What Is an AI Agent?

Microsoft's own Cloud Adoption Framework puts it directly: an AI agent is "a flexible software program that uses generative AI models to interpret inputs, such as system events, user messages, or other agent messages, reason through problems, and decide on the most appropriate actions." The key contrast Microsoft draws is with traditional software: "Unlike traditional applications that rely on fixed rules, agents dynamically orchestrate workflows based on real-time context."

That distinction, fixed rules versus dynamic orchestration, is the whole definition in practice. A traditional feature runs the same branch of logic every time a given input arrives. An agent decides, at each step, which branch to take, which tool to call, and when it has enough information to stop. This is no longer a niche pattern: per Digital Applied's tracking of Gartner's Q1 2026 data, "80% of enterprise applications shipped or updated in Q1 2026 embed at least one AI agent," up from 33% in 2024.

What five components does an actual AI agent need?

Per Microsoft Learn's breakdown, five parts make up a working agent:

• Generative AI model: the reasoning engine that processes instructions and decides what to do.
• Instructions: the scope, boundaries, and behavioral rules that keep the agent from drifting outside its intended job.
• Retrieval: the grounding data the agent pulls from to answer accurately instead of guessing.
• Actions: the tools, APIs, or systems the agent can actually call to do something, not just describe something.
• Memory: state carried across steps or turns, so a multi-step task does not reset its own progress halfway through.

Drop actions and memory from that list and what remains looks much closer to a well-grounded chatbot than an agent: it can answer accurately, but it cannot act or track a task across steps.

How does an agent differ from a RAG (retrieval-augmented generation) system specifically?

The distinction Microsoft draws is about who decides the retrieval path. "Standard RAG applications follow a deterministic retrieval process to answer queries," always the same sequence: fetch, then generate. "AI agents use a generative model to decide which knowledge and tools to use at each step," which means the same question can take a different path through the system depending on context. That adaptability is the entire point of calling something an agent rather than a RAG pipeline, and it is also the source of the unpredictability discussed below.

What are the three levels of AI agent, and why does the level matter?

Microsoft Learn organizes agents into three tiers by autonomy: productivity agents (retrieve and summarize information for a human to act on), action agents (take a specific, bounded step inside an existing workflow, like updating a record), and automation agents (run a multi-step process end to end with minimal oversight). Each step up trades more business impact for more governance overhead; an automation agent that can trigger real actions without a human checkpoint needs meaningfully more testing and monitoring than a productivity agent that only summarizes a document.

Why does "nondeterministic" matter for anyone shipping an agent feature, not just building one?

Because the same input will not reliably produce the exact same output or path every time, which breaks an assumption most product testing relies on. Microsoft Learn states this directly: agents introduce "nondeterministic behavior" that demands real testing and governance discipline. For a PM, that means a launch checklist built for a deterministic feature (fixed test cases, expected output) is not sufficient for an agent feature; the checklist needs to account for a range of acceptable outputs and a plan for catching the outputs outside that range.

Builders Camp's AI Agents bootcamp covers designing agent tools, guardrails, and the testing discipline that nondeterministic behavior actually requires, and Building your AI Operating System covers orchestrating agents across a broader set of recurring PM work rather than a single feature. For the underlying vocabulary this guide assumes, agent memory and tool use in AI agents cover two of the five components above in more depth, and agentic workflow covers how several agents or steps chain together into one.