The document discusses the Azure AI Agent Service, which allows organizations to build enterprise-grade AI agents capable of automating and executing business processes nearly autonomously. These agents can learn from feedback and operate with minimal human oversight, enhancing efficiency in tasks such as onboarding, travel booking, and customer support. Key features include integration with existing tools, flexible model selection, extensive data connections, and robust security measures, aimed at transforming productivity and decision-making across various industries.

1. Build enterprise-
grade AI agents with
Azure AI Agent
Service
SATO Naoki (Neo)
Principal Software Engineer
Microsoft Corporation

2. Every app
will be reinvented
with AI
New apps
will be built that
weren’t possible before

3. Copilot & AI stack
Copilot devices
Copilot

4. Visual
Studio
+
Copilot
Studio
GitHub
+
Azure
Cloud to Edge
Azure AI Foundry
Data
Infrastructure
Trustworthy AI
Copilot & AI stack

5. Agentic AI
A new frontier:

6. Agentic AI
use AI to automate and execute business processes,
nearly autonomously
AI agents can plan, adapt to new information, and
execute tasks independently, making them capable
of handling complex, dynamic environments,
decision-making and autonomous action. They can
learn from feedback, adjust their strategies, and
operate with minimal human oversight.
Reason over a provided business process
Retrieve context to complete the process
Perform an action for the end-user

7. K
Inefficiency
and High
Operational
Costs
Organizations face significant challenges
with complex, manual, and time-consuming
workflows and minimal automation
Lack of
Visibility &
Control
Human Error
Scalability
Issues

8. Promising even more efficiency, value, and advantage
VALUE
Yesterday
Today
User
Chatbot
Able to answer questions
“What is the dress code
in the office?”
User
Single AI Agent
Able to take action
“Order a laptop for a new
employee”
Human Supervisor
User
HR Agent
Able to collaboratively solve
complex tasks
“Onboard 5 employees by
Monday”
Human Supervisor
IT Agent
Training Agent
User
RPA
Able to complete repetitive
tasks
“Inputs new hire information
into HR System”

9. Sends welcome
email
HR Manager
IT Manager
Training Manager
New Hire
Manually sets up
user accounts and
provisions access
Gets new hire
forms
Has questions
Answers
questions
Completes
paperwork
Confirm computer
preference
Confirmed
Initiates IT
process
Missing
applications
submits IT
ticket.
Contacts
procureme
nt to obtain
license.
Initiates
training
process
Schedule
conflict
Reviews
training
schedule and
reschedules
Sends feedback
survey
Waits for more
data before
analyzing and
optimizing the
entire process.
Completes the
training still has
questions
TODAY
Existing solutions can only automate very specific tasks that have clear inputs and outputs
Initial Interaction Document Verification
Access &
Technology Provisioning
Training
Onboarding Feedback
Loop
Survey is
completed
RPA automates the verification of
completion and accuracy
with predefined rules.
RPA automates assigning predefined training
modules but cannot adjust based on user
feedback

10. • Provides a personalized
onboarding journey based
on interaction
IT Agent
Training Agent
New Hire
TOMORROW
With AI Agents, these steps can be automated for the first time, while keeping human in the loop.
HR Agent
• Assesses documents and
learns from interactions
• Analyzes role, experience, and learning
preferences to recommend training
• Gathers real-time feedback
• Identifies patterns makes informed
decisions
Provides adaptive
training
• Sets up user accounts, adapts to
troubleshoot unexpected issues, and
learns from errors.
Fills out forms and has no
questions
The new hire has
questions during the
training.
Orchestrates the additional
processes
Human in the Loop, Supervisor and Approver
Initial Interaction Document Verification
Access &
Technology Provisioning
Training
Onboarding Feedback
Loop

11. Key Applications of
AI Agents

12. This cutting-edge shift promises to ignite a fresh
wave of productivity and efficiency
Intelligent Decision making
Increased Productivity
Enhanced Efficiency
Cost Reduction
Automation is undergoing a thrilling
transformation with autonomous
agents now being designed to
proactively plan actions and perform
tasks for users.

