Enterprise AI & Agentic Solutions with Microsoft Foundry
Move beyond isolated chatbots and build enterprise AI solutions that can reason, use business knowledge, call tools and APIs, work across multiple channels, and integrate with your existing systems. With Microsoft Foundry, we help organizations design and implement modern AI and agentic solutions using models, agents, knowledge, tools, workflows, evaluations, tracing and enterprise integration.
Whether you are starting with a simple AI assistant, building a multi-agent solution, integrating enterprise data and APIs, or extending an existing application with AI capabilities, we can help you design the right architecture and implement it.
Talk to Us Our ServicesWhat is Microsoft Foundry?
Microsoft Foundry is Microsoft's platform for building, evaluating, deploying and managing enterprise AI applications and agents.
It provides a foundation for working with AI models, agents, knowledge sources, tools, workflows, evaluations, observability and enterprise security within a managed environment.
Modern enterprise AI solutions often require much more than sending a prompt to a language model. You need to decide:
- Which model should be used for each workload?
- Should the solution use one agent or multiple agents?
- How should agents be instructed and coordinated?
- How should enterprise knowledge be provided?
- How should data from databases, APIs and business applications be accessed?
- How should tools and functions be exposed to agents?
- How should conversations and context be managed?
- How should responses be grounded and validated?
- How should token usage, latency and cost be monitored?
- How should safety, permissions and governance be implemented?
- How should the solution be evaluated before going into production?
- How should problems be traced when an agent behaves unexpectedly?
- How should AI be integrated into web, mobile, voice and messaging experiences?
This is where our experience in enterprise application architecture, data platforms and modern AI comes together with Microsoft Foundry.
What Can We Implement with Microsoft Foundry?
We can help you design and implement complete AI and agentic solutions rather than treating Foundry only as a place to access language models.
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Enterprise AI Assistants
Build AI assistants that can answer questions, use enterprise knowledge and support users through natural-language conversations. Assistants can be designed for internal employees, customers, partners or operational teams, depending on the requirements.
The solution can combine:
- Instructions
- Enterprise knowledge
- Search and retrieval
- Business data
- APIs
- Tools
- Conversation context
- Structured outputs
- External applications
The objective is to build an assistant that works within the business context rather than a generic chatbot.
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AI Agents
Build agents that can reason, select tools and perform actions based on user requests. An agent can combine model reasoning with business rules, enterprise data and application services to complete tasks rather than only provide textual answers.
We can design agents that:
- Understand user intent
- Use enterprise knowledge
- Query business data
- Call APIs
- Execute approved tools
- Follow business rules
- Maintain conversational context
- Produce structured responses
- Coordinate with other agents
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Multi-Agent Solutions
Not every problem should be handled by a single agent. Complex enterprise solutions may require multiple specialized agents that collaborate, delegate tasks or work through defined orchestration patterns.
We can design multi-agent solutions using patterns such as:
- Sequential processing
- Agent handoff
- Routing
- Specialized agents
- Supervisor and worker patterns
- Group collaboration
- Workflow-based orchestration
The right pattern depends on the business process, required control and complexity of the solution.
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Knowledge & Retrieval
Connect AI solutions to enterprise knowledge rather than relying only on what a model was trained on.
Knowledge can come from sources such as:
- Documents
- Policies
- Procedures
- Product information
- Technical documentation
- Data platforms
- Structured databases
- Search indexes
- Other enterprise systems
We can help design retrieval and grounding strategies so that responses are based on appropriate enterprise information.
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Enterprise Data Integration
AI agents often need access to live business information rather than only static documents.
We can integrate agents with:
- SQL databases
- Microsoft Fabric
- Databricks
- APIs
- Internal applications
- Cloud services
- Business platforms
- Custom data services
The agent can retrieve the information it needs while respecting the permissions and boundaries defined by the architecture.
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Tools, APIs & Business Actions
Move beyond question answering by allowing agents to perform approved business actions. Agents can call tools and APIs to complete tasks such as retrieving information, creating records, checking status, initiating workflows or interacting with existing applications.
We can help design:
- Tool definitions
- API integration
- Input and output schemas
- Validation
- Authorization
- Error handling
- Guardrails
- Business rules
The objective is to give agents useful capabilities without giving them uncontrolled access to business systems.
