Build & Deploy an AI Assistant
A rapid 2–4 week engineering sprint deploying a functional, governed multi-agent AI prototype on your sovereign cloud environment — with real tool execution, MCP connectors, HITL approval gates, and an executive-ready demo. Proof of value before production spend.
Why a PoC Sprint Before Production?
Agentic AI systems are fundamentally different from traditional automation. Agents plan, use tools, access data, and take actions — and the gap between a compelling demo in a vendor sandbox and a governed deployment on your own infrastructure is where most government AI programs stall.
This sprint collapses that gap. In 2–4 weeks, your team has a working prototype — deployed on your cloud, connected to your data, governed by your HITL controls — that demonstrates concrete value to your agency's leadership, removes the guesswork from production scoping, and gives your engineering team a real architecture to build forward from.
Your sovereign cloud
Deployment target
2–4 weeks fixed scope
Duration
Live governed prototype
Output
What We Build
Multi-Agent Architecture Design
Design of the agent topology — orchestrator, sub-agents, tool-calling architecture, memory strategy, and inter-agent communication patterns using LangGraph, AutoGen, or Azure AI Agent Service based on your existing cloud and team capability.
Tool Calling & MCP Integration
Development of custom tool definitions and Model Context Protocol (MCP) server connectors for your specific data sources, internal APIs, and agency systems — giving agents real access to the information they need to act.
Sovereign Cloud Deployment
Deployment of the agent prototype to your existing cloud environment — Azure, AWS, or GCP — with sovereign landing zone configuration, network isolation, IAM role binding, and secret management aligned to your agency security standards.
HITL Approval Gate Implementation
Implementation of human-in-the-loop checkpoint design within the agent workflow — routing high-risk or irreversible actions to human approvers before execution, with configurable approval authorities and override paths.
Agent Monitoring & Observability
Instrumentation of the agent prototype with trace logging, token usage monitoring, latency measurement, and cost attribution — providing the observability foundation needed to operate and optimise the agent in production.
Production Roadmap & Architecture Handover
Documentation of the production hardening requirements, scalability design, security controls, and deployment architecture for moving the prototype to a production-grade system — with an effort-estimated roadmap for your engineering team.
What You Receive
Functional Multi-Agent AI Prototype
A working, deployed multi-agent AI prototype on your cloud environment — running real tool calls, accessing your data sources, and executing the target workflow end-to-end using LangGraph, AutoGen, or Azure AI Agent Service.
Custom Tool Calling & MCP Server Connectors
Purpose-built tool definitions and MCP server connectors for your agency systems — ready to be extended and reused in production agent deployments.
Sovereign Cloud Landing Zone Deployment
Fully deployed prototype on your existing cloud tenancy with network isolation, IAM configuration, secret management, and environment separation aligned to your agency security standards.
Interactive Executive Demo & Production Roadmap
A scripted, repeatable executive demo environment for board and steering group presentation, plus an effort-estimated production roadmap covering hardening, scaling, and full deployment.
Proven Delivery
A Freedom of Information conversational agent deployed in production at a Commonwealth department.
Sprint Timeline
Architecture Design & Environment Setup
- Stakeholder alignment: engineering leads, cloud platform team, business owner
- Use case scoping and agent workflow definition workshop
- Multi-agent architecture design: topology, orchestration framework, memory strategy
- Cloud environment access provisioning and landing zone configuration
- MCP server and tool calling scope agreement
Core Agent Build
- Agent orchestration layer implementation (LangGraph / AutoGen / Azure AI)
- Custom tool definitions and MCP server connector development
- Agency data source integration and access testing
- HITL checkpoint implementation and approval gate configuration
- Initial end-to-end workflow testing in sandbox environment
Deployment, Demo & Handover
- Sovereign cloud deployment with full IAM and network configuration
- Agent monitoring and observability instrumentation
- Executive demo environment preparation and scenario scripting
- Production roadmap development with effort estimates
- Documentation: architecture decision record, deployment runbook, operational guide
Executive Demo & Production Handover
- Live executive demo to program sponsor and engineering leadership
- Full deliverables handover: prototype, connectors, deployment, roadmap
- Engineering team walkthrough of architecture and codebase
- Optional: 30-day post-sprint production support check-in
Who This Is For
- CTOs and AI Platform Leads ready to prove agentic AI value
- Engineering teams that need an architecture reference implementation
- Innovation leads with a specific workflow automation use case
- Agencies that completed a Data or AI Assurance sprint
- Accountable authorities requiring a live demo before approving production budget
Common Triggers
- Agency approved AI exploration budget with a demo milestone
- Vendor demo showed promise but lacked agency integration
- Engineering team lacks agentic AI architecture experience
- Production deployment stalled waiting for a reference design
- Need a governed prototype before committing production spend
- Specific high-value workflow identified for automation
Related Advisory
AGENTAI Engineering & Agentic Systems
Full advisory pillar →
Ready to ship a live agentic prototype?
Fixed-scope. Senior-led. A working governed multi-agent prototype on your cloud in 2–4 weeks — with an executive demo and production roadmap.
Book AI Assistant SprintDownload Capability Statement