13. AI Agent workflows can enhance efficiency, accuracy, and customer
satisfaction
Key Use Cases Across Industries
• Assists employees in
booking business trips
• Integrates with Tripadvisor,
Outlook, and SharePoint
• Books via Teams chat or
email
• Uses OCR to gather receipts
• Automates expense report
submission and tracking
Travel Booking &
Expense Management
• Personalized onboarding
assistant for new hires
• Uses LLMs grounded in HR
data from SharePoint
• Provide relevant training
materials
• Schedule orientations and
set up software accounts
• Monitor task completion
and ensure efficient
onboarding
Employee Onboarding
• Diagnoses issues by
referencing history and
product manuals
• Provides tailored solutions
or escalates through
automated workflows
• Creates tickets and
schedules follow-ups
• Updates CRM records,
enhancing future support
Personalized Customer
Support
• Analytics data from data
lake and data warehouse
• Responds to user requests
in natural language
• Generates insights,
visualization, and sends via
Teams or email
• Automates data handling
for real-time, effortless
decision-making
Data Analytics and
Reporting

14. Challenges in Development
with AI Agents

15. 4 primary considerations
K
Providing agents
with the right
context
Knowledge Evaluation
Ensuring they
complete their
tasks correctly
Actions
Giving them the
tools to
complete their
tasks
Security
Ensuring they
only have access
to what they
should
Successfully developing AI Agents requires

16. So far, building agents from scratch been difficult to do
Tool Integration
Creating a cohesive system through complex integration of various
tools and APIs that have different interfaces, data formats, and
requirements.
Interoperability
Achieving interoperability between different tools and platforms to
ensure that data can be shared and understood across different
systems.
Scalability
Handling increased data volumes, more complex computations, and
higher user loads without degrading performance.
Real-time Processing
Ensuring tools can handle real-time requirements without significant
latency.
Maintenance
Making labor–intensive updates to integrated tools for compatibility
with new versions and prevention of obsolescence and security
vulnerabilities.
Flexibility
Modifying or customizing existing tools or developing new ones to
meet unique requirements.
Error Handling
Ensuring errors are handled gracefully and continue functioning
despite tool failures or unexpected inputs is critical for reliability.
Security
Implementing robust encryption, access controls, and compliance
with privacy regulations to protect sensitive data.

17. Organizations need platforms that enable rapid
development of performant, secure AI Agents
Current Frameworks
Security and data privacy risks
What’s Needed
Secure, responsible AI that protects
sensitive information and behaves
compliantly
Lack of integrated tools, insecure data
grounding, challenging orchestration
Ineffective deployment of AI across websites,
applications, and production environments
Restrictive pre-defined models that are
challenging to customize
Flexible models that enable processing and
integration of information from multiple
modalities or types of data
Connected complex workflow automation
grounded by seamless connection to
enterprise data
Tools and APIs that seamlessly integrate
across enterprise applications

18. Visual Studio
Copilot Studio
GitHub
Azure AI
Foundry SDK
Azure AI Foundry
Model Catalog
Open-source models
Foundational models Task models Industry models
Azure AI
Content Safety
Azure
AI Search
Azure AI
Agent Service
Azure OpenAI
Service
Observability
Customization
Evaluations Governance Monitoring
Announcing

19. Azure AI Agent Service
Public Preview
Empower developers to securely build, deploy, and scale AI agents with ease
Flexible Model
Selection
Extensive Data
Connections
Enterprise-grade
Security
Rapid Development
and Automation
AI.Azure.com

20. The full package
Built-in enterprise
readiness
BYO-file storage
(coming soon)
BYO-search index
Azure AI Foundry SDK – Agent Service
OBO Authorization Support
Enhanced Observability
Extensive Ecosystem of Tools
Knowledge
Microsoft Fabric*
SharePoint*
Grounding with Bing Search
Azure AI Search
Your own licensed data*
Files (local or Azure Blob)
File Search
Code Interpreter
Actions
Azure Logic Apps*
OpenAPI 3.0 Specified Tools
Azure Functions
Model
Catalog
Azure OpenAI Service
(GPT-4o, GPT-4o mini)
Models-as-a-Service
Llama 3.1-405B-Instruct
Mistral Large
Cohere-Command-R-Plus
*Indicates feature is coming soon

