AI Agents
What they are and why they matter
AI agents are systems powered by artificial intelligence (AI) that perform complex tasks or make informed decisions with varying human involvement. They surpass traditional chatbots and large language models (LLMs) by integrating data and advanced analytics tools to be more adaptable and capable of complex reasoning across industries.
Traditional AI to AI agents
AI systems have evolved from simple rule-based programs to intelligent, adaptive models capable of complex reasoning – transforming how software is developed and deployed.
Why is agentic AI important?
Agentic AI helps organizations move from generating insights to acting. Unlike traditional AI systems that primarily predict, generate or recommend, agentic AI systems can reason about next steps, use tools and take actions across workflows with varying levels of human oversight. As organizations move beyond experimentation, their focus is on deploying AI that operates within a business context, adheres to governance standards and delivers measurable outcomes.
This distinction makes agentic AI especially relevant in today’s market:
- The demand for automation and productivity. Organizations are under pressure to reduce costs, increase human productivity and scale operations quickly. Automation through AI agents is a powerful solution, capable of handling both repetitive and more complex functions.
- Enhanced decision-making. Making informed decisions is crucial to staying competitive. AI agents help organizations analyze vast amounts of data, extract insights and make real-time decisions that can improve operational efficiency, customer experience and profitability.
- Dynamic environments and adaptability. The pace of change in business environments requires systems that can quickly adapt. Agentic AI excels at operating in dynamic environments, adapting its plans and actions based on new information, feedback and changing conditions.
Agentic AI can improve productivity by automating multistep workflows, coordinating tasks and supporting faster decisions – enabling humans to focus on higher-value work.
Agent autonomy can be thought of along a spectrum, characterized by these levels:
- Level 1: Human directed. Agents recommend or prepare actions while humans initiate or approve consequential actions.
- Level 2: Human supervised. Agents execute defined tasks within established boundaries, with ongoing human oversight.
- Level 3: Semiautonomous. Agents plan and execute multistep workflows independently within predefined guardrails, escalating exceptions or higher-risk decisions.
- Level 4: Highly autonomous. Agents pursue goals and execute workflows with minimal human intervention, subject to predefined policies, controls and monitoring.
Of course, giving an agent full ownership can introduce risk. There are several factors to help determine the appropriate level of autonomy, including the level of risk, industry use and regulatory environment. In addition, these factors should be considered:
- Ease of reversing agent decisions.
- Impact/severity if the agent is wrong.
- Sensitivity of the data involved.
- Permissions and systems the agent can access.
- Predictability and observability of the task.
- Ability to detect failures.
- Need for human judgment.
- Financial, customer and safety consequences.
With inherent risk, it’s more important than ever to build AI systems with governance, particularly for highly regulated industries where transparency, accountability and human oversight are nonnegotiable. The most successful agentic AI systems will keep humans in the loop, combining human judgment with AI-driven speed and scale.
How are AI agents used in today's world?
AI agents are shaping industries by enabling automation, improving efficiency and enhancing customer interactions. Explore more resources:
Agentic AI explained
What is agentic AI? As a leading trend in tech, many people are wondering what it is and how it will affect organizations. Several factors make agentic AI especially relevant today – including the need for automation, improved decision-making and greater productivity. However, there are risks and concerns with autonomous AI that bring responsible AI to the forefront. Listen to Marinela Profi of SAS explain agentic AI, real-world use cases and the benefits and risks.
How are industries using AI agents?
AI agents are revolutionizing multiple industries by improving efficiency, decision-making and customer experiences.
AI agents operate on a spectrum of decision-making – from fully autonomous actions to human-guided oversight. The key is balancing complexity, speed and determinism to ensure AI delivers the right outcomes at the right time. Bryan Harris Chief Technology Officer SAS
How do AI agents work?
AI agents aren’t a one-size-fits-all solution. Instead, they operate on a spectrum of autonomy, spanning two different decision loops:
- Human out of the loop. Operating autonomously, making real-time decisions without human intervention.
- Human in the loop. Engaging in human oversight as needed, assisting but not entirely replacing human decision-making.
Each of these decision loops comes with key considerations, including:
- Complexity of the problem. Lower-complexity problems are often best handled autonomously, whereas solving complex challenges often benefits from human oversight.
- Determinism. Systems operating independently must deliver consistent, repeatable outcomes. Those working alongside humans can allow for more exploratory or adaptive results.
- Speed of decision-making. Real-time use requires millisecond-level responses, while nuanced scenarios may afford more time for analysis.
- Accuracy and AI governance. The level of automation varies depending on the accuracy required and the need for regulatory oversight in industries like banking, insurance and health care.
What is AI agent governance?
AI agent governance is the set of policies, controls and oversight practices that guide how autonomous AI systems are designed, deployed, monitored and improved. Governance is critical for AI agents because autonomous systems can recommend actions, initiate workflows and affect business outcomes.
AI agent governance ensures AI agents operate ethically, legally and transparently. Effective governance requires AI systems to be accountable, explainable and auditable while remaining under appropriate human control. Some of the key governance pillars include:
- Human-in-the-loop oversight.
