AI AGENT INTEGRATION
Let customers and partners use your service through AI agents
We integrate MCP and APIs so your service can securely accept requests from AI agents and perform only approved actions.
USE CASES
What tasks can AI agents perform in your service?
For services where customers, partners, or employees regularly interact with data and perform recurring operations.
B2B procurement
A partner's AI agent checks the catalogue, pricing, and availability, places an order, and retrieves its status directly from the system.
Booking and scheduling
A customer asks an AI agent to find a suitable time, make or reschedule a booking, or check its status. The action is completed directly in the system.
SaaS and business platforms
Users interact with the service through a familiar AI agent, without switching between interfaces.
Catalogues and marketplaces
An AI agent searches, filters, and compares products, properties, or offers, checks availability, and submits a request to the system.
INTEGRATION SCOPE
What we implement
We implement the integration end to end: MCP, APIs, access control, reliability, testing, and ongoing development.
MCP server and agent tools
We expose approved service operations as MCP tools with clear parameters, outputs, and usage rules.
API for agent operations
We create or restructure the API layer with clear schemas, input validation, and predictable responses.
Authentication and access control
We configure OAuth, tokens, roles, and approval for critical actions. Each agent receives only the permissions it needs.
Reliability and auditability
We add request limits, duplicate-operation protection, error handling, and a complete action log.
Testing with AI agents
We test data access, operations, errors, and permission boundaries across multiple compatible agent systems.
Documentation and development
We prepare tool descriptions, schemas, and examples. After launch, we maintain versions and add new operations.
HOW WE WORK
From business scenarios to integration launch
- 01
Scenarios and operations
We define AI-agent tasks, user roles, and the required constraints.
- 02
API and access model
We design operations, data schemas, authentication, permissions, and approval for critical actions.
- 03
MCP integration
We build the MCP server, connect the API, and define tools for agent systems.
- 04
Testing and launch
We test scenarios, failures, and security, then deploy the production version.
- 05
Ongoing development
We add new operations, maintain versions, and monitor integration stability.
Where can AI-agent integration be useful for your service?
We will review customer, partner, and internal scenarios, assess the current API, and recommend an MCP integration architecture.
After the conversation, you will receive a list of priority operations, a recommended access model, and the appropriate integration format.