21. Introducing
Azure AI Foundry SDK – Agent Service
Azure OpenAI
Assistants API
+ +
Extensive Ecosystem of Tools
Model Catalog
Actions
Azure AI Search
Microsoft Fabric (coming soon)
SharePoint (coming soon)
Grounding with Bing Search
Azure Logic Apps (coming soon)
OpenAPI 3.0 Specified Tools
Azure Functions
Your own licensed data (coming soon)
Files (local or Azure Blob)
File Search
Code Interpreter
Azure OpenAI Service
(GPT-4o, GPT-4o mini)
Models-as-a-Service
Llama 3.1-405B-Instruct
Mistral Large
Cohere-Command-R-Plus
Built-In Enterprise Readiness
BYO-file storage
(coming soon)
BYO-search index BYO-thread storage BYO-virtual network
(coming soon)
OBO Authorization Support Enhanced Observability
Knowledge

22. How does your agent work?
User
Travel Booking
Agent
“Help me book a trip to New York
for a client meeting? I need to fly out
next Monday and return on Friday.” Knowledge Sources
(search, files, databases, storage etc.)
Models
(Azure OpenAI Service, Models-as-a-
Service)
Actions
(Pre-built or custom tools to
automate processes)
“I’ve booked your trip to New York
as requested. Here are details:…”

23. How does your agent work?
Step 1:
Create an Agent
Step 2:
Create a Thread
Step 3:
Run the Agent
Step 5:
Check the Run status
Step 6:
Display the Agent’s
Response
Agent
Travel Planning Agent
Instructions
You are a travel booking and
expense management assistant
designed to help employees plan,
book, and manage business
travel.
Run 2
Model
Tools (optional)
File Search
Code Interpreter
Function Calling
Bing Search
Microsoft SharePoint (coming soon)
Microsoft Fabric (coming soon)
Azure Logic Apps (coming soon)
Azure Functions
OpenAPI 3.0 specified tools
User’s message
I need to book a hotel
in New York for 2 stays.
Agent’s message
Here are some
suggestions:
Run 1
1
Use Tripadvisor API
to search the
nearest hotel
Create message
2
Your data (optional)
User’s message
What’s the daily meal
allowance for the
business trip?
Agent’s message
The daily allowance for
your business trip is
$75, as per company
policy.
Use Microsoft
SharePoint to
query the
company travel
policy
Create message
2
1
Thread
Travel Planning
Azure AI Search
Files (local or Azure Blob)

24. Chat Completions API vs Assistants API vs Agents
Assistants API
Chat Completions API
• Lightweight and powerful
• Inherently stateless
• Assistants API features, plus
• Build with a model of your choice
(OpenAI, Llama, Mistral, Cohere…)
• Real time web-grounding with Bing
• Secure grounding on enterprise
data in SharePoint and Fabric
• Bring Your Own Licensed data
(Tripadvisor)
• Connect to 1400+ data sources and
services with Azure Logic Apps
• Long running, event driven actions
with Azure Functions
• Standardized OpenAPI 3.0 tools
• Bring Your Own Storage
• Bring Your Own Private Network
• Bring Your Own AI Search Resource
• Limitless scaling with PTUs
• Open Telemetry based tracing
vs
Azure AI Agent Service
• Build with OpenAI models
• Stateful (inbuilt conversation state
management)
• Access persistent threads
• Automatic management of the
model’s context window
• Access files in several formats
• Utilized Microsoft-managed
storage
• File Search (API handles chunking,
embeddings storage and
creation, and implementing vector
search)
• Code Interpreter
• Function Calling
vs

25. Customers are innovating with Azure AI Agent Service

26. Demo
https://youtu.be/ltt7JNd30Ag?si=Y2Cbdx8YZOPFWJzB&t=1506

28. Rapid
Development
and Automation

29. Rapid Development and Automation
Out-of-the-box
connectors
Connection with
3P APIs
Custom code Code
Interpreter
Client-side
functions

30. Automation
1,400 built-in
connectors through
Azure Logic Apps
(coming soon)
• Create and run automated workflows
with Azure Logic Apps with little to no
code using the visual designer and
selecting from prebuilt operations.
• Simply define the business logic for
your workflow to connect your agent to
external systems, tools, and APIs.
• Microsoft connectors include Microsoft
products such as Azure App Service,
Dynamics365 Customer Voice,
Microsoft Teams, M365 Excel
• Non-Microsoft connectors
include enterprise services such as
MongoDB, Dropbox, Jira, Gmail, Twilio,
SAP, Stripe, ServiceNow and many
more.
Playground experience in Azure AI Foundry portal is coming soon in January

