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Guided AI-103 Domain 2
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AI-103 Study Guide

Domain 1: Plan and Manage an Azure AI Solution

  • Choosing the Right AI Model Free
  • Foundry Services: Your AI Toolkit Free
  • Retrieval, Indexing & Agent Memory
  • Designing AI Infrastructure
  • Deploying Models & CI/CD
  • Quotas, Scaling & Cost
  • Monitoring & Security
  • Responsible AI: Filters, Auditing & Governance

Domain 2: Implement Generative AI and Agentic Solutions

  • Connecting Your App to Foundry Free
  • Building RAG Applications
  • Workflows & Reasoning Pipelines
  • Evaluating AI Models & Apps
  • Agent Fundamentals: Roles, Goals & Tools Free
  • Building Agents with Retrieval & Memory
  • Agent Tools & Knowledge Integration
  • Multi-Agent Orchestration & Safeguards
  • Agent Monitoring & Error Analysis
  • Prompt Engineering & Model Tuning
  • Observability & Production Operations

Domain 3: Implement Computer Vision Solutions

  • Image & Video Generation
  • Multimodal Visual Understanding
  • Responsible AI for Visual Content

Domain 4: Implement Text Analysis Solutions

  • Text Analysis with Language Models
  • Speech, Translation & Voice Agents

Domain 5: Implement Information Extraction Solutions

  • Ingestion, Indexing & Grounding Pipelines
  • Extracting Content with Content Understanding
  • Exam Prep: Putting It All Together

AI-103 Study Guide

Domain 1: Plan and Manage an Azure AI Solution

  • Choosing the Right AI Model Free
  • Foundry Services: Your AI Toolkit Free
  • Retrieval, Indexing & Agent Memory
  • Designing AI Infrastructure
  • Deploying Models & CI/CD
  • Quotas, Scaling & Cost
  • Monitoring & Security
  • Responsible AI: Filters, Auditing & Governance

Domain 2: Implement Generative AI and Agentic Solutions

  • Connecting Your App to Foundry Free
  • Building RAG Applications
  • Workflows & Reasoning Pipelines
  • Evaluating AI Models & Apps
  • Agent Fundamentals: Roles, Goals & Tools Free
  • Building Agents with Retrieval & Memory
  • Agent Tools & Knowledge Integration
  • Multi-Agent Orchestration & Safeguards
  • Agent Monitoring & Error Analysis
  • Prompt Engineering & Model Tuning
  • Observability & Production Operations

Domain 3: Implement Computer Vision Solutions

  • Image & Video Generation
  • Multimodal Visual Understanding
  • Responsible AI for Visual Content

Domain 4: Implement Text Analysis Solutions

  • Text Analysis with Language Models
  • Speech, Translation & Voice Agents

Domain 5: Implement Information Extraction Solutions

  • Ingestion, Indexing & Grounding Pipelines
  • Extracting Content with Content Understanding
  • Exam Prep: Putting It All Together
Domain 2: Implement Generative AI and Agentic Solutions Premium ⏱ ~12 min read

Agent Tools & Knowledge Integration

Agents are only as useful as the tools they can access. Learn how to integrate APIs, knowledge stores, search, Content Understanding, and custom functions into your AI agents.

Giving agents superpowers

β˜• Simple explanation

An agent without tools is like a brilliant person locked in an empty room β€” smart but useless. Tools let agents interact with the real world.

Tools are the bridges between your agent’s reasoning and your actual systems. Need to search a database? Tool. Extract data from a PDF? Tool. Send an email? Tool. Call an external API? Tool. Each tool is a well-defined function the agent can decide to call.

