With the revolutionary abilities to make autonomous decisions, adapt to environmental changes and collaborate to solve complex problems, it’s safe to say that agentic AI is the new frontier of technology. AI agents are just a piece of the puzzle. Learn how to harness the power of artificial intelligence agents and transform your organization today.

What is an AI agent?

An artificial intelligence agent is a system designed to collect data, interact with its environment and perform self-directed tasks according to a set of predetermined goals. Using advanced natural language processing techniques and large language models, AI agents are able to intake user inputs and respond with autonomy. Their functions go far beyond simply answering questions, but AI virtual assistants and chatbots are popular examples.

Key principles of artificial intelligence agents:

  • Autonomy: AI agents operate independently, making decisions and fulfilling actions without the need for operator intervention.
  • Goal-oriented: AI agents break down high-level instructions into actionable steps and sequence plans directly leading to a desired final outcome.
  • Perception: AI agents continuously gather and interpret environmental data using sensors, streams and APIs to understand context and evolving variables.
  • Rationality: AI agents use logic and reasoning to evaluate which action will maximize the likelihood of successfully completing their assigned task.
  • Proactivity: AI agents anticipate future scenarios, predict disruption and initiate preventative actions before being explicitly prompted.
  • Continuous learning: AI agents analyze past experiences, data patterns and continuous feedback to refine problem-solving strategies and internal reasoning approaches.
  • Adaptability: AI agents dynamically adjust behaviors and responses when encountering any error or unexpected shifts in the environment.

What are the benefits of using AI agents?

It doesn’t matter whether you’re in healthcare, manufacturing, or financial services—any industry can utilize artificial intelligence agents to improve your internal operations and user experience. Since AI agents are trained to handle repetitive tasks, such as data entry or basic customer service, the models can be used to augment items on your human employees’ to-do lists. That way, your valuable staff has more attention and time to dedicate to innovative and strategic initiatives.

Organizations can also use AI agents to directly enhance the customer experience. In the digital age, users expect a certain level of engagement and personalization when it comes to interacting with businesses. Incorporating AI virtual assistants or learning agents into your online-facing systems to answer everyday queries or provide recommendations can lead to lasting consumer loyalty.

With AI agents, there’s no room for second-guessing. The proactive and predictive design of artificial intelligence agents enables leaders to make informed decisions, as seen in CGI Credit Studio, before strategizing their next move. By actively simulating scenarios, accessing external tools and going far beyond standard data analysis, agentic AI is able to provide recommendations and speed up timelines.

Agentic AI vs AI agents

When talking about artificial intelligence, it’s important to understand the lingo. While agentic AI and AI agents are intertwined, the two have different focuses, scope, autonomy and collaborative abilities.

AI agents, for example, are single software applications that are designed to autonomously execute a given task. Agentic AI describes a larger framework that plans and orchestrates multiple artificial intelligence agents to achieve a broader goal. The outcome your agentic AI system is attempting to achieve will directly depend on the programming of the individual agents.

Types of artificial intelligence agents:

  • Simple reflex agents
  • Model-based reflex agents
  • Goal-based agents
  • Utility-based agents
  • Hierarchical agents
  • Multi-agent systems
  • Learning agents

What is a simple reflex AI agent?

A simple reflex AI agent operates based on a set of predefined rules and its immediate data. Designed to handle simple, structured tasks that don’t require extensive training, such as resetting passwords, simple reflex AI agents are not able to respond to scenarios beyond a given condition or action rule. Since they run instantly and do not rely on complex reasoning, they are ideal for situations where safety, speed and predictability are more important than long-term adaptability.

What is a model-based reflex AI agent?

Model-based reflex agents, while similar to simple reflex models, have a more advanced decision-making mechanism. Instead of following one specific rule and seeing the task to completion, a model-based design will evaluate probable outcomes and consequences before making every decision.

By using supporting data, it builds and relies on an internal model of a perceived environment to support any actions taken. Self-driving cars, robotic vacuum cleaners and smart thermostats are perfect real-world examples of this type of artificial intelligence agent.

What is a goal-based AI agent?

Goal-based agents (sometimes referred to as rule-based agents) possess robust reasoning capabilities. The model compares evaluated environmental data with different approaches to ensure it can achieve the desired outcome along the most efficient path. If you have multi-step complex problems where a simple sequence of actions cannot be pre-programmed, this is the ideal AI agent design.

What is a utility-based AI agent?

Utility-based AI agents apply a complex reasoning algorithm to assist operators in maximizing their desired outcome. By comparing different scenarios and their values, risks and benefits, the artificial intelligence agent is able to offer the one that leads to the most reward. This approach is frequently used to offer dynamic pricing in ride-share apps, weight market volatility in financial trading tools and evaluate appliance loads in utility grids.

What is a hierarchical AI agent?

Hierarchical AI agents are an organized group of multiple agents arranged in a specific tier. Agents placed at higher levels decompose overarching tasks into smaller, more manageable assignments that are then handed down to lower-level agents. Each agent within the hierarchical system runs independently, sending progress reports to a supervising agent that collects the results and coordinates other components to ensure the goal is collectively achieved.

What is a multi-agent system?

Multi-agent systems (MAS) describe an environment where multiple agents interact with one another to accomplish shared objectives. Depending on the goals, these artificial intelligence agents can be designed in similar ways (homogeneous) or in differing structures (heterogeneous). MAS can be trained to collaborate or compete and are most effective in complex environments where localized control is unlikely, such as managing a delivery truck fleet in a supply chain.

