Building an AI solution goes beyond calling a model. The focus is on creating production-ready AI applications and agents with Microsoft Foundry that can use enterprise data, interact with tools, process different content types, and collaborate to complete real tasks.
| Area | What you should understand |
|---|---|
| Generative AI apps | Build conversational applications using models, APIs, and SDKs |
| Grounding | Connect models to your own data for relevant, fact-based responses |
| Agents + tools | Let agents retrieve information and take actions |
| Multi-agent systems | Orchestrate specialized agents to collaborate on workflows |
| Multimodal AI | Process text, documents, vision, and speech |
| Production | Deploy, publish, monitor, secure, and apply responsible AI safeguards |
Exam focus: Understand not just what these capabilities do, but when and why you would use them together in an Azure AI solution.
A useful mental model for AI-103 is:
User → AI App/Agent → Model → Data + Tools → Action/Response
For more complex solutions:
User → Orchestrator → Agent A + Agent B + Agent C → Tools/Data → Result
An agent therefore isn't simply a chatbot. It combines a model's reasoning capabilities with instructions, knowledge, and tools so it can perform useful work.
The course assumes working knowledge of Python, REST APIs/SDKs, Azure fundamentals, and generative AI concepts. Hands-on practice is important: build applications in Microsoft Foundry, connect models to data, add tools to agents, experiment with multimodal inputs, and create multi-agent workflows.
Key takeaway: Think beyond prompts and models. AI-103 is about assembling the components required for an end-to-end AI solution:
Models → Grounding → Tools → Agents → Orchestration → Production
Generative AI models are powerful, but their trained knowledge is limited. Tools extend models beyond text generation, allowing them to access real-time information, take actions, ground responses in facts, extend functionality, and build intelligent workflows.
| Tool | Purpose |
|---|---|
code_interpreter |
Generate and run code for calculations and data analysis |
web_search |
Find current information on the internet |
file_search |
Search files and ground responses in specific knowledge |
function |
Call custom functions implemented by your application |
Remember: current information → web_search · uploaded/private documents → file_search · calculations/code → code_interpreter · application-specific actions → function
Tools are provided through the tools collection. The model can determine which available tool is appropriate for a request.
response = client.responses.create(
model=model_name,
input="Answer the user's request using the available tools.",
tools=[
{"type": "code_interpreter", "container": {"type": "auto"}},
{"type": "web_search"},
{"type": "file_search", "vector_store_ids": [vector_store.id]}
]
)
print(response.output_text)
Core flow: User → Responses API → Model → Tool → Result → Model → Response
For file_search, documents are stored in a vector store and prepared for semantic retrieval:
Files → Chunking → Embeddings → Vector Store → Retrieval → Model
This lets the model answer using relevant document content rather than relying only on its trained knowledge. Uploaded company policies or private documents → File Search + Vector Store.
Functions are different because the application executes the function, not the model. The model identifies the required function and returns a function-call request:
User → Model → Function Call → Application → Function → Result → Model → Response
The application executes the requested code and returns its result. This process can run in a loop when multiple tool calls are needed.
Key distinction: built-in tools extend the model with predefined capabilities; function calling connects the model to your own application logic and actions.
AI safety is no longer only about what future systems might be capable of. New reports show that people are already trying to use advanced AI for potentially harmful activities.
Anthropic says it has detected and disrupted attempts to misuse its Claude models across several areas, including cyberattacks, surveillance, influence operations and potentially dangerous biological research.
AI can make such activities easier by helping users analyze information, write code, coordinate tasks and automate parts of complex workflows. More capable AI agents could increase this effect by performing multiple steps with less human involvement.
The findings do not mean AI systems are independently launching attacks. They show a different challenge: powerful general-purpose tools can amplify the capabilities of people who misuse them.
For AI providers, businesses and governments, safeguards will increasingly need to combine technical restrictions, monitoring, security testing and human oversight.
APIs and MCP are not competing technologies—they solve different parts of the integration problem.
APIs do the actual work. They let software communicate with services, databases, and other systems. With AI applications, the model itself does not call an API; it chooses an action, while software outside the model executes it.