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Dynamic Instructions & Context
Enterprise agents may need different instructions depending on the user, business process, data available or current state of the conversation.
We can design solutions that dynamically provide:
- System instructions
- User context
- Business rules
- Customer information
- Access permissions
- Session information
- Tool availability
- Knowledge sources
This allows the agent to behave differently depending on the situation while still following controlled business logic.
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Model Selection & Routing
Different tasks may require different models. A complex reasoning task may need a more capable model, while a simple classification or extraction task may be handled by a smaller and more cost-effective model.
We can design model strategies based on:
- Accuracy
- Reasoning requirements
- Latency
- Cost
- Context size
- Multimodal requirements
- Tool usage
- Structured output requirements
Where appropriate, solutions can route different tasks to different models rather than using one model for everything.
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Voice & Multilingual AI
Extend AI solutions beyond text. Agents can be integrated with speech and voice experiences to support real-time conversations, multilingual interactions and voice-enabled applications.
We can design solutions that combine:
- Speech input
- Speech-to-text
- Text-to-speech
- Multilingual interactions
- Real-time voice
- Agent reasoning
- Business tools and APIs
This allows an existing enterprise agent to support users through more natural communication channels.
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Multichannel AI Solutions
The same enterprise AI capability may need to be available through different applications and communication channels.
We can design agent architectures that can be integrated with:
- Web applications
- Mobile applications
- Messaging platforms
- Voice interfaces
- Internal business applications
- Custom portals
The AI layer can remain centralized while different channels provide the user experience.
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Evaluations & Quality Testing
An agent that appears to work during development may still fail on real user questions.
We can help define evaluation approaches for areas such as:
- Answer accuracy
- Groundedness
- Tool selection
- Instruction following
- Response relevance
- Safety
- Business-rule compliance
- Expected output format
Evaluation should be part of the development lifecycle rather than something performed only immediately before production.
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Tracing, Monitoring & Observability
AI solutions require a different type of operational visibility compared with traditional applications.
When an agent gives an incorrect answer, you may need to understand:
- Which instructions were applied
- Which knowledge was retrieved
- Which tool was selected
- What tool inputs were generated
- What information was returned
- Which model was used
- How many tokens were consumed
- Where latency was introduced
- Which step caused the problem
We can help design tracing and monitoring so that AI behaviour can be investigated and improved.
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Security, Safety & Governance
Enterprise agents should not have unlimited access to information or business actions.
We can design solutions with appropriate controls around:
- Authentication
- Authorization
- Data access
- Tool access
- User context
- Sensitive information
- Prompt and instruction boundaries
- Business rules
- Content safety
- Auditability
Security and governance should be considered from the architecture stage, not added after the agent has been built.
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Production Integration
A successful proof of concept is not the same as a production-ready AI solution.
We can help move AI solutions into production by addressing areas such as:
- Application integration
- API architecture
- Identity and access
- Session management
- Scaling
- Reliability
- Error handling
- Logging
- Monitoring
- Deployment
- Cost management
Microsoft Foundry in the Real World – Experience Designing Modern AI Solutions
Modern AI solutions can become complex very quickly. A solution may start with a simple assistant but later require multiple agents, different models, enterprise knowledge, databases, APIs, dynamic instructions, multiple communication channels, voice, multilingual interactions, tracing and detailed control over how agents behave.
We have designed several solutions and products using modern AI and agentic architecture patterns.
Our experience includes designing solutions that combine:
- Single and multi-agent architectures
- Different orchestration patterns
- Dynamic instructions and context
- Enterprise knowledge
- Structured and unstructured data
- Database integration
- API and tool integration
- Multiple models
- Multilingual interactions
- Voice
- Multiple communication channels
- Evaluations and tracing
- Token and cost management
- Security and governance
This experience helps us approach Microsoft Foundry from a complete solution architecture perspective rather than concentrating only on creating an individual agent.
The objective is to build AI solutions that are useful, controllable, maintainable and ready to become part of real enterprise applications.
How Can You Engage with dinesQL?