What is a learning AI agent?

Learning agents use continuous sensory input and feedback mechanisms to learn from past experiences and improve their performance for future tasks. Unlike traditional AI programs that rely on fixed rules, learning agents are always adapting to improve.

How do AI agents learn and adapt?

AI agent learning refers to the technology’s ability to improve its performance over time. This is accomplished without human intervention, as the agent interacts with its environment, processes data and optimizes its decision-making to suit its pre-set goal. Agent learning is the driver of the impressive autonomy held by the technology.

It should be noted that not all artificial intelligence agents can learn. Some are programmed to be more passive and lack the fundamentals required to adapt to future tasks. If you want an enhanced and dynamic tool, look to learning agents to unlock the full potential of AI.

Learning agents typically have 6 definable features:

  • Sensors, which perceive the agent’s environment and send decision-making data.
  • Actuators, the means by which an agent interacts with its surroundings.
  • Performance element, which helps make informed decisions based on current knowledge.
  • Learning element, which adjusts and improves the knowledge depending on provided feedback and lived experience.
  • Critics, which evaluate, reward and penalize the actions.
  • Problem generator, which suggests exploratory actions to help the learning agent discover new strategies for further task endeavors.

5 types of AI agent learning:

  1. Supervised agent learning: Artificial intelligence agents train machine learning (ML) algorithms on labeled datasets, corresponding each input to a known output. This style is often used to build predictive models, such as AI virtual assistants or chatbots (a component seen in CGI Advantage), as it’s best for fine-tuning a general dataset for a specific domain or organization.
  2. Unsupervised agent learning: Artificial intelligence agents are encouraged to perform data analysis on unlabeled data to find patterns and structures without operator assistance. If you’re attempting to cluster customer behavior to improve marketing strategies or detect anomalies in cybersecurity settings, this is the agent you need.
  3. Reinforcement agent learning: Artificial intelligence agents focus on decision-making workflows, addressing sequential processes in uncertain environments where they define a policy and optimize for long-term goals. Unlike supervised and unsupervised learning agents, this model learns by trial and error, advancing through actions and rewards.
  4. Continuous agent learning: Artificial intelligence agents update their models continuously as they intake new information or encounter environmental changes to ensure they stay up to date for top efficiency. This type of learning agent is designed to ensure that a system can handle evolving tasks and challenges without forgetting previous knowledge.
  5. Multiagent learning: Artificial intelligence agents in this multi-operative system can either collaborate or compete. In cooperative learning, the agents share knowledge to achieve a common outcome. In competitive learning, agents refine their strategies by competing in specific settings.

What is AI agent communication?

When it comes to AI agents, communication is key. Any time artificial intelligence agents interact with each other, external systems or human operators, it can be called an act of communication.

Think of it like a team meeting: each autonomous agent will come to the table with the information that it alone has gathered from its environment. Once all the cards are put on the table, the agentic AI program can take the next steps towards the shared goal.

How do AI agents communicate?

While some AI technologies will only require one agent, there are others that require vast ecosystems to achieve a larger task. In these cases, the agents must work outside of their isolated environments and “talk” to one another. But how?

Artificial intelligence agents have a few different protocols and frameworks for communication, each with their own time and place. Agent-to-agent communication relies on a common language, making it so that agents crafted by different platforms and systems are able to discover each other and securely transfer context regardless of manufacturer.

AI agents can also read from and write to a common database, rather than sending direct messages, letting them monitor progress and pick up tasks where other agents left off without skipping a beat. In some cases, agents use a peer-to-peer style, where they converse, debate and validate ideas without a boss to direct them.

5 Common challenges for AI agents:

  1. Lack of standardized protocols: In some cases, the AI agent ecosystem can become fragmented, as different platforms may use unique dialects, schemas and structures. To combat these integration bottlenecks, developers build bespoke connectors to unite distant frameworks and standardize communication.
  2. Misinterpretation: Artificial intelligence agents sometimes misunderstand instructions due to poorly structured data, missing context or multi-step reasoning that goes beyond their design. Software developers mitigate this by adding frameworks with shared contextual memory pools and implementing strict input/output schemas.
  3. Latency: Since more complex agentic workflows require multiple tools, API calls and model invocations, there is a risk of mission-critical operations in the chain of dependencies grinding to a halt. To maintain service-level objectives and avoid synchronous lockstep queries, developers map different workflow tasks to optimized hardware alongside parallel execution.
  4. Security: Artificial intelligence agents interface with production environments and can fail to intercept dynamic threats, making them vulnerable to credential leakage, unauthorized tool execution and prompt injection. Implementing trust frameworks, better in-system security measures, strict intent analysis, zero-trust action verification and behavioral guardrails protects the agents from risk.
  5. Human-agent communication: The gap between human intent and AI agents is largely due to over-communication, a loss of transparency and over-relying on the model until failure. To provide clearer and more actionable intervention points, experts are advocating for “human-in-the-loop” oversight to help users safely guide and monitor the agent without fear of breakage.

Look for AI agents at CGI today

Whether you’re debating implementing a payment integrity solution like CGI Proper Pay in your organization, incorporating a new AI virtual assistant on your website or dreaming of building a complex multi-agent system to handle repetitive administrative tasks in your office, one thing is clear: artificial intelligence is the future. Connect with our team at CGI to find the right AI agent for your needs.