API & MCP Development

Build the Backbone for Intelligent Automation

Behind every effective AI agent is a reliable and secure infrastructure. At LoadSys, we help teams build the robust APIs and governance frameworks required to power agentic AI systems. Whether you’re starting from scratch or upgrading existing services, we create the data and interface layers that allow LLM agents to safely act, retrieve, and reason at scale.

Model Context Protocols (MCPs) are like a playbook for AI agents. They define:

What tools the agent can use (APIs, databases, functions)
What information it can access—and when
How it should behave in specific scenarios
When to escalate or ask for help

We work with your teams to create reusable MCP frameworks that ensure consistency, security, and explainability across all AI-powered workflows.

API Architecture & Development

We build modern, scalable REST and GraphQL APIs that power LLM agents, applications, and services. All APIs include logging, versioning, and robust authentication.

MCP Design & Implementation

We co-create Model Context Protocols that define how agents behave, access tools, and handle business logic—balancing autonomy with safety.

Data Access Layers

We create data connectors and abstraction layers to unify data across cloud storage, databases, SaaS tools, and files—enabling structured, reliable access for AI agents.

Our Approach

Our process combines strategic planning, secure engineering, and governance modeling to ensure every API and MCP implementation is production-ready and future-proof. We collaborate closely with your team to design the infrastructure your agents need to thrive—while keeping performance, security, and maintainability at the core.

Discovery & Architecture

We begin by understanding your systems, data flows, and agent goals. We define the interfaces, access patterns, and governance models needed to support AI-driven automation.
API & Data Layer Development

We design and build secure, documented APIs and connectors that provide structured access to your internal tools, databases, and third-party services. Our goal is to create stable, scalable interfaces that LLM agents can rely on.
MCP Design & Governance Modeling

We co-develop Model Context Protocols (MCPs) that control agent access and behavior. This includes setting boundaries, escalation rules, and toolchain permissions to ensure safe, compliant automation.
Testing & Deployment

We test all components in sandbox environments before deploying to production. Final rollout includes observability tooling, audit logging, and optional training for internal developers or embedded Agent-Coders.

Solutions

From unified access to cross-system automation, our API and MCP development services unlock the full potential of agentic AI systems. These solutions are built for security, scale, and real-world execution—empowering AI agents to perform high-impact tasks safely and efficiently.

Unified API Gateway for AI Agents

A central API gateway that exposes and governs access to internal services, allowing LLM agents to perform tasks securely across systems.

Cross-System Process Automation

Custom APIs and MCPs that allow agents to automate workflows like employee onboarding, purchase order processing, or multi-step form generation across tools.

AI-Powered Document Workflows

APIs that let agents retrieve, classify, redact, and route documents (e.g., legal, financial, or HR) with embedded rules via MCPs.

Knowledge Retrieval Interfaces

Data access layers that let agents securely pull structured and unstructured content from files, wikis, and databases—enabling RAG (retrieval-augmented generation).

Context-Aware CRM Assistants

APIs and MCP logic that give agents real-time access to CRM data, enabling dynamic updates, lead scoring, or personalized outreach automation.

Multi-Agent Coordination Frameworks

Infrastructure to support multiple agents collaborating via defined APIs and MCP roles—useful for complex scenarios like support escalation or multi-department workflows.