Agentic AI engineering.
Build AI systems that do more than answer questions. Enfuse engineers coordinated agents that retrieve business context, use approved enterprise tools, complete multi-step workflows, and involve people at the right decision points.
Four parts of a production agent system
Orchestration and workflow execution
Specialized agents operate within defined workflows, with persistent task state, controlled handoffs, checkpoints, retries, failure handling, and approval requirements. They reach existing applications through APIs and governed tool interfaces. We combine deterministic software with AI reasoning — not every problem needs multiple agents, and more agents do not automatically produce better results.
Enterprise context and knowledge
Connect agents to the information they need without giving them unrestricted access to everything. We engineer permission-aware retrieval, document processing, structured and unstructured data integration, business terminology, source attribution, data freshness, and bounded workflow memory so AI operates within the organization's business rules and access boundaries.
Security, evaluation, and AgentOps
Reliable agents require more than a good prompt. We build agent and service identities, least-privilege permissions, controlled tool execution, human approvals, audit trails, monitoring, and pre-production evaluation — so you can understand what a system did, detect failures, and improve behavior through reviewed, measurable changes rather than uncontrolled self-modification.
Interoperability and integration
Model Context Protocol (MCP) and agent-to-agent (A2A) interfaces are useful integration mechanisms for connecting agents to tools and to each other. We use them where they fit, alongside the security, reliability, and portability engineering they do not provide on their own.
Need the whole system inside your boundary? See on-premises multi-agent systems.
Microsoft, Google Cloud, and customer-controlled infrastructure
Technology options are selected per project. Not every solution needs to connect both clouds.
Microsoft Azure and Microsoft-aligned environments
Engineer enterprise AI workflows across Microsoft environments, connecting agents to approved business applications, data platforms, identity services, and cloud-native infrastructure. Extend selected architectures to customer-controlled infrastructure when operational and security requirements call for local deployment.
Technologies may include Microsoft Agent Framework, Microsoft Foundry, Azure Kubernetes Service, Azure data services, and supported Microsoft identity integrations. Running the open-source framework locally does not make Foundry-managed services available locally.
Google Cloud and Google-aligned environments
Build agentic applications and data-connected workflows using Google Cloud technologies, with deployment architectures selected for the customer's performance, integration, and governance requirements.
Technologies may include Google's Agent Development Kit, Gemini services where available, managed agent services, BigQuery, Cloud Run, and Google Kubernetes Engine. Self-hosted ADK applications still need model endpoints and dependencies that fit the chosen boundary.
Customer-controlled infrastructure
Deploy on private infrastructure with an architecture matched to the customer's hardware, operating model, and security requirements. Scope can include Kubernetes, GPU systems, local inference, infrastructure automation, private data services, and the lifecycle tooling required to operate the system.
We favor architectural choice, while being explicit about the dependencies each option carries.
The operating model behind this — human and agent teams inside a boundary. Comparing partners? See how the sovereign AI vendor categories differ.
Plan a production deployment
One working session to scope the workflow, the data, the controls, and the deployment boundary.