31. Automation
Build standardized
custom tools with
OpenAPI 3.0
specifications
• Connect your AI agent to an external
API using an OpenAPI 3.0 specified tool,
allowing for scalable interoperability
with various applications
• Build an organization-side tools library
specified with the OpenAPI 3.0 standard
• Enable your custom tools to
authenticate access and connections
with managed identities (Microsoft
Entra ID) for added security, making it
ideal for integrating with existing
infrastructure or web services
Screenshot
Playground experience in Azure AI Foundry portal is coming soon in January

32. Automation
Implement stateless
or stateful code-
based actions with
Azure Functions
• Enable your agent to perform external
interactions with the world, such as
calling APIs or asynchronously sending
and waiting for events.
• Azure Functions and Azure Durable
Actions enable you to execute
serverless code for synchronous,
asynchronous, long-running, and
event-driven actions such as
approving invoices with human-in-the-
loop, monitor an end-to-end product
supply chain over long periods of time,
and many more.
Screenshot
Playground experience in Azure AI Foundry portal is coming soon in January

33. Automation
Perform advanced
data analysis with
Code Interpreter
• Code Interpreter allows your agent to
write and run Python code in a
sandboxed execution environment,
handling diverse data formats and
generate files with data and visuals.
• With Code Interpreter enabled, your
agent can run code iteratively to solve
more challenging code, math, and data
analysis problems.
• Unlike the Assistants API, you can
integrate data in your storage to use
with this tool.
Screenshot
Playground experience in Azure AI Foundry portal is coming soon in January

34. Automation
Extend Llama Stack
agents with cloud-
based tools
• Supports the agent protocol for
developers that are using Llama Stack
SDKs
• Natively offer a scalable, cloud-hosted,
enterprise grade tools
• Wireline compatible with Llama Stack
• Protocol adapter will be a standalone
distributable, discoverable from the
llama-stack OSS repo directly
Screenshot
Playground experience in Azure AI Foundry portal is coming soon in January

35. Extensive
Data
Connections

36. Extensive Knowledge Connections
Public web data Corporate data Structured data Unstructured
data
Licensed data

37. Data Connections
Real-time, web data
grounding with Bing
Search
• Create more reliable and trustworthy
applications with real-time public
information from Bing
• Grounding with Bing Search allows your
agents to integrate real-time public
web data, ensuring their response is
accurate and up to date.
• By including supporting URLs and
search query links, Grounding with Bing
Search enhances trust and
transparency, empowering the users to
verify responses with the original
sources.
Playground experience in Azure AI Foundry portal is coming soon in January

38. Data Connections
Grounding with
private data in
Microsoft SharePoint
(coming soon)
• Grounding with SharePoint makes your
SharePoint content more accessible to
your end users.
• Enterprise-grade security features, such
as On-behalf-of (OBO) authentication
for SharePoint, ensure secure and
controlled access for end users.
Playground experience in Azure AI Foundry portal is coming soon in January

39. Data Connections
Chat with your
structured data in
Microsoft Fabric
(coming soon)
• Integrate your agents with Fabric AI
Skill to unlock powerful data analysis
capabilities.
• Fabric AI Skills can transform
enterprise data into conversational
Q&A systems, allowing users to
interact with data through chat and
uncover data-driven and actionable
insights effortlessly.
• On-behalf-of (OBO) authentication
simplifies access to enterprise data in
Fabric while maintaining robust
security, ensuring proper access
control and enterprise-grade
protection.
Playground experience in Azure AI Foundry portal is coming soon in January

40. Data Connections
Incorporate private
data in Azure AI
Search, Blob, and
your local files
• Bring your existing Azure AI Search
index or create a new one using
the improved File Search tool.
• This tool leverages a built-in
ingestion pipeline to process files
from your local system or Azure
Blob Storage.
• Maintain complete control over
your data as your files remain in
your own storage, and your Azure
AI Search resource ingests them
Playground experience in Azure AI Foundry portal is coming soon in January

41. Data Connections
Gain a competitive
advantage with
licensed data
(private preview)
• Empower your agents to deliver
nuanced, informed answers tailored to
specific use cases
• Integrate licensed data from
specialized data providers, such as
Tripadvisor, to enrich agent responses
• Tripadvisor enhances the quality of
your agent’s responses to travel
related queries with high-quality, fresh
data, such as travel guidance and
reviews (coming soon)
Playground experience in Azure AI Foundry portal is coming soon in January

