AI-900 Practice Question: Describe features of generative AI workloads on Azure
What is 'agentic AI' and how does it differ from a simple chatbot?
⚠ Common exam trap
Watch out — candidates often confuse 'agentic AI' with any AI that 'acts on behalf of a user' (like a customer service bot), missing the key distinction of autonomous multi-step planning and tool use that defines agentic AI.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
AI that autonomously plans and executes multi-step workflows using tools to accomplish complex goals
Agentic AI refers to AI systems that can autonomously plan and execute multi-step workflows by using external tools, APIs, or data sources to achieve complex goals. This differs from a simple chatbot, which typically responds to user prompts in a single turn without independent goal-setting or tool orchestration. In generative AI workloads on Azure, agentic AI might leverage Azure AI Agent Service or Semantic Kernel to chain together calls to Azure Cognitive Search, Azure Functions, or external APIs, enabling tasks like automated report generation or multi-step data analysis.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
AI that represents a company as a legal agent for contractual purposes
Why it's wrong here
Legal agency is a formal relationship in which a human or entity is authorized to act on another's behalf, creating binding contractual obligations. Agentic AI has no legal personhood or contractual capacity; the 'agent' in artificial intelligence refers to a software entity that perceives its environment and takes actions to achieve goals. Therefore, representing a company for contractual purposes is a legal function unrelated to the autonomous, tool-using behavior that defines agentic AI.
- ✓
AI that autonomously plans and executes multi-step workflows using tools to accomplish complex goals
Why this is correct
Agentic AI is defined by its autonomous capability to understand a complex objective, break it into subtasks, select and call appropriate tools, and adapt when steps fail—executing a full multi-step workflow with minimal human intervention. It combines goal reasoning, chain-of-thought planning, tool use, and error recovery in a continuous loop, going well beyond a single prediction. This option correctly captures the essence of agentic systems.
- ✗
Chatbots that can respond on behalf of a company's customer service team
Why it's wrong here
A customer service chatbot typically operates in a single-turn or brief multi-turn pattern, retrieving scripted responses or matching intents and then terminating the interaction. Agentic AI, by contrast, is an autonomous system that decomposes a high-level goal into a sequence of actions, invokes external tools as needed, and iterates until the workflow is complete. Merely 'responding on behalf' of a support team is reactive conversational behavior, not proactive goal-directed task execution.
- ✗
AI models that were trained by multiple agents working simultaneously in parallel
Why it's wrong here
Training models with multiple parallel agents is a distributed computing technique used to shorten training time by partitioning data or model parameters across workers. This describes an offline training strategy and has no bearing on the resulting system's runtime autonomy or capacity to plan multi-step actions. Agentic AI refers to a deployment architecture where the model acts independently in an environment, not to how learning was parallelized.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
Go deeper
Related to this question
Learn chapter
Types of AI Workloads
Key term
Azure OpenAI Service
Azure OpenAI Service is a cloud platform from Microsoft that lets developers use powerful artificial intelligence models, like GPT-4, to build applications that can understand and generate human-like text, code, images, and more.
Key term
Generative AI
Generative AI is a type of artificial intelligence that creates new content—like text, images, or code—by learning patterns from existing data.
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