; you can read it here.