Study guide
Agent Tools, Functions, Memory, and Evaluation
Microsoft Azure AI Apps and Agents Developer Associate AI-103
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1 AI-103 Blueprint, Microsoft Foundry, and Solution Planning
Current AI-103 Exam Facts Foundry Hubs, Projects, Models, and Deployments Security, Networking, Monitoring, and Responsible AI
2 Generative AI, Agents, and Retrieval-Augmented Generation
Prompt Design, Model Selection, and Structured Outputs RAG, Grounding, Embeddings, and Azure AI Search Agent Tools, Functions, Memory, and Evaluation
3 Vision, Language, Information Extraction, and Final Review
Vision, Multimodal, Speech, and Language Workloads Image and Video Generation, Editing, and Content Understanding Pipelines Document Intelligence, Content Understanding, and Extraction AI-103 Study Plan, Traps, and Practice Strategy
Agent Tools, Functions, Memory, and Evaluation
Key Takeaways Explain This Topic AI
A Foundry agent combines a model, instructions, tools, and managed runtime concepts so it can reason, retrieve, call APIs, and complete multi-step tasks.
Knowledge tools ground answers, action tools change systems, and function or OpenAPI schemas define the callable contract the model can request.
Threads or conversations store multi-turn state, but cross-session memory should be deliberately persisted, permissioned, summarized, and injected only when relevant.
Tool calls require validation, least privilege, trusted outputs, retry handling, and human approval for costly, sensitive, or irreversible actions.
Evaluation and tracing are production requirements: measure groundedness, relevance, safety, tool correctness, latency, token use, and failure patterns.
Last updated: June 2026
From Chatbot to Agent
A simple chatbot mostly generates text. An agent can reason over a goal, use tools, retrieve knowledge, call APIs, run code, and continue through multiple steps. Microsoft Foundry Agent Service provides managed agent runtime options so teams can define prompt agents quickly or deploy hosted agents with custom code, while still using Foundry models, platform tools, identity, observability, and publishing controls.
On the AI-103 blueprint this lives under Build agents by using Foundry , which explicitly lists defining agent roles, goals, conversation-tracking approach, and tool schemas; integrating retrieval, function-calling, and memory; orchestrating multi-agent solutions; and building autonomous or semiautonomous workflows with safeguards and approval-flow controls.
Core Agent Pieces
Piece Purpose Design question
Model Reasoning and language capability Does the task need a large, small, reasoning, or multimodal model?
Instructions Role, goals, constraints, and policies What should the agent always do or refuse?
Tools Knowledge access or external action What can the agent read, calculate, or change?
Conversation state Multi-turn context What history is needed for this run?
Memory store Durable user or task facts What should persist across sessions, and who may see it?
Evaluation Quality and safety evidence Which metrics block release or trigger rollback?
AI-103 uses terms such as agents, conversations or threads, messages, runs or responses, tool schemas, memory, tracing, and evaluation. In the classic thread model, a thread stores conversation messages and a run invokes the agent against that thread. Newer Foundry agent patterns expose a Responses API and conversation concepts, but the exam idea is constant: state is explicit, scoped, and inspectable.
Knowledge Tools and Action Tools
The single most useful agent distinction on this exam is knowledge tools versus action tools .
Knowledge tools add context and are generally safe to call automatically. Examples: Azure AI Search, file search, web grounding, Microsoft Fabric, or a knowledge base. They should return source metadata so the agent can cite evidence and tracing can explain what was retrieved.
Action tools change or inspect external systems and carry real risk. Examples: function calling, Azure Functions, Logic Apps, OpenAPI tools, Model Context Protocol (MCP) endpoints, code interpreter, and browser automation.
The model may choose a tool, but the platform or host code must enforce policy. For a purchase-order agent, reading vendor status can be automatic; submitting an order, changing a bank account, or emailing a customer should require confirmation and least-privilege credentials.
A tool schema is the contract the model sees: name, description, parameters, required fields, enum values, and expected result shape. Good schemas are narrow and obvious. Do not expose a generic run_sql tool when a read_vendor_invoice_status(vendorId) tool would limit blast radius. Validate arguments before execution, validate outputs before reusing them in prompts, and treat tool output as untrusted — retrieved or returned text can carry indirect prompt injection that tries to hijack the next model call.
Memory and Threads
Conversation memory is not the same as RAG, and the exam likes to blur them. RAG retrieves source documents; memory preserves user or task context.
- Concept Stores Scope Risk to manage
- Thread / conversation Prior turns of one chat Single session Context-window growth
Cross-session memory Durable user or task facts Across sessions, keyed by user/tenant/task Privacy, stale facts, leakage
RAG index Source documents and chunks Whole knowledge base Permissions, freshness
A thread can remember prior turns for one conversation, but cross-session memory should live in a governed database or index keyed by user, tenant, or task. Store only useful facts, summarize long histories instead of replaying every message, expire stale memory, and never inject sensitive or irrelevant history into every prompt — doing so wastes tokens and risks leaking one user's data into another's context.
Evaluation, Tracing, and Operations
Foundry evaluations can test agents, models, or datasets before deployment and monitor production quality afterward. For RAG and agents, measure retrieval relevance, final-answer relevance, groundedness , response completeness, safety, tool success rate , refusal correctness, latency, and token cost. Tracing captures the evidence trail: prompt construction, retrieved chunks, model calls, tool invocations, outputs, errors, and timing. When an agent gives a bad answer, traces reveal whether the defect came from weak instructions, missing retrieval, a bad tool result, or an unsafe tool choice.
The exam treats observability as a release gate, not an afterthought: you should be able to point at the metric or trace that blocks a deployment or triggers a rollback.
Multi-agent orchestration and safeguards
The blueprint also lists orchestrated multi-agent solutions and autonomous or semiautonomous workflows with approval flow controls . A common pattern routes a request to specialized agents (for example, a retrieval agent, a planning agent, and an action agent) under an orchestrator that holds shared goals and policy.
The exam favors designs where each sub-agent has the least-privilege tools it needs and nothing more, where high-impact actions pause for human approval, and where the orchestrator logs every hand-off in a trace. Autonomy is a spectrum: read-only research can run unattended, but any irreversible action should route through an approval flow so a human owns the consequence.
- Official Anchors
- Foundry Agent Service overview: https://learn.microsoft.com/en-us/azure/foundry/agents/overview
Agent tools overview: https://learn.microsoft.com/en-us/azure/foundry-classic/agents/how-to/tools-classic/overview
Threads, runs, and messages: https://learn.microsoft.com/en-us/azure/foundry-classic/agents/concepts/threads-runs-messages