Every organization is at a different stage of AI adoption. You may be exploring the technology, designing your first enterprise agent, already building a solution internally, or trying to move an existing proof of concept into production. We offer three primary engagement models.
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Complete AI Solution Implementation – Outsource the Entire Project to Us
You provide the business requirements and access to the required enterprise systems, and we take responsibility for designing and implementing the solution.
A complete engagement can include: Requirements → Use Case Design → Architecture → Model Selection → Agents → Knowledge → Tools & APIs → Integration → Evaluation → Deployment → Monitoring → Knowledge Transfer.
Depending on your requirements, the implementation may include:
- Microsoft Foundry environment
- Model selection
- Agent design
- Multi-agent orchestration
- Enterprise knowledge
- Retrieval
- Database integration
- API integration
- Tool development
- Voice and multilingual capabilities
- Application integration
- Evaluations
- Tracing
- Security
- Production deployment
This model is suitable when you need an experienced team to take responsibility for delivering the AI solution end to end.
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Architecture & Design – Let Us Design It Before You Build It
You may already have application developers and technical teams but need experienced architects to determine how the AI solution should be structured. We can work with you on:
- Use case assessment
- Foundry suitability
- Target architecture
- Single-agent versus multi-agent design
- Agent orchestration patterns
- Model strategy
- Knowledge architecture
- Retrieval strategy
- Data integration
- Tool and API architecture
- Context management
- Security
- Evaluation strategy
- Observability and tracing
- Token and cost considerations
- Production architecture
- Implementation roadmap
At the end of the engagement, your team can continue implementation with a clearly defined architecture and design approach.
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AI & Foundry Experts – Extend Your Existing Team
If your organization already owns the project, we can provide experienced architects, engineers and consultants to work alongside your internal team.
Resources can support specific areas such as:
- Agent architecture
- Microsoft Foundry
- Multi-agent design
- Retrieval
- Tool integration
- Enterprise data integration
- Voice
- Evaluation
- Tracing
- Production deployment
This model is suitable when you need additional AI expertise or implementation capacity without outsourcing the complete project.
Why Work with dinesQL?
We bring more than 20 years of experience in enterprise data, software architecture, analytics and integration, combined with hands-on experience designing modern AI and agentic solutions.
Although generative AI introduces new capabilities, successful enterprise solutions still depend on many familiar architectural disciplines:
- Application architecture
- Data architecture
- Integration
- APIs
- Security
- Identity
- Business rules
- Reliability
- Monitoring
- Performance
- Governance
- Production operations
AI introduces additional considerations such as:
- Model selection
- Prompt and instruction design
- Agent orchestration
- Retrieval
- Context management
- Tool selection
- Evaluation
- Tracing
- Token usage
- Hallucination and grounding
- Safety
Our focus is therefore not simply on creating an agent. Our focus is on designing and implementing the right enterprise AI solution using Microsoft Foundry.
Frequently Asked Questions
Yes. We can assess your business requirements, existing applications, enterprise data, APIs, security requirements and future plans and design an end-to-end Microsoft Foundry architecture. This can include models, agents, knowledge, tools, APIs, multi-agent orchestration, data integration, evaluation, tracing, security and production integration.
It depends on the problem. A single agent is often the right choice when the responsibilities are closely related and can be managed with one set of instructions and tools. Multiple agents may be more appropriate when the solution contains distinct responsibilities, specialized knowledge or workflows that need independent control. We evaluate the use case before recommending a multi-agent architecture.
A chatbot primarily provides conversational responses. An AI agent can go further by reasoning about a request, selecting tools, retrieving enterprise information, calling APIs and performing approved actions. The exact capabilities depend on how the solution is designed.
Yes. Agents can be integrated with enterprise knowledge and structured data through appropriate retrieval, data services, APIs and tools. This can include data from databases, Microsoft Fabric, Databricks, internal applications and other enterprise systems. The architecture should ensure that users and agents only access information they are authorized to use.
Yes. Microsoft Fabric can provide governed enterprise data and business context that can be used by AI and agentic solutions. For example, Fabric Data Agents can be integrated with Foundry Agent Service, allowing a Foundry agent to work with data available through Fabric. This is particularly useful when the AI solution needs to combine Fabric data with other knowledge sources, tools and APIs.
