AI Agents & MCP

Agents are the new workforce. Give them guardrails, not root.

Everyone wants AI agents touching real systems — Kubernetes, GitHub, dashboards, ticketing. Nobody wants an unaudited LLM with production credentials. The gap between those two is an engineering discipline.

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What you get out of it

Platform tooling safely exposed to agents through purpose-built MCP servers
Non-human identities with scoped permissions, audit trails and kill switches
Multi-agent workflows that cut toil: incident analysis, runbook generation, infra review
A governance model your security team actually signs off on

What we deliver

MCP server design and implementation for your internal tools and APIs
Agent orchestration setup (LangGraph, AutoGen, Semantic Kernel) with human-in-the-loop controls
Non-human identity and permission model for agent credentials
Observability for agent actions: full audit log, replay and anomaly alerts
Pilot use case delivered end-to-end (e.g. automated incident triage)

Tools we work with

MCPLangGraphAutoGenSemantic KernelAzure OpenAIEntra Workload IDOpenTelemetry

Where we’ve done this before

Multi-national enterprise

GenAI platform on GPU-backed AKS with cost control built in

Production GenAI and agentic workloads with full audit trails, evaluation gates on every model change, and FinOps controls that eliminated idle GPU burn and capped token spend per workload.

Related services

Platform Engineering

Golden paths, self-service environments and reusable IaC modules — so your developers ship instead of filing tickets.

DevOps & CI/CD

Pipelines with quality gates, PR validation and GitOps delivery — releases become boring, in the best way.

DevSecOps

SAST/SCA, IaC scanning, SBOMs and signed artifacts wired into the pipeline — security that doesn’t slow delivery.

Ready to fix ai agents & mcp?

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