Custom AI Agent Development
Copilot Studio agent engineering; MCP server development; RAG pipelines; vector retrieval; and secure agent design for GCC and GCC-High environments. Production-proven architecture. Nationwide delivery. SAM.gov active.
Custom AI agent development at Puget Sound AI means engineering agents that meet government accuracy, compliance, and auditability requirements from the first line of code. Copilot Studio agents, custom MCP server infrastructure, retrieval-augmented generation pipelines, and vector search architectures, all designed for the GCC and GCC-High boundaries where commercial AI agent patterns fail. Nineteen production agents and systems delivered this year in GCC environments across the United States.
What We Engineer
Each agent type is chosen based on your use case, accuracy requirements, and GCC constraint profile.
Copilot Studio Agents
Generative orchestration agents built in Microsoft Copilot Studio for GCC and GCC-High tenants. Tool routing, citation-bound knowledge retrieval, approval workflows, and Teams-native deployment. GCC connector and licensing constraints applied from the start.
MCP Server Development
Custom Model Context Protocol servers that give AI agents safe, auditable, policy-governed access to backend systems inside the GCC boundary. Service principal authentication, least-privilege scope, policy enforcement at the tool layer, and full audit logging to SharePoint. Each additional tool inherits the compliance stack for free.
RAG Pipelines
Retrieval-augmented generation pipelines that ground AI agent responses in your agency documents, SharePoint libraries, and knowledge bases. Chunk-and-embed pipelines for SharePoint content indexed into Azure AI Search (Government) within the FedRAMP boundary.
Vector Retrieval Systems
Semantic vector search over government document collections using Azure AI Search in Government regions. Hybrid keyword-and-vector retrieval tuned for accuracy over large SharePoint libraries. Reranking and relevance filtering applied before responses are generated.
Local SLM Inference
Small language model inference for classification, extraction, and routing tasks that do not require cloud API calls. Deployed inside your GCC boundary on approved compute. Reduces latency, API costs, and data-movement risk for high-volume classification workflows.
Secure Agent Design for GCC-High
Agent architectures for GCC-High environments operating under DFARS, ITAR, and DoD IL4/IL5 requirements. Feature parity verified; connector inventory reviewed; data flow documentation produced as a compliance deliverable for your information security office.
GCC and GCC-High Constraints Are Real
Connector Restrictions
GCC restricts or blocks many third-party connectors available in commercial Power Platform. Every agent design is validated against the actual GCC connector catalog before architecture is committed. We do not design around connectors and discover later they are unavailable.
Data Residency Boundaries
Every data flow in a GCC agent must stay within the FedRAMP-authorized boundary. Agent designs that send data to commercial Azure endpoints, external APIs, or non-FedRAMP services violate the boundary. We document every data flow before and during build.
Feature Parity Gaps
GCC-High lags GCC; GCC lags commercial M365 on new feature releases. Copilot Studio capabilities, Power Platform features, and Azure AI Foundry integrations all have GCC-specific availability timelines. We verify before we design; we do not assume commercial documentation applies.
ISO and ATO Alignment
Government AI agents must satisfy information security officers and fit within the agency ATO boundary. Every engagement includes data flow documentation, service account audit, and a security review package your ISO can evaluate. No surprises at the ATO review stage.
How Agent Engagements Are Structured
Fixed scope; defined deliverables; the engineer who scopes it builds it.
- Scoping call (20 min): Review your GCC or GCC-High environment, confirm the target use case, verify connector and feature availability, and agree on a fixed-price quote.
- Architecture definition: Written specification covering agent type, tool routing or RAG pipeline design, data sources, authentication model, policy enforcement layer, and audit logging. Signed off before build starts.
- Build inside your tenant: All engineering executed inside your GCC or GCC-High environment. No external data processing. Weekly progress demos. Security team can review at any point.
- Testing and validation: Functional testing, edge case and adversarial input testing, accuracy validation against your knowledge sources, and user acceptance testing.
- Documentation and handoff: Architecture diagrams, source code, MCP tool definitions, operational runbook, and staff training session. Your team can operate and extend every system independently.
Need a starting point? The GCC AI Jumpstart delivers 2–3 production agents in 6–8 weeks with governance and training included. Fixed price: $40,000–$60,000.
Puget Sound AI is headquartered in Puyallup, Washington, and delivers custom AI agent development to government agencies and enterprises across the United States. Remote-first delivery. SAM.gov active (UEI SU4QWJZWXY97, CAGE 17DX6). Available under FFP, T&M, micro-purchase, and SAP (FAR 13) vehicles. VOSB; VetCert in progress. Regional on-site service available for agencies in Tacoma | Seattle | Puget Sound.
Custom AI Agent Development Questions
What is the difference between a Copilot Studio agent and a custom AI agent built with Python or other code?
Copilot Studio agents are built within the Microsoft low-code platform and deploy natively into Microsoft Teams and other M365 surfaces without custom hosting. Coded agents built in Python or other languages offer more flexibility in model selection, architecture, and integration patterns, but require compute hosting within the GCC boundary. Puget Sound AI builds both; the right choice depends on your use case, licensing, and infrastructure constraints.
What is an MCP server