Yes. A Foundry-based application or agent can be designed to access data and services exposed from Databricks through appropriate integration patterns such as APIs, databases or enterprise application services. The appropriate approach depends on the type of data, security requirements and workload.
Yes. Existing APIs can be exposed as controlled tools that agents can invoke when needed. We can help design how tool descriptions, parameters, authentication, validation, error handling and business rules should be implemented so that the agent interacts with the API safely.
Yes, if that is part of the approved solution. An agent can call tools that create or update information in external systems. However, actions that change business data require stronger controls than read-only tools. Depending on the use case, we may recommend validation, explicit user confirmation, authorization checks and other guardrails before an action is executed.
Yes. We can design and implement multi-agent architectures using different orchestration patterns depending on the problem being solved. The solution may use specialist agents, routing, handoffs, supervisors, workflows or other collaboration patterns. We do not recommend multiple agents unless they provide a clear architectural benefit.
Yes. The models and surrounding solution can support multilingual interactions, depending on the selected model and requirements. We can design solutions where the same agent supports users in multiple languages while continuing to use the same enterprise tools, knowledge and business logic.
Yes. Voice can be added to an agent so users interact through speech rather than only text. A voice-enabled solution can combine speech recognition, agent reasoning, business tools and speech generation to provide real-time conversational experiences.
Yes. The agent and business logic can be designed as a centralized service while different channels provide the user interface. For example, the same underlying agent architecture can potentially support web, mobile, messaging and voice experiences. The exact implementation depends on the channel and application architecture.
This requires testing and evaluation. We can define representative questions and scenarios and evaluate areas such as answer quality, grounding, tool usage, instruction following and business-rule compliance. Evaluation should continue as the agent changes and new use cases are introduced.
Yes. Tracing and observability can help identify which model, instructions, retrieval results and tools were involved in generating a response. This is important for debugging complex agentic solutions and should be considered as part of the production architecture.
AI cost depends on several factors including model selection, number of model calls, prompt size, conversation history, retrieved context and generated output. We can help design strategies around model selection, context size, conversation management, retrieval, tool usage, multi-agent calls, token monitoring and usage tracking. The objective is to use the appropriate level of intelligence for each task rather than automatically sending every request to the most expensive model.
Yes. Many proof-of-concept solutions work well for demonstrations but require additional architecture before they are suitable for production. We can assess the existing solution and help address areas such as security, identity, enterprise integration, reliability, error handling, evaluation, tracing, scaling, deployment, monitoring and cost management.
Yes. You may engage us for a specific area such as architecture and design, agent development, multi-agent architecture, knowledge and retrieval, API and tool integration, enterprise data integration, Microsoft Fabric integration, Databricks integration, voice and multilingual capabilities, evaluation, tracing and observability, security or production deployment. You do not need to outsource the complete project.
Yes. We can provide technical resources who work alongside your existing application, data and AI teams while your organization retains ownership of the overall project. Resources can support a specific requirement or participate throughout the implementation.
Yes. Not every requirement needs an agent. Sometimes a traditional application, workflow, search solution or simpler AI integration is more appropriate. We can first understand the use case and recommend whether it should be implemented using a standard application, an AI assistant, a single agent, a multi-agent solution or a combination of approaches.
Start by talking with our AI Agent. Tell the Agent what you are planning to build, the business problem you are trying to solve, the systems involved and where you are in your AI journey. Our AI Agent can explain our Microsoft Foundry services, help you understand how we may be able to support your solution and answer questions about working with us. When you are ready, you can also make an appointment with one of our consultants directly through the AI Agent to discuss your requirements in detail. From there, we can determine whether you need a complete AI solution implementation, architecture and design support, or experienced AI and Foundry resources to work with your existing team. You can also write to us directly, go to Contact Us page and initiate the first conversation.
Planning an Enterprise AI or Agentic Solution?
Whether you are building your first enterprise agent, designing a multi-agent architecture, connecting AI to enterprise data and APIs, adding multilingual voice capabilities, or taking an existing proof of concept into production, talk with us. Talk with our AI Agent to discuss your requirements, understand how we can help and make an appointment with one of our consultants.
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