What is an AI agent?

A single AI agent is an autonomous digital worker designed to complete one specific task from start to finish. A multi-agent system is a group of agents that coordinate with each other to tackle bigger, more complex goals.

A single AI agent can be powerful on its own. It can take in information, reason about what to do next, and then act on your behalf. This makes it great for well-scoped tasks like drafting a report, triaging an IT ticket, or updating a record in your CRM. These agents shine when the process is linear and contained—they move step by step until the job is done.

But business processes are rarely a single set of simple activities. Think about onboarding a new employee, resolving a complex customer issue, or closing a major sales deal. Each involves multiple decisions, systems, and types of expertise. Each requires a different set of information—for example, customer data or employee data. That’s where multi-agent systems come in. Instead of relying on one agent to do everything, you can create a team of specialized agents that collaborate—one pulling data, another analyzing it, another drafting communications, and another checking for compliance before anything goes out.

The difference is similar to hiring a single generalist versus building a cross-functional team. A single agent is efficient for targeted tasks, while a multi-agent system can manage complex, multi-step workflows that demand coordination, quality control, and adaptability. This team-based approach is becoming the model that unlocks enterprise-scale value.

Multi-agent frameworks—protocols that allow multiple agents to coordinate, share context, and divide tasks—are a key enabler for establishing and scaling multi-agent systems. These frameworks typically include capabilities for communication protocols, orchestration, conflict resolution, and shared memory, the things that agents need to collaborate efficiently and reliably. First emerging as open-source projects on platforms like GitHub, they are now being hardened for enterprise use, where they provide the foundation for scalable, team-based AI systems that can handle end-to-end business processes.

AI agents aren’t one-size-fits-all. Over time, researchers and developers have defined different categories of agents depending on how they perceive the world, make decisions, and act. Here are the main types:

Classical agent types

• Simple reflex agents: these agents respond directly to stimuli with pre-defined rules. They’re fast and efficient but limited to narrow use cases because they don’t consider context. Example: A spam filter that flags emails containing suspicious keywords or patterns.

• Model-based reflex agents: these agents maintain an internal state or “model” of the world, enabling them to act with context rather than relying only on immediate inputs. Example: A smart thermostat that uses current temperature plus a model of how a home heats and cools over time to adjust settings intelligently.

• Goal-based agents: these agents don’t just react—they explicitly represent goals and select actions that move them toward achieving those objectives. Example: A digital scheduling assistant that evaluates different meeting times to achieve the goal of “schedule a meeting for all attendees.”

• Utility-based agents: these agents weigh possible outcomes and choose the one that maximizes overall benefit or minimizes cost. Unlike goal-based agents, they can evaluate tradeoffs across competing outcomes. Example: A ride-hailing app that chooses the best route by balancing speed, fuel costs, and traffic conditions.

• Learning agents: these agents improve over time by analyzing feedback, adjusting strategies, and developing new capabilities without requiring explicit reprogramming. Example: A customer support chatbot that learns from user interactions and improves its answers based on feedback.

Modern extensions in agentic AI

• Multi-agent systems (MAS): instead of a single agent, MAS involves multiple agents that collaborate (or sometimes compete) to solve problems. This allows for specialization and emergent teamwork. Example: A set of AI research assistants where one agent retrieves documents, another summarizes them, and a third checks for accuracy before delivering a final report.

• Cognitive / BDI agents (Belief-Desire-Intention): this is an advanced model where agents explicitly represent what they believe, what they want, and what they intend to do. While rooted in academic research, this framework is influencing modern designs of reasoning-heavy agents. Example: Autonomous robotics systems that need to reason about conflicting goals like “conserve energy” vs. “complete the mission.”

Together, these categories illustrate the spectrum of intelligence and autonomy in agents—from simple reflex systems to advanced, learning, multi-agent environments. In practice, many modern frameworks combine features across these types—for example, using utility-based optimization inside a multi-agent system that also incorporates learning loops.

AI agents open the door to big opportunities, but they also bring new risks that organizations need to tackle head-on. The good news is that these challenges can be managed with the right mix of technology, governance, and human oversight.

