Probabilistic Power: What Makes AI Agents Different from Traditional Automation?
I have found it challenging to fully comprehend the concept of an agent within the context of AI solutions. For instance, does incorporating a large language model into a workflow qualify it as an agent?
While some argue that it does, this raises questions about whether we are advancing towards genuinely agentic AI solutions capable of transforming firms into frontier firms.
It appears that we proceeded to discuss building, optimizing, managing, and securing agents without first establishing a clear understanding of what an agent is within the context of artificial intelligence. Consulting colleagues or experts in AI regarding the definition of an AI agent may reveal that there is no universally accepted interpretation. Even prominent technology firms in the field of artificial intelligence lack consensus on the definition of an AI agent or on what does not qualify as one.
An AI agent offers substantial potential for enhancing productivity. However, without a shared understanding among stakeholders, we risk missing out on the full advantages that this transformative technology can deliver.
This article seeks to provide a comprehensive overview of AI agents by examining their definitions, analyzing key distinctions, outlining shared characteristics, and highlighting several prominent examples within the industry.
The objective of this article isn’t just to arrive at a single unified definition, but to ensure that leaders remain grounded in the promises that AI Agent brings, of becoming the frontier firm.
Definition of an Agent
Let us begin by defining an agent, a general-purpose agent.
As per Investopedia, an agent is a person or entity authorized to act on behalf of another individual or organization, often in business, legal, or other formal contexts. The key concept is representation—a principal entrusts the agent to perform tasks, make decisions, or negotiate transactions, either broadly or with specific limitations Source
According to this definition, an agent undertakes decision-making and other responsibilities on behalf of another individual or organization. It is important to highlight these essential components for further discussion.
Definition of AI Agent
Next, let us review how leading technology and analysts define AI agents.
Microsoft
Systems that make decisions, invoke tools, and participate in workflows—sometimes independently, sometimes collaboratively. Agents differ from assistants by completing goals with less direct prompting and orchestrating tasks autonomously. source
OpenAI
Systems that intelligently accomplish tasks, from simple workflows to complex objectives. Built from composable components (models, tools, knowledge/memory, guardrails, orchestration) to act independently, solve problems, use tools, and adapt behavior. source
Anthropic
A single system capable of reasoning through any task independently, planning and iterating, directing its own tool usage, and controlling task execution with flexibility. source
Gartner
An autonomous or semiautonomous software entity that uses AI to perceive, decide, act, and achieve goals in digital or physical environments. Supports or augments users by operating with autonomy to pursue goals. source
Salesforce
Goal-oriented, autonomous AI assistants that perform tasks and business interactions by initiating and completing sequences, handling conversations, adapting actions, and escalating as needed for security and ethics. source
The following is my analysis of these definitions:
· Microsoft distinguishes between agents and assistants. This distinction is relevant because Microsoft provides M365 Copilot, which serves as an assistant, along with tools and platforms for developing, operating, and managing agents.
· OpenAI emphasizes that agents act autonomously and adapt their behavior.
· Anthropic is similar to OpenAI, but it emphasizes independent task execution. According to their blog post, Anthropic distinguishes between workflow and agent: workflows follow a predefined code path, while agents determine their own processes dynamically.
· Gartner notes that an agent may function in either digital or physical environments. For example, a self-driving car is considered an agent operating in a physical environment.
· Salesforce highlights that an agent could escalate, presumably to a human, for security and ethics.
A common theme across the definitions is that an agent operates autonomously or independently. Autonomy and independence are often considered key characteristics of an AI agent, distinguishing agents from other types of software such as workflows and microservices.
Key characteristics - Autonomous
This discussion will focus on autonomy. Autonomy means possessing the ability or right to make one's own decisions, act independently, and govern oneself without external control or interference. Autonomous entities determine their actions and direction freely, based on internal reasoning and goals.
In the context of AI Agents, autonomy refers to an agent’s ability to make decisions and take actions without direct human intervention or constant oversight.
For instance, in a workflow where expense reports are routed to a human approver. An AI agent could autonomously make approval decisions or supply relevant context about the expense along with a recommendation for approval or rejection to the human reviewer.
Autonomous execution brings another factor for consideration when compared to traditional workflows – probabilistic vs deterministic output
Probabilistic vs Deterministic output
AI Agents are considered probabilistic because their outputs can vary for the same input, relying on models, statistical inference, and randomness to make decisions—unlike traditional deterministic workflows, which always produce the same result from the same input based on fixed rules.
This behavior of probabilistic output is like how a human would analyze certain inputs and decide. For instance, if multiple scientists are reviewing a research paper, each one would have a unique perspective based on their individual experiences and expertise.
Deterministic approaches excel in structured, critical contexts needing consistency and explainability, while probabilistic agents thrive in situations demanding adaptability, learning, and nuance amid uncertainty
Examples of AI Agents
AI Agent
Autonomy
Microsoft Researcher Agent (within Microsoft 365 Copilot)
Automates complex, multi-step research workflows by leveraging advanced AI reasoning models. It autonomously extracts key insights from massive internal and external data sources, integrates data from third-party platforms (like Salesforce, ServiceNow), and collaborates with other specialized agents to deliver comprehensive, context-aware insights. Demo video
Siemens Industrial Automation Agent
Siemens’ AI agents for industrial automation demonstrate autonomy by proactively executing complete workflows across the industrial value chain, moving beyond traditional query-based assistants to orchestrate multi-stage processes independently. These agents utilize a sophisticated orchestrator to coordinate specialized agents that understand user intent, adapt through continuous learning, and collaborate with both internal and third-party tools as needed. Source
DHL Enhanced Customer Solutions AI Agent
DHL Supply Chain recently announced its partnership with Boston Consulting Group to deploy two generative AI applications aimed at enhancing data management and analytics. This shift is intended to improve how the company assesses proposals and develops customer-specific logistics solutions Source
Microsoft Discovery (Enterprise Agentic R&D Platform)
A platform enabling collaboration with a team of specialized AI agents performing research, hypothesis testing, and iterative learning in R&D processes. Researchers configure agents with domain knowledge and processes, allowing them to collaborate autonomously to accelerate scientific discovery. Source
SharePoint agents:
SharePoint agents can analyze documents and answer questions about any site or library where users have permission, using natural language. Scenario video 1, video 2
Anthropic: Multi-agent research system
Anthropic’s multi-agent system (powered by Claude) is deployed in research, software development, and customer support. For example, enterprises use it for literature review, knowledge discovery, coding agents for GitHub issue resolution, and handling open-ended customer support scenarios that require both conversational flow and programmatic actions. Source
These cases illustrate autonomous agent capabilities like independent task planning, decision-making, tool use, and adaptive learning within complex enterprises and SaaS environments, exemplifying what defines agentic AI solutions today.
Summary
Agentic AI solutions show autonomy by independently perceiving environments, reasoning through complex tasks, and making decisions to achieve goals with minimal human oversight. They act flexibly and learn continuously, enabling examples such as autonomous vehicles, customer service bots, intelligent document handlers, and complex research assistants to operate effectively in dynamic real-world contexts.
This autonomy differentiates agentic AI from traditional AI tools by providing decision making and task completion capabilities, augmenting human behavior.