42. Flexible
Model
Selection

43. Flexible Model Selection
Azure OpenAI
Service models
Models as a
Service
(Serverless API)
Azure AI
Services models
Multi-modal
support
Fine-tuned
models

44. Flexible Model Selection
Leverage Models as
a Service (MaaS)
• Support a variety of models from Azure
AI Foundry model catalog to power
your agent’s reasoning. The supported
models include:
• GPT-4o
• GPT-4o mini
• Llama 3.1
• Mistral Large
• Cohere Command R+
• Leverage Model Inference API to easily
swap models and compare
performance to find the best model for
your specific needs.

45. Flexible Model Selection
Use fine-tuned
models for your
agents
• Customize AI models to address your
unique business needs with fine-tuning,
enabling you to build task-specific
agents while optimizing token costs.
• Through collaborations with partners
like Scale AI, you can efficiently label
training data and create fine-tuned
models that integrate seamlessly with
Azure AI Agent Service.
• This process enhances the performance
of AI agents, streamlining development
and reducing time to production.
Azure AI Agent Service supports fine-tuned gpt-35-turbo (0125) as of Jan 2025

46. Flexible Model Selection
Multi-modal support
across text, image,
and audio
• Unlock new scenarios with multi-modal
support, enabling AI agents to process
and respond to diverse data formats
beyond text, expanding the potential
use cases.
• Support for GPT-4o’s image and audio
modalities so that you can analyze and
combine data from various formats to
deliver comprehensive insights, make
decisions, and provide relevant outputs
tailored to specific user needs
Playground experience in Azure AI Foundry portal is coming soon in January

47. Enterprise-grade
Security

48. Enterprise-grade Security
Bring your own
storage
Keyless setup
and
authentication
Private Virtual
Network support
Tracing/
monitoring
Content filters

49. Security
Bring your own
storage and search
• Bring your own storage account
(coming soon) and Azure AI Search
resource for custom data handling.
• All files you upload will be stored in
your storage account, ensuring you
maintain complete control over your
data.
• All indexes get created using your
connected Azure AI Search resource.
Playground experience in Azure AI Foundry portal is coming soon in January

50. Security
Bring your own
virtual network
(coming soon)
• No public egress foundational
infrastructure ensures the right
authentication and security for your
agents and tools, without you having to
do trusted service bypass
• Container injection allows the platform
network to host APIs and inject a
subnet into your network, enabling
local communication of your Azure
resources within the same virtual
network (VNet).
• If your resources are marked as private
and non-discoverable from the Internet,
the platform network can still access
them, provided the necessary
credentials and authorization are in
place

51. Security
Monitor agent
performance with
OpenTelemetry-based
training (coming soon)
• Gain insights into your AI agent's
performance and reliability
• OpenTelemetry-compatible metrics for
offline and online evaluation of agent
outputs through the Azure AI Foundry
SDK
• Add local variables and intermediate
results to trace decorator for detailed
tracing capabilities for user defined
functions
• Options to prevent sensitive or large
data logging as per OpenTelemetry
standards

52. Security
Build responsibly
with content filters
and XPIA mitigation
(coming soon)
• Supports prebuilt and custom content
filters that detect harmful content at
varying severity levels.
• Prompt shields protect agents against
cross-prompt injection attacks from
malicious actors.
• As with Azure OpenAI Service, prompts
and completions processed by the
Azure AI Agent Service are not used to
train, retrain, or improve Microsoft or
3rd party products or services without
your permission. Customers can delete
their stored data when they see fit.