Reliability and hallucinations: guardrails and human-in-the-loop mechanisms

Even the most advanced AI agents can misinterpret context or produce inaccurate results. This problem, often called “hallucination,” makes it risky to let agents run unchecked in critical workflows. To manage reliability, organizations need to build in safeguards such as retrieval-augmented generation (RAG), guardrails that restrict outputs to safe boundaries, and human-in-the-loop review for high-stakes decisions. By combining automated checks with human oversight, companies can increase trust while still reaping efficiency gains.

Security and compliance: data governance, policy alignment

AI agents thrive on data, but that creates exposure if governance isn’t strong. Sensitive information flowing through agents must be protected under data privacy frameworks like GDPR, U.S. Health Insurance Portability and Accountability Act (HIPAA), and emerging AI-specific regulations. Enterprises need clear policies for how agents access, store, and share data, along with transparent audit trails.

The most critical aspect of deploying agents is ensuring they operate within a governed platform tailored to the enterprise’s unique context.

Integration complexity: ensuring agents can work across legacy systems

Most enterprises don’t run on a clean tech stack, and that makes integration one of the toughest challenges. Agents must be able to navigate a mix of modern APIs, legacy platforms, and siloed data sources. Without careful planning, the result is broken workflows or duplication of effort. Successful implementations rely on orchestration platforms and connectors that bridge old and new systems, ensuring that AI agents can act consistently across the full enterprise environment.

AI agents are reshaping industries by streamlining processes, scaling data analysis, and freeing up human teams from repetitive tasks. With smarter automation and decision-making processes, they help organizations work faster, adapt to market shifts, and maximize data insights. Here’s how AI agents are driving change across sectors.

Customer service: agents that resolve queries autonomously across channels

Customer service is a clear win for AI agents. Instead of just responding, agents can now resolve issues end-to-end—everything from pulling up customer details to processing refunds or scheduling callbacks. AI agents move service operations from reactive support to proactive problem-solving, redesigning how workflows are structured rather than layering automation onto old processes.

AI agents have become a crucial component in the contact center alongside conversational and generative AI technologies. These are goal-driven entities capable of planning, adapting, and executing on behalf of users. Within the contact center, they are orchestrated alongside automation robots and people to perform work—navigating systems, making decisions, and executing tasks in real time.”

Scott Dresser, Growth Sales, Contact Center Expert, UiPath

Healthcare: taking the pain out of patient care

AI agents are revolutionizing healthcare by assisting with diagnostics, patient data management, treatment planning, and remote monitoring. For example, they’re helping doctors diagnose faster and more accurately and develop more tailored treatments. Beyond clinical support, they’re also handling administrative work like staff and patient scheduling. At one UiPath customer, AI agents have freed up 8 to 15 hours per week for clinical managers.

Sales and marketing: lead qualification and personalized outreach

AI agents are transforming sales funnels by analyzing CRM data, ranking leads, and even sending outreach that's tailored to each prospect’s context. In insurance, AI agents are creating personalized content and offers, helping insurers engage customers more effectively and increase conversion rates.

IT operations: auto-triaging incidents and triggering workflows

In IT, agents act like smart first responders to triage issues, propose root causes, and kick off remediation steps autonomously. In modern IT help desks, agents handle predictable tasks like routing tickets, escalating only when necessary.

Finance and banking: faster reporting, stronger compliance

AI agents are enabling smarter workflows in finance by autonomously handling routine tasks like invoice matching, reconciliation, and preliminary cash forecasting. In one study, AI agents achieved up to 80% faster cycle times in purchase order transaction processing and matching, while improving audit trail quality and compliance. This shift helps finance teams focus on high-value analysis and strategy rather than repetitive processing.

Manufacturing and supply chain management: more predictability and uptime

AI agents are transforming manufacturing and supply chains by making operations smarter, faster, and more efficient. On the production line, they predict maintenance needs to minimize downtime and keep things running smoothly. In fact, manufacturers that adopt multi-agent systems report up to a 30% reduction in unplanned downtime and significant gains in throughput and labor productivity. In the supply chain, they analyze IoT sensor data to catch issues before they become costly breakdowns and adjust routes, inventory, and schedules in real time to handle delays or shortages.