AB-100: Agentic AI Business Solutions Architect Exam Guide

Contrast traditional AI with agentic AI, showing how agentic AI acts to achieve goals rather than just responding. Highlight its impact on business workflows, from customer support to HR onboarding.

Learn license requirements for creating and using Microsoft AI agents, including the $30 Copilot plan, Copilot Studio licenses, tenant licenses, and pay-as-you-go access for unlicensed users.

Create your first ai agent with microsoft 365 copilot by configuring branding, description, instructions, and a knowledge source from an hr handbook.

Master the Microsoft agentic AI ecosystem, where Copilot Studio designs and orchestrates agents and AI foundry supplies models and intelligence for AB-100.

Identify when AI agents are needed over automation by analyzing context and decisions. Use a quick three-question rule to decide the right solution for exams and real-world tasks.

Learn how data grounding connects agentic AI to trusted company data, official documents, and approved databases to improve accuracy, reduce hallucinations, and ensure enterprise-ready decisions.

Learn what a prompt is in generative AI, how prompts start conversations, and how to craft efficient prompts and prompt engineering for agentic AI, with an Azure VM example.

Explore how prompt engineering crafts precise prompts to guide AI models toward accurate, useful output, using search engine analogies and site-specific queries as practical examples.

Learn direct prompts for AI, telling the system exactly what you want. Use fast, clear answers and limit output to provided data, with examples like emails and data summaries.

Explore direct prompts that extract concise, to-the-point information and rewrite content professionally, saving time by summarizing articles, generating headings, and polishing messages.

Role-based prompts make AI act as a field expert, delivering consultant-style, strategic advice for marketing, HR, or policy tasks like social media strategy and remote-work policies.

Open-ended prompts let AI think freely and brainstorm, generating creative ideas without restrictions. Use them to surface concepts like fitness brand social campaigns and high-potential industries for 2025.

Learn how analytical prompts analyze data from spreadsheets or sales data, upload a spreadsheet to generative AI, and identify key trends to support data-driven decisions.

Master best practices for writing prompts that are clear, specific, and contextual. Break down complex queries, limit scope, specify format, use examples, and reuse effective generative AI prompts.

Define the inputs, actions, and outputs that guide an AI agent from start to finish. Keep workflows simple to ensure clear outputs and easier testing, monitoring, and scaling.

Design prompts with clarity and specificity to guide AI agents and reduce wrong answers. Use strong fallback logic to handle uncertainty, never guessing, and include clear escalation paths to humans.

Define and update ready-made prompts for an ai agent by adding suggested prompts and frequently asked questions, enabling users to start conversations quickly with prebuilt queries.

Define fallback logic for unknown queries by escalating to a specified contact when information is not found in provided documents, avoiding guesswork and ensuring enterprise-ready safety.

Learn how to share AI agents created with Microsoft 365 Copilot with others in your organization, using specific people, groups, or a company-wide link.

Learn how to design enterprise-ready ai agents by applying architecture principles: reliability, security, performance efficiency, operational excellence, and cost optimization, using grounded data and auditable actions.

Monitor ai agents with telemetry to improve reliability, accuracy, and risk detection, while reducing costs and ensuring compliance through audit data and governance.

Assess AI agent performance by measuring latency, accuracy, and failures, and balance these metrics to ensure speed, correctness, and stability.

Identify biases in AI and their sources—training data, algorithm bias, and predictive bias—through examples, enabling fairer generative AI deployments.

Identify why bias matters in AI systems and how to address and prevent it by reviewing training data, evaluating the algorithm, and monitoring predictive bias to ensure fair AI outcomes.

Protect data privacy and security in generative AI by following GDPR-like laws, planning data use and retention, and enforcing encryption and breach response.

Implement governance frameworks for responsible AI development by establishing clear rules, fairness checks, transparency, expert input, stakeholder involvement, training employees, and compliance with privacy laws like GDPR.

Lead with human-centric values by prioritizing transparency and accountability in generative AI to safeguard human well-being, explain decisions, own mistakes, and improve lives.