AI Agents & Agentic Systems
Spec-Driven AI Agents with Our Own Multi-Agent Framework
We build production-grade AI agents using a spec-driven methodology and our proprietary multi-agent framework. MCP proxy development, custom skills, orchestrated workflows — running on public LLMs (Claude, GPT, Gemini) or self-hosted SLLMs (Llama, Mistral, Phi). From single tools to enterprise-scale agent systems.
Capabilities
What We Build
Spec-Driven Agent Development
Every agent starts with a formal specification — inputs, outputs, tool access, guardrails, and success criteria defined before code. This methodology eliminates ambiguity, enables reproducible testing, and ensures agents behave predictably in production. Specifications become living documentation that evolves with your system.
Proprietary Multi-Agent Framework
Our custom-built multi-agent framework orchestrates specialized agents that collaborate on complex tasks. Shared context, delegated tool use, coordinated planning, and automatic fallback handling. Designed for production reliability with built-in observability, cost tracking, and human-in-the-loop checkpoints.
MCP Proxy Development & Custom Skills
We build MCP servers, token-optimizing proxies, and custom skill libraries that connect agents to your APIs, databases, and external services. Semantic caching, intelligent model routing, and context window management reduce LLM costs by 40-70% while maintaining quality. Standard MCP interfaces mean any compatible model can use any tool.
Public Models & Self-Hosted SLLMs
Deploy agents on Claude, GPT, Gemini for maximum capability — or on self-hosted small language models (Llama, Mistral, Phi) for data sovereignty, lower latency, and cost control. Our framework abstracts the model layer so you can switch or combine models without rewriting agent logic.
FAQ
Frequently Asked Questions
More Case Studies
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UNICEF Digital Wallet: Financial Inclusion for 4M+ People
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How Xcapit Labs built a platform that enables collaborative machine learning on fully encrypted data using Fully Homomorphic Encryption (FHE), so organizations can train AI models together without ever exposing their sensitive information.
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Automated tests
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ML algorithms
Ready to Build Your AI Agent System?
Tell us about your use case and we'll architect the right agent solution — whether it's a single MCP tool or a full multi-agent system.