MCP adds a standardized layer around this process. An MCP server can expose useful actions—such as reading messages or creating tickets—while handling the underlying API calls, authentication, formats, and other implementation details.
This makes integrations easier to discover and reuse across multiple AI applications instead of rebuilding them for each one.
When to use which?
Direct APIs: Simple applications, experiments, or a small number of known operations.
MCP: Multiple AI applications sharing tools and systems.
In short, MCP does not replace APIs. It provides a common, reusable way for AI applications to access the capabilities behind them.
Original video: MCP vs API Explained: Do You Really Need MCP? (en / 17:17) - KodeKloud (YouTube)
larly relevant:
When was the lawn last fertilized? Which month was the hedge trimmed? And what work was done in the garden last autumn? Small details like these are surprisingly easy to forget.
Keeping a record of garden tasks makes it much easier to look back and plan future work. The challenge is finding a structure that is simple enough for everyday use while remaining organized over several years.
A practical approach is to organize the records hierarchically in a note-taking application:
The Garden Chronicle serves as the long-term archive, while each Garden Log contains the records for a particular year.
A simple table works well for the individual entries:
| Date | Task | Notes |
|---|---|---|
| 09 Sep 2026 | Mowed the lawn | Cutting height recorded |
| 15 Sep 2026 | Fertilized the lawn | Autumn fertilizer |
| 03 Oct 2026 | Pruned shrubs | Seasonal pruning |
The Notes column is particularly useful for recording products, quantities, plant varieties, weather conditions, or observations.
With very little effort, this creates a useful garden history that can support future planning and make recurring seasonal tasks easier to track.
Have dozens of tabs open in Brave and want to save them before closing the browser? A simple bookmark export provides an easy backup without installing extensions or running scripts.
First, press Ctrl + Shift + D in Brave. This bookmarks all tabs in the current window and places them together in a folder. Give the folder a recognizable name, such as Open Tabs Backup.
Next:
Open Brave’s Bookmark Manager by entering brave://bookmarks/ in the address bar.
Select the three-dot menu in the upper-right corner.
Choose Export bookmarks.
Select a location and save the resulting .html file.
The exported HTML preserves bookmark titles and URLs, making it useful as a portable backup. It can also be opened in a browser or imported into compatible browsers later.
One limitation is worth knowing: Brave exports the complete bookmark collection, not only the temporary folder containing your open tabs.
Closing a notebook in OneNote does not delete it. Cloud-based notebooks are stored in services such as OneDrive or SharePoint, so permanent deletion must happen at the storage location.
Close it in OneNote. This removes the notebook from the app but leaves its data untouched.
Open OneDrive or SharePoint and locate the notebook in its actual storage location.
Delete the complete notebook rather than individual sections.
Check the recycle bin. A normally deleted notebook remains recoverable until it is removed from the recycle bin or the retention period expires.
Empty the recycle bin if you want to remove it immediately from your accessible storage.
Microsoft 365 business and SharePoint environments may have additional recycle-bin stages or organizational retention policies. These can preserve deleted information even after a user empties the recycle bin.
The key distinction is simple: closing removes a notebook from OneNote; deleting removes it from cloud storage. For permanent removal, always check the underlying storage and its recycle bin.
Europe’s AI ambitions just received a major financial boost. French AI company Mistral has raised €3 billion, giving it a valuation of about €21 billion ($24 billion) and marking the largest equity funding round by a privately owned European technology company.
Mistral develops large AI models and competes in a market dominated by much larger American companies. The new capital is expected to support further model development, computing infrastructure and international expansion.
The investment also has a broader European dimension. Governments and businesses increasingly want greater choice over where their AI technology and data come from. A strong European AI provider could offer another option alongside major US and Chinese platforms.
The enormous investment required to develop advanced AI is concentrating the industry around companies capable of accessing substantial computing power and capital.
Mistral’s latest funding shows that Europe is trying to remain part of that race—not simply as a customer for AI developed elsewhere, but as a producer of its own technology.