The AI-103 exam tests five categories of agent tool integration:

  • APIs β€” calling external REST APIs and services
  • Knowledge stores β€” Foundry IQ and indexed document collections
  • Search β€” Azure AI Search for retrieval and grounding
  • Content Understanding β€” extracting structured data from documents
  • Custom functions β€” your own code that runs when the agent calls it

The five tool categories

CategoryWhat It DoesWhen to UseExample
APIsCalls external services via RESTReal-time data, third-party servicesCRM lookup, payment processing, weather API
Knowledge storesSearches indexed documentsQ&A over company docsFoundry IQ with policy documents
SearchPerforms sophisticated queriesComplex retrieval with rankingAzure AI Search with hybrid search
Content UnderstandingExtracts data from documentsProcessing uploaded filesExtract fields from invoices, contracts
Custom functionsRuns your own codeBusiness logic, calculations, data transformsCalculate shipping cost, validate input, format output

Built-in vs custom tools

Built-in tools vs custom functions
FeatureBuilt-in Foundry ToolsCustom Functions
ExamplesCode interpreter, web search (Bing), Foundry IQYour own APIs, database queries, business logic
SetupEnable in agent configurationWrite function code, define schema, register with agent
HostingManaged by FoundryYour infrastructure (Container Apps, Functions, etc.)
Best forCommon capabilities out of the boxDomain-specific actions unique to your business

Content Understanding as an agent tool

Content Understanding is particularly powerful as an agent tool because it lets agents process documents in real-time:

CapabilityAgent Use Case
OCRAgent reads a photo of a receipt or form uploaded by the user
Layout analysisAgent understands table structures in uploaded spreadsheets
Field extractionAgent pulls specific values (invoice number, total, date)
Document classificationAgent identifies what type of document was uploaded
ℹ️ Real-world example: NeuralMed's intake agent

NeuralMed’s patient intake agent uses multiple tool types:

  1. Content Understanding β€” patient uploads insurance card photo β†’ agent extracts member ID, group number, provider
  2. Custom function β€” verify_insurance(member_id, provider) β†’ checks if insurance is active
  3. Knowledge store β€” searches NeuralMed’s service catalog to match patient needs with available services
  4. API β€” check_availability(doctor_id, date_range) β†’ finds available appointment slots
  5. Custom function β€” book_appointment(patient_id, doctor_id, slot) β†’ books the appointment

Five tools, one seamless patient experience. The agent orchestrates the entire flow.

Tool design best practices

PracticeWhy It Matters
Descriptive namesAgent understands when to use each tool
Clear parameter docsAgent passes correct arguments
Error handlingAgent can recover gracefully when a tool fails
Minimal scopeEach tool does one thing well (single responsibility)
Idempotent writesCalling the same tool twice doesn’t create duplicates
πŸ’‘ Exam tip: Content Understanding vs prompting a multimodal model

The exam tests when to use Content Understanding vs a multimodal model for document processing:

  • Extract specific fields from structured documents (invoices, forms) β†’ Content Understanding (purpose-built, higher accuracy)
  • Understand and reason about document content β†’ Multimodal model (GPT-4o with vision)
  • Both needed? β†’ Content Understanding extracts data, then the model reasons about it

Rule: extraction = Content Understanding, reasoning = multimodal model.

Key terms

Question

What is Content Understanding as an agent tool?

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Answer

Using Foundry's Content Understanding service within an agent to process documents in real-time. The agent can extract fields from uploaded PDFs, photos, or forms as part of a conversation flow.

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Question

What is the code interpreter tool?

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Answer

A built-in Foundry tool that lets agents write and execute Python code during conversations. Used for data analysis, calculations, chart generation, and file processing. Runs in a sandboxed environment.

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Question

What is MCP (Model Context Protocol)?

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Answer

An open protocol for connecting AI agents to external tools and data sources. Foundry supports MCP for integrating custom tools, enabling agents to use a wide ecosystem of pre-built tool servers.

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Knowledge check

Knowledge Check

MediaForge's content agent receives a client brief as a PDF. The agent needs to extract the target audience, budget, and timeline fields, then generate 5 headline ideas based on the brief. Which tools should the agent use?

Knowledge Check

Atlas Financial's compliance agent needs to verify a customer's identity by checking their driver's license. The customer uploads a photo. Which tool combination is appropriate?

🎬 Video coming soon

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Multi-Agent Orchestration & Safeguards

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