What Are AI Agents? Defining Their Role, Capabilities, and Future

AI agents are being hailed as the next big advancement in artificial intelligence, but there’s a catch: no one can quite agree on what they actually are.

At their core, AI agents are intelligent software systems that can perceive their environment, make decisions, and take actions to complete tasks—often with little to no human involvement. Think of an AI agent as a supercharged virtual assistant that doesn’t just answer questions but actively takes action across different systems to get things done.

For example:

• Perplexity AI recently released a shopping agent that helps users find holiday gifts.

• Google’s Project Mariner can book flights, find recipes, and shop for household items.

While the idea seems straightforward, the lack of consensus among tech leaders has led to confusion over what AI agents actually do—and what they could become.

A Shared Concept, Different Visions

Even among industry giants, there’s no single definition of an AI agent. Instead, perspectives vary depending on the tool’s purpose:

• Task-Based Assistants: Google sees AI agents as task-focused helpers. For example, they might assist developers with coding, help IT professionals troubleshoot logs, or guide marketers in creating a color scheme.

• Digital Co-Workers: Asana envisions AI agents as "extra employees"—tools that autonomously complete assigned tasks, much like a highly efficient colleague.

• Customer Experience Tools: Sierra, a startup founded by Bret Taylor and Clay Bavor, focuses on agents that enhance customer service by solving more complex problems than traditional chatbots.

Rudina Seseri, managing partner at Glasswing Ventures, sums up the general concept:

“An agent is an intelligent software system designed to perceive its environment, reason about it, make decisions, and take actions to achieve specific objectives autonomously.”

These agents rely on AI/ML techniques—like natural language processing (NLP), machine learning (ML), and computer vision—to operate independently, often alongside other agents or human users.

What Makes AI Agents Different?

At first glance, AI agents may sound similar to tools like ChatGPT or virtual assistants like Siri. However, there’s a key difference:

• Traditional AI tools: React to commands and questions.

• AI agents: Go further by reasoning, taking action, and solving tasks across multiple systems—like booking a trip, troubleshooting software, or building a report.

Imagine telling an AI agent to "plan a vacation to Italy." The agent could:

• Find flights and hotels.

• Compare prices and book the best options.

• Build an itinerary and sync it to your calendar.

This ability to act across systems is what sets AI agents apart—though it’s also where they run into challenges.

The Challenges of Building AI Agents

While AI agents hold great promise, experts agree that building them is incredibly difficult. The obstacles include:

• Crossing Systems: Many legacy systems lack APIs (tools that let software communicate), making it hard for AI agents to access and work with them.

• Reasoning Limitations: Current large language models (LLMs), like GPT-4, aren’t yet capable of complex, multi-step reasoning needed for fully autonomous agents.

• Multiple Models: Experts believe the most effective agents will combine multiple specialized models, not a single LLM. Fred Havemeyer, head of U.S. AI and software research at Macquarie, explains:

“The most effective agents will likely be collections of multiple models with a routing layer that sends requests to the most effective agent and model.”

Why AI Agents Are Still a Work in Progress

For AI agents to operate autonomously, as envisioned, several advances need to happen:

• Improved Reasoning: AI must become better at breaking down abstract goals into logical steps.

• Infrastructure: Building a dedicated AI agent tech stack to support performance, scale, and reliability.

• AI Evolution: Progress in areas like GPU performance, model quality, and AI frameworks.

Aaron Levie, CEO of Box, remains optimistic:

“There are multiple components to a self-reinforcing flywheel that will dramatically improve what AI agents can accomplish in the near and long term.”

However, not everyone shares this optimism. MIT robotics pioneer Rodney Brooks warns that AI advancements may not come as quickly as some expect:

“When a human sees an AI system perform a task, they immediately generalize it to things that are similar and make an estimate of the competence of the AI system...and they’re usually very over-optimistic.”

The bottom line: We aren’t there yet. AI agents are still in their infancy, and solving the challenges of multi-system access, reasoning, and true automation will take time.

The Future of AI Agents

Despite the hurdles, experts agree that AI agents represent a critical step forward in AI development. David Cushman of HFS Research explains the goal:

“It’s where AI operates independently and effectively at scale. Humans set the guidelines, the guardrails, and apply multiple technologies to take the human out of the loop — when everything has been about keeping the human in the loop with GenAI, so the key here is to let the AI agent take over and apply true automation.”

Ultimately, the vision is for agents to handle tasks completely autonomously. Fred Havemeyer, head of U.S. AI and software research at Macquarie US Equity Research, describes this future:

“As I’m thinking about the future of agents, I want to see and I’m hoping to see agents that are truly autonomous and able to take abstract goals and then reason out all the individual steps in between completely independently.”

While this future hasn’t arrived yet, the groundwork is being laid. As AI models improve and infrastructure catches up, we’re likely to see increasingly capable agents that can handle more complex, real-world tasks.

Key Takeaway

AI agents are emerging as a transformative technology, with the potential to automate tasks and streamline workflows like never before. While challenges remain—especially around reasoning, infrastructure, and multi-system access—the promise is clear: a future where AI agents act autonomously to accomplish tasks on our behalf.

For now, we’re in the early stages of this journey, but progress is being made. Whether they’re digital co-workers, customer experience tools, or task-based assistants, AI agents are evolving to handle more—and they’re only just getting started.

Editor’s Note: This article was created by Alicia Shapiro, CMO of AiNews.com, with writing, image, and idea-generation support from ChatGPT, an AI assistant. However, the final perspective and editorial choices are solely Alicia Shapiro’s. Special thanks to ChatGPT for assistance with research and editorial support in crafting this article.