The first time I used Bito’s AI Code Review Agent, I thought it might catch some typical anomalies. But it surprised me by identifying deeper issues like performance inefficiencies and potential security risks. That experience made me curious about how AI agents work and what makes them so effective.

I found that their power lies in perception, reasoning, and action. They analyze their environment, make decisions, and continuously improve.

In this blog, we’ll break down how AI agents function, covering perception, reasoning, and learning. By understanding these mechanisms, you’ll see how they adapt to real-world challenges and drive intelligent automation.

If you’re new to AI agents, check out our in-depth guide on AI agents before diving into their inner workings.

Additional context: The challenges of AI agents

AI agents are impressive, no doubt. But, like any tech, they come with their share of challenges. Computational limitations often mean deliberative, and hybrid agents struggle with real-time decisions. And if the training data has biases, well, the agent’s decisions will too.

Then there’s the infamous reward hacking, when agents find shortcuts to achieve goals in ways you didn’t expect and definitely didn’t want. Navigating these issues takes constant monitoring, strong validation, and solid ethical guidelines to keep things on track.

That said, understanding these challenges makes the next part even more interesting. Let’s dive into how AI agents actually work, how they adapt and manage to overcome these hurdles.

The core workflow of AI agents

Understanding how AI agents work starts with breaking down their structured workflow. AI agents operate through a process that enables them to interact with their environment, process information, and execute intelligent actions.

The key stages include:

1. Perception (data collection)

AI agents gather data from various sources, such as system logs, APIs, sensors, or user inputs. The accuracy and relevance of this data directly impact the agent’s ability to make informed decisions.

Example: AI agents like Bito’s AI Code Review Agent scan your code file, detect the programming language, and identify context from surrounding functions to provide relevant code suggestions.

2. Data processing and understanding

Once data is collected, the agent processes it using either rule-based logic or machine learning models. Rule-based agents follow predefined conditions, while AI-powered models detect patterns, anomalies, and relationships to extract meaningful insights.

Example: Bito’s AI Code Review Agent analyses pull requests, identifies security risks and inefficiencies, and suggests improvements based on best practices.

3. Decision-making

AI agents determine the best action based on algorithms. Some operate on fixed rules, executing tasks according to explicit conditions, while others adapt using past data. More advanced agents improve decision-making over time through reinforcement learning.

Example: Bito Wingman plans complex tasks, such as modifying a function based on a Jira ticket. It breaks down the request, understands dependencies, and ensures changes align with project requirements.

4. Action execution

After selecting an action, the agent executes it autonomously or with user confirmation. This could involve applying code changes, updating documentation, triggering external workflows, or generating recommendations for review.

Example: Bito Wingman can update a build script, refactor legacy code, and push commits directly to a repository, saving developers hours of manual work.

5. Learning and adaptation

AI agents refine their behavior over time using feedback loops. This learning happens through supervised feedback, reinforcement mechanisms, or real-time pattern recognition, allowing them to improve accuracy and adapt to evolving requirements.

Example: Bito’s AI Code Review Agent continuously refines its suggestions based on user feedback, ensuring that future reviews align more closely with a team’s specific coding style and preferences.

AI agent architectures

The architecture of an AI agent determines how it processes data, makes decisions, and interacts with its environment. Different architectures suit different tasks, depending on whether an agent needs to react instantly, plan its actions, or balance both approaches.

1. Reactive agents

Reactive agents operate solely based on current inputs from their environment. They do not store past experiences or anticipate future outcomes. Instead, they follow a condition-action rule set, meaning they react the same way to identical inputs every time.

This makes them highly efficient for tasks that require fast decision-making in well-defined environments. However, their lack of memory prevents them from adapting to changing conditions or improving their behaviour over time.

2. Deliberative agents

Deliberative agents take a more structured approach by maintaining an internal representation of the world. Instead of acting immediately, they evaluate different possibilities before selecting the best course of action.

This allows them to handle more complex tasks, as they can analyse past interactions, predict outcomes, and make informed decisions. However, deliberative agents require significant computational resources to process information, making them slower than purely reactive agents.

3. Hybrid agents

Hybrid agents combine the strengths of reactive and deliberative architectures. They can respond to immediate stimuli when necessary while also using stored knowledge and planning capabilities for complex decision-making.

This makes them well-suited for dynamic environments where both short-term reactions and long-term strategies are needed. Hybrid agents often incorporate layered architectures, where lower layers handle real-time responses and higher layers manage long-term planning.

AI agents are further classified based on their decision-making complexity and learning capabilities. To explore these types in detail, check out our Types of AI Agents guide.

Learning and adaptation

Not all AI agents are capable of learning. While reactive agents operate on static rules, deliberative and hybrid agents evolve through continuous learning. This is how AI agents improve over time:

1. Supervised learning: Agents improve by learning from labelled examples. For instance, an AI code reviewer refines its suggestions by analysing feedback on past recommendations.
2. Reinforcement learning: Agents learn through trial and error, optimizing decisions based on rewards or penalties. This is common in AI systems that automate strategic planning.
3. Self-learning and fine-tuning: Some agents adjust their internal models dynamically, learning from real-world usage patterns without requiring explicit retraining.

From fixed rules to adaptive intelligence

• Rule-based agents operate with predefined logic, requiring manual updates to change behaviour.
• Adaptive agents analyse past interactions, refine decision-making, and self-correct errors over time.

This ability to learn is what separates simple automation from truly intelligent AI systems.

Conclusion

AI agents operate through structured workflows, intelligent architectures, and adaptive learning mechanisms to automate complex tasks. Whether they react instantly, plan decisions, or continuously learn, these agents form the foundation of AI-powered automation across industries.

To select the right AI agent architecture for your project:

• Choose reactive agents for time-sensitive applications requiring real-time responses, like cybersecurity monitoring.
• Opt for deliberative agents in scenarios that require complex decision-making and planning, like autonomous navigation.
• Use hybrid agents when balancing immediate responses with long-term planning, such as in customer support systems.