What are AI agents? A detailed guide for digital leaders

As their capabilities grow and business demand increases, agents are quickly moving from experimental to essential.

So, what’s driving the rise of autonomous AI agents?

• Agentic AI provides a shift from task-specific tools to intelligent systems that can reason, determine intent, and take persistent action toward goals on their own.
• This evolution is powered by LLMs, which communicate and understand natural human language (and not machine) languages.
• There’s a growing need for automation that’s adaptive, contextual, and event-driven, and not just rule-based.

They typically follow an agentic loop of:

• Perception (gathering input)
• Planning and reasoning (determining intent algorithmically)
• Action (executing via tools or APIs)
• Reflection and adaptation (learning from outcomes)

Here’s how they function step by step:

1. Perception and planning

AI agents start by gathering input. This may include:

• Natural language from a user prompt
• System or application data (for example, logs, customer records, sensor data)
• Real-time updates from APIs or webhooks

They then use LLMs and embedded logic to:

• Interpret intent.
• Set subgoals or deconstruct tasks.
• Select the best tools for execution.

2. Understanding and evaluation

LLMs provide the reasoning layer, helping agents:

• Understand context
• Evaluate possible actions
• Match decisions to its objectives

Machine learning models may also be used to evaluate outcomes, compare alternatives, or learn from previous patterns.

3. Action

Once a plan is formed, agents interact with digital systems through tools, algorithms, APIs, and external services. As a result, one agent can take all four of the following actions:

• Sending personalized emails based on CRM data
• Updating database records after customer interactions
• Triggering workflows in an application, like resetting a password
• Orchestrating app deployment pipelines

4. Iteration and adaption

Autonomous AI agents improve through feedback. This includes:

• Adjusting strategies after failed actions
• Learning from user responses
• Revising goals in light of new information

This adaptive behavior is what makes agents context-aware, persistent, and flexible.

Types of AI agents

There are several types of AI agents, each with different levels of sophistication and purpose. Below are the most common:

• Model-based agents
  • Use an internal representation of the world
  • Can simulate outcomes and take action based on environmental knowledge
  • Example: An agent that navigates a building using a floor map
• Goal-based agents
  • Proactively address specific goals rather than simple reactions
  • Use decision-making frameworks to plan actions to achieve outcomes
  • Ideal for dynamic environments where end goals matter more than predefined rules
• Utility-based agents
  • Evaluate multiple options based on a utility function
  • Choose the action that maximizes long-term value or reward
  • Often used in autonomous trading
• Learning agents
  • Improve over time using ML, often through reinforcement or supervised learning
  • Learn from success/failure, user feedback, or historical data

Single agents vs. multi-agent systems

AI agents differ in how they operate: either as single, focused entities or as collaborative multi-agents. This dictates how complex tasks are managed and scaled across the enterprise.

Single agents operate independently to complete a defined task. An example is a coding assistant that helps developers write optimized functions.

Multi-agent systems are multiple agents working together, each with a specialized role, while coordinating actions. Think about a network of agents managing a logistics chain, each optimizing its own route while coordinating deliveries.

Real-world AI agent use cases

AI agents have the potential to drive digital transformation across businesses, improve customer experience, and streamline internal processes. Here are some examples of how AI agents can be used:

• Customer service: Agents optimize support operations by triaging issues, providing instant responses to common questions asked in natural language and escalate complex cases. This frees human agents to focus on critical customer interactions.
  
  The result: Reduced response times and improved customer satisfaction.
• Banking and finance: Autonomous agents detect fraudulent transaction patterns using vector and graph databases to understand intent, and ensure regulatory compliance through continuous monitoring.
  
  The result: Increased fraud detection accuracy and faster, more compliant customer onboarding.
• AI insurance agents: In the insurance industry, agents optimize operations, like claims processing, automate personalized quote generation (based on natural language input), manage policy renewals, and accelerate claims triage and verification.
  
  The result: Enhanced customer experience and higher efficiency.
• HR: AI agents help streamline candidate screening, personalize onboarding processes by answering natural language questions, and proactively monitor employee sentiment with natural language understanding.
  
  The result: More efficient hiring processes and improved talent retention.
• AI sales agents: For sales, agents provide personalized offer recommendations based on buyer behavior, automate outreach, schedule meetings, manage follow-ups, and keep CRM records updated.
  
  The result: Higher conversion rates and reduced sales cycle times.
• Healthcare: Agentic systems can monitor patient vitals, assist in recommending personalized treatment plans, answer clinician and patient questions in language they can understand, and automate essential administrative tasks.
  
  The result: Enhanced care delivery and improved operational efficiency.
• AI agents in software development: Intelligent agents accelerate the entire SDLC, automate CI/CD pipelines, optimize task allocation within development teams, and track project progress.
  
  The result: Faster releases, superior code quality, and truly empowered development teams—all core benefits for OutSystems customers.

Business value: Unlocking new possibilities

AI agents offer several business advantages. Agentic AI enables accelerated time-to-market by automating complex, repetitive, and often bottleneck tasks (for example, data processing or finding and fixing bugs), which significantly speeds up development and operational cycles. This translates into faster releases and quicker adaptation to market demands.

Moreover, agents drastically improve operational efficiency by reducing manual errors and optimizing workflows, ensuring consistency and freeing teams from repetitive and time-consuming tasks. This results in cost savings and higher productivity across different business functions.

Finally, the ability to use algorithms, patterns, natural language processing, and models to delegate task execution to intelligent agents means you can enable entirely new business models. These models can unlock opportunities for new services and revenue streams, differentiate your offerings, and scale operations without linear increases in headcount.

Developer value: Empowering teams with custom agents

For development teams, AI agent integration leads to increased development speed as agents actively assist with code generation, suggesting context-aware snippets, completing functions, and automating repetitive development tasks. They can also generate test cases and documentation, helping teams move quickly from idea to prototype.

Custom agents also reduce cognitive load by offloading monotonous and time-consuming aspects of coding and maintenance, freeing developers to focus on creative problem-solving, strategic architecture, and true innovation.

Ultimately, with routine tasks expertly handled by AI, your development teams are empowered to concentrate on high-impact strategic initiatives that truly move the business forward, leading to greater job satisfaction and a more innovative, agile culture.

Get started with AI agents and OutSystems

With 93% of organizations reporting they are already developing—or plan to develop—their own custom agents, it’s clear organizations are eager to tap into the limitless potential of agentic AI. However, implementing agentic AI and building AI agents both require specialized resources and can introduce new risks.

OutSystems Agent Workbench changes all that. It empowers IT to create custom agents that streamline operations, elevate experiences, and grow revenue, all on the AI-powered low-code platform made for enterprise innovation. It centralizes agent development and orchestration in a single platform, reducing redundancy, increasing oversight, and eliminating the risk of AI agent sprawl.

Agent Workbench enables you to configure a foundational layer of LLMs and data to fuel agent action. You can unify access to third-party and custom AI models as well as enterprise knowledge sources—both structured and unstructured—to help you accelerate development, scale AI solutions with ease, and future-proof your AI strategy.

With Agent Workbench’s AI agent orchestration features, you can coordinate multiple agents across workflows—whether working side-by-side, in sequence, in hierarchy, with or without humans in the loop—to drive new levels of business value and ROI. You gain centralized control, faster automation, and stronger compliance across all of your agentic systems.

AI agents frequently asked questions