AI Agent Development and Integration Services
AI agent development services cover building agents and integrating them into a company’s systems and data. Unlike a chatbot, an agent built through custom AI agent development can analyse information, prepare documents, make limited decisions and trigger actions across CRM, ERP, SharePoint, Microsoft 365, email, documents, databases and APIs.
AI agent development services in real business scenarios
The AI agent development services we offer can be customised to your organisation’s specific systems, data and policies.
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Custom AI agent development is tailored to a specific company’s tasks.
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Enterprise AI agents can integrate multiple systems and data sources.
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AI agent integration connects the agent with the systems and data already in use within the company.
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For security reasons, the agent’s permissions, decision-making limits and approval points are established.
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The model is selected based on the task, desired quality, cost and security requirements.
AI Agent Development Services
Custom AI agent development aligns each agent with the organisation’s processes, systems and tasks, so AI agent development services cover not only model selection but also company context and instructions, decision logic, the user interface, data access, action controls and monitoring requirements.
When may such enterprise AI agents be required? Most often, when a standard AI assistant that mainly answers questions is no longer sufficient because the task requires several information sources to be combined, defined actions to be performed or work to take place across different systems. An agent can also form part of broader enterprise automation solutions and operate alongside other automation solutions.
AI Agent Solutions That We Can Develop and Integrate
Custom AI agents
Document analysis agents
Information search agents
Data preparation agents
ERP-integrated agents
Email and document agents
AI voice agents
Enquiry processing agents
Enterprise AI agents
Document drafting agents
Data analysis agents
CRM-integrated agents
Microsoft 365 and SharePoint agents
API-connected agents
Multi-agent systems
AI Agent Integration
with Business Systems
If an organisation is already using certain systems and does not intend to change them, AI agent development services are designed to work with these systems. AI agent integration may encompass CRM, ERP, Microsoft 365, SharePoint, email, documents, databases, APIs and other internal business systems.
The integration method is selected based on the IT environment, system capabilities and access and security requirements. The agent can be connected via REST APIs, Microsoft Graph, Power Automate, CRM and ERP connectors, direct database access or document repositories. Users can interact with it via Teams, a portal, a business system or email, whilst the agent can also run in the background and use real-time or external data when the task requires it.
AI Voice Agents and Voice Interfaces
AI voice agents are one form of agent interface. How is it unique? Voice AI agents can receive or make calls, register enquiries, search for information during a conversation and trigger actions within systems.
Voice AI platforms such as Newo AI, Retell AI, or similar technologies can be used for such solutions. The voice agent operates within the same systems, data access, permissions, and action limits as other AI agents.
Power Automate
Power Apps
Power BI
How Do AI Agents Work with Company Data?
AI agents can work with company data by securely connecting to relevant systems and using the information available within them to support responses and actions. Each agent is tailored to the organisation’s context, data, instructions and operational boundaries, with access and permissions limited to what is required for each task.
Company Data and Documents
The agent is granted controlled access to the sources required to perform the task.
Business Rules and Instructions
The agent operates in accordance with established rules, permissions and approval points.
Evaluation and Refinement
Expected and edge-case behaviour is tested, whilst instructions and safeguards are refined.
From Information to Decisions and Actions
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Information retrieval and analysis. The agent uses only the sources available to it.
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Document preparation. It can prepare drafts of documents or emails in accordance with the rules.
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Decisions within defined boundaries. AI agent development services can include AI agents with defined decision boundaries that independently resolve only clear, low-risk tasks.
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Actions within systems. AI agents taking actions across systems can initiate only the actions permitted by their assigned access rights.
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Human approval. Higher-risk actions require human approval based on data sensitivity, financial or legal impact, reversibility and the organisation’s rules.
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Action audit. Actions are logged and can be verified, with the required controls defined as part of how AI projects are delivered.
Workflow Automation Vs AI Agents
Predefined automated workflows
Operate according to a predefined sequence of steps and rules.
When faced with an unforeseen situation, the system executes a predefined exception or hands the task over to a human.
Data is used in specific, predefined process steps.
The outcome is determined according to pre-defined rules and conditions.
Best suited to repetitive processes with a clear and stable workflow.
AI agents
Select a permitted action based on the task, context and defined boundaries.
When faced with an unforeseen situation, the agent assesses the context but does not exceed the limits set for it.
Information is retrieved and analysed from various connected sources depending on the task at hand.
The agent can make a decision within the defined limits or seek human approval.
Best suited to tasks that require interpreting information, responding to context and using multiple tools.
Model-Agnostic
AI Agent Architecture
Power Automate
Power Apps
Power BI
Model-agnostic AI agent development does not tie the solution to a single AI model provider. For each agent, a model or combination of models is selected based on the task, output quality, cost, security requirements, data location and required functionality.
Depending on these requirements, AI agent development services can be built using OpenAI, Claude, Gemini, Grok, DeepSeek or another suitable model. As the agent’s architecture is decoupled from any single provider, the model can be changed if necessary without having to rebuild the entire agent.
Ready to Build AI Agents
That Use Your Systems?
Automation & Data Audit helps to assess processes, systems, data and automation opportunities before selecting AI agent development services. Based on this assessment, we can identify the most suitable initial use case for the agent and its limitations.
FAQ
AI agents use the data, instructions, AI model and tools provided to perform a specific task. AI agent development services define their access, permitted actions and human approval points, enabling them to respond, prepare a document or initiate an authorised action.
The main difference is that a chatbot primarily answers questions during an active conversation, whilst an AI agent can perform a task assigned to it. It utilises connected data and business systems, so it can find and analyse information, prepare a document or, within set limits, initiate an action. A chat window may be one of the AI agent’s interfaces, but its functionality is not limited to this.
Workflow automation executes a predefined sequence, whilst an AI agent can interpret context and select a permissible action. Workflow automation is suitable for stable processes, whilst an agent is suited to changing information. Both solutions can operate together.
AI agents can be integrated via REST APIs, Microsoft Graph, Power Automate, system connectors, direct database access or document repositories. The method is chosen based on the IT environment, system capabilities and access and security requirements. Multiple integrations can be combined within a single solution.
Yes, AI agents can work with CRM and ERP systems provided there is a secure method of integration. Via APIs, standard connectors or other agreed-upon access methods, they can retrieve the necessary data and initiate authorised actions. The agent is granted only the permissions required for its function.
AI agent training involves adapting the agent to the company’s data, instructions, rules and operational limits. Its access is controlled, and its behaviour is verified through evaluation tests. This does not involve training a new foundation model on the company’s data.
An AI agent’s actions are controlled by permissions, access limits, approval points, audit logs and error handling. Clear, low-risk actions may be carried out automatically, whilst actions with legal or financial implications may require human approval. The threshold is agreed upon for each project.
The cost of an AI agent project depends on the complexity of the tasks, the scope of the systems and integrations, data readiness, and access, security and control requirements. It is also influenced by integration, testing, deployment and maintenance. Therefore, AI agent development services are assessed on a case-by-case basis, taking into account the specific use case and IT environment.