53. Basic agent setup
• Overview: All agents created in this project use multi-
tenant search and storage resources fully managed by
Microsoft. You don't have visibility or control over these
underlying Azure resources.
• Required customer resources:
• AI hub
• AI project
• AI Services/AOAI
• How to use: Deploy the basic setup template
• Tool Implications:
• File search and code interpreter
• Uploaded files get stored in Microsoft managed
storage
• Vector stores get created using a Microsoft
managed search resource
• Azure AI Search tool is not supported
• Azure Blob Storage with file search is not supported

54. Standard agent setup
• Overview: Agents use customer-owned, single-tenant
search and storage resources. With this setup, you
have full control and visibility over these resources,
but you incur costs based on your usage.
• Required customer resources:
• AI hub
• AI project
• AI Services/AOAI
• Key Vault
• Storage account
• Azure AI Search
• How to use: Deploy the standard setup template
• Tool Implications:
• File search and code interpreter
• Uploaded files get stored in your Azure
Blob Storage account
• Vector stores get created using your Azure
AI Search resource

55. Start building agents with Copilot Studio…
Build, deploy, realize value and prototype faster for simple scenarios
Build agents for business
processes without setting up
& managing your own
infrastructure…
Copilot Studio Azure AI Foundry
...and scale with Azure AI
Bring your vectorized indices
from Azure AI Search
Access 1800+ models from
Azure AI model catalog

56. ...or you can start with code first solutions
Control and customize with options to manage your own infrastructure
Copilot Studio
Build highly customized
apps, Agents, and APIs
using Azure AI Foundry...
...and integrate Copilot
Studio components with
Microsoft 365 Agent
SDK
including the ability to
customize and deploy
app UI to 15+ channels
Visual Studio/ GitHub
Learn more about the better-together with Copilot Studio and Azure AI Foundry
Better together:CopilotStudioandAzure AI

57. Deploy agents with
Azure AI Foundry
Orchestrate them together with
AutoGen and Semantic Kernel
Single-agent Multi-agent
1 2
State-of-the-art
research SDK
Production-ready
and stable SDK
Managed agent
micro-services
Ideation Production

58. Agent Sample #1
Web Search and Extraction
Web Search
Query
Decomposition
Retrieve Web
Sites
Search
Query
Site Analyzer
Extract HTML
Body
Screenshot
Analysis
Verifier
Memory Thoughts
Error Handling
Page Transition
Convert to
Markdown
Page 1
Page 2
Text
Text
Screenshot
Generator
Web Result 1
* Azure AI
Content
Understanding
Azure
AI
Search
Doc Search
* We can use the Azure AI Content Understanding service to extract images and videos, and transform them from unstructured to structured data
Azure AI
Agent
Service
Azure AI
Agent
Service
Azure AI
Agent
Service

59. Agent Sample #2
Call Center Agent Assist
Query Parsing
C u s t o m e r Q u e r y
My SmartHome
thermostat isn’t
connecting to Wi-Fi,
and I keep seeing an
error code E-22. How
can I fix this?
LLM
{ Wi-Fi }
{ Error: E-22 }
{ Thermostat X10 }
Planner Agent
Azure AI
Agent
Service
Video Agent Doc Agent Web Agent
1. Doc Agent → Search internal support
documentation for error code "E-22" and Wi-Fi
troubleshooting.
2. Video Agent → Locate and timestamp
troubleshooting video content related to Wi-Fi
connection and error code E-22.
3. Web Search Agent → Search external sources
for recent discussions, tips, or product-specific
issues regarding Wi-Fi and the error code.
D y n a m i c P l a n
Gathers internal
troubleshooting steps.
Summarizes video clips
related to Wi-Fi and
error code E-22.
Finds the latest external
resources related to the
issue
* Abstraction
Recent Online
Findings
from Web
Internal Steps
from Documents
Video Link
and Timestamps
from Videos
* We can use the Azure AI Content Understanding service to extract images and videos, and transform them from unstructured to structured data
Azure AI
Agent
Service
Azure AI
Agent
Service
Azure AI
Agent
Service

60. Agent Sample #3
AI Teammates powered by multi-agents
Marketing Director
Role: Social Media Manager
Qualification: Expert
Skills: Content creation
Coding...
AI Teammate
Marketing Director
AI Teammate
Publish to
Linked In
Create an AI Teammate
Collaborate with AI Teammate
Invite AI Teammate to
Teams meeting
Conversation AI Coding Agent Content Gen
Azure AI
Agent
Service
Azure AI
Agent
Service
Azure AI
Agent
Service
Create AI Teammate

61. Get started with Agent Service on
Microsoft Learn Docs
https://aka.ms/MicrosoftLearn_Agents
Get started with Azure AI Agents
through Azure AI Foundry SDK
Watch the Microsoft Ignite sessions on-
demand
 Building intelligent multi-agent systems
with Azure AI
Read the Techcommunity announcement

62. Thank you