- Explainability and traceability.
- Auditability and lineage.
- Testing and evaluation.
- Ethical and bias auditing.
- Regulatory and business safeguards.
- Identity and access management.
- Tool and action permissions.
- Human interaction and escalation.
These measures help organizations mitigate risks related to discrimination, financial losses, misinformation and cybersecurity threats. AI governance should be a standard business discipline, much like cybersecurity and data privacy governance are today. Because agents can access data, invoke tools and take actions, agent security must extend beyond protecting the model itself to securing the entire agentic system.
Emerging regulations, industry standards and dedicated AI governance boards will help organizations deploy autonomous systems responsibly. Rather than slowing innovation, governance will enable organizations to build safer, smarter and more trustworthy AI agents that can deliver business value while maintaining trust.
The risks of improper governance
An AI agent is more than a language model. An enterprise agent typically combines a model with instructions, business context, memory or retrieved knowledge, tools and APIs, orchestration and governance controls. These components allow the agent to reason about a goal and take actions within defined boundaries.
Without proper governance, AI agents could become a liability. Here are a few unwanted outcomes:
- Decision risk. Agents may make inaccurate, biased or inappropriate decisions.
- Operational risk. Agents may execute incorrect actions, trigger cascading workflows or cause unintended business consequences.
- Security and privacy risk. Agents with access to tools, credentials and sensitive data can create new attack surfaces, including prompt injection, unauthorized tool use and data leakage.
- Compliance and legal risk. Agent behavior may violate regulations, organizational policies or contractual obligations.
- Reputational risk. Incorrect or inappropriate agent-generated communications or actions can damage customer trust and brand reputation.
AI governance works best when it’s treated as a strategic advantage, not a burden. It’s a living system that evolves in relation to technologies, organizations and the world around it.
AI agents in practice
AI agents operate through five key components: Perception, cognition, decisioning, action and learning.
What is retrieval-augmented generation?
Retrieval-augmented generation, or RAG, is a technique that helps AI systems generate more accurate responses by grounding them in trusted external data. Instead of relying solely on information learned during training, a retrieval-augmented generation system first searches relevant documents, databases or knowledge sources and then uses the retrieved information to help generate an answer. This allows the model to provide more current, accurate and context-specific responses.
In agentic AI systems, RAG helps agents access the right enterprise knowledge before making recommendations or taking action. A typical RAG workflow has two main stages:
- Retrieval – when the system searches or queries relevant knowledge sources, often using semantic search, vector embeddings, keyword search, structured queries or a combination of techniques.
- Generation – when the language model receives both the user's question and the retrieved content as input, enabling it to create a response grounded in the retrieved evidence rather than relying entirely on its internal knowledge.
RAG is widely used in enterprise search, customer support, knowledge management and agentic AI because it can reduce AI hallucinations and make responses more trustworthy. Organizations can connect RAG systems to internal documents, policies, manuals and databases, allowing users to receive answers based on authoritative sources. However, the effectiveness of RAG depends heavily on the quality of the retrieval process, the relevance of the underlying data and how well the retrieved information is integrated into the model's response.
RAG is one of several grounding mechanisms. For agentic systems, grounding can extend beyond RAG. Agents may access databases, APIs, analytical models, business rules and enterprise applications through tools. This allows an agent not only to retrieve knowledge, but also to obtain the current state, perform analysis and take governed actions.
The role of the environment
An AI agent doesn’t operate in a vacuum – it interacts with systems, people and processes that shape its decisions. The environment provides the context and feedback that influence perception, cognition and actions. A well-defined environment helps the agent make better decisions and continuously improve.
AI agents versus agentic AI
AI agents and agentic AI have been used interchangeably, but they have distinct meanings. Read on to learn the difference.
AI agents are specific, task-oriented AI systems designed to perform repetitive tasks on behalf of a user. These agents can automate processes, analyze data and make decisions based on predefined rules and machine learning algorithms. They interact with their environment, systems, people and processes to shape their decisions and actions.
Agentic AI refers to intelligent systems or "agents" that exhibit a higher level of autonomy and decision-making capabilities. These systems can make decisions, carry out tasks, and learn from their interactions within a given environment. Agentic AI is a broader framework that uses multiple AI agents to achieve complex goals autonomously. It involves a combination of AI, automation and human oversight to redefine how businesses operate, make decisions and interact with technology.
In short: AI agents are the tools. Agentic AI is the system that uses those tools to think, decide and act on its own. Not all AI agents are agentic – true agentic AI requires a higher level of autonomy and coordination. But full autonomy alone isn't enough for enterprise use. That’s where thoughtful orchestration, human oversight and trust come in.
SAS® Intelligent Decisioning
SAS Intelligent Decisioning empowers organizations to automate and manage complex decisions with speed and precision. Combining business rules management, real-time event detection, decision governance and advanced analytics enables enterprises to make data-driven decisions at scale. From personalized marketing and next-best actions to credit services and fraud prevention, it streamlines real-time customer interactions and operational workflows.
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