AI agents: capabilities and use cases

Advanced capabilities of AI agents

The efficient operation of AI agents depends on several core capabilities, which generally fall into the following categories.

Perception

AI agents gather, interpret, and process different types of data, including text, video, audio, and images. They can understand human language input using natural language understanding (NLU) and convert it into text, processing information in real time.

Reasoning

In AI agents, reasoning is defined as the ability to analyze information within specific contexts and make decisions based on perception, logic, and memory. A multistep reasoning approach allows agents to break down complex tasks into small steps, making the process more manageable.

Task planning and execution

Agents also use a multistep approach in planning. An AI agent can prioritize and establish a sequence of steps for task completion and implement these steps without human intervention to achieve a specific goal.

Autonomy and learning

Autonomous task execution is an inherent feature of advanced AI agents, which perform tasks and make decisions independently and can operate in dynamic environments. This means they can adapt to changes and improve using iterative feedback techniques and dynamic learning—acquiring new knowledge or skills from data, experience, and interactions with other systems and humans, and adapting to their environment.

Interaction and communication

Agentic systems can interact with each other, other systems, and humans to fulfil tasks. Communication allows them to share data, make more informed decisions, and refine over time.

Challenges of AI agent integration and how to deal with them

If you plan to use AI agents, there are a few important things to keep in mind. Their autonomous operation can raise safety and bias concerns, which can be addressed by restricting access to essential data and incorporating human oversight, especially for high-stakes decisions. Here’s a breakdown of common challenges and solutions.

Hallucination risks

Built on LLMs, AI agents can generate inaccurate or fabricated information, which can be dangerous, especially in domains where factually correct data is critical, such as healthcare and finance. This problem is often the result of inadequate or biased training data.

To prevent hallucinations, AI models should be trained on relevant, limited data. Fact-checking pipelines and human-in-the-loop (HITL) oversight are crucial, along with using specific templates to guide model responses.

Task misalignment or overreach

When AI agents don’t fully capture human intent, they may perform tasks they weren’t designed for or misinterpret goals, leading to unexpected behavior or even harm.

Companies can mitigate this problem by defining goals precisely and including HITL systems, allowing humans to intervene and control agents. Reinforcement learning with human feedback (RLHF) helps align LLMs with nuanced objectives, teaching them through examples.

Data privacy and security concerns

AI agents often handle vast amounts of sensitive data, especially in fields like healthcare and finance, making them vulnerable to security threats like unauthorized access, data breaches, and cyberattacks. To ensure user adoption of these systems, data protection is crucial.

The key to secure AI agents is regulatory industry standards and requirements, such as GDPR, CCPA, and HIPAA. They provide companies with clear guidelines, best practices, and recommendations on handling sensitive data, ensuring control, security, and compliance.

Ethical and social bias

Some agentic AI errors are related to unfair or discriminatory representation of certain groups of people. This often happens due to biased training data and flawed assumptions in algorithms and can be dangerous because it amplifies existing inequalities and institutionalizes injustice.

Dealing with bias in agentic AI systems requires using inclusive and representative training data, implementing regular audits and feedback loops, and involving diverse development teams. It’s crucial to establish ethical guidelines and transparent policies, controlling how AI systems are trained, tested, and used.

Resources and cost

Implementing AI agents requires significant resources, including high-quality data, high-performance hardware, skilled engineers, initial investment, operational costs, and maintenance overheads.

Businesses can adopt several practices to reduce expenses and manage resources effectively. First, it’s important to partner with an experienced AI development company, which is usually cheaper and quicker than building a full in-house team. Outsourced experts not only help you kick-start your agentic AI project, they create a custom roadmap for the entire process, find suitable tools for your domain and requirements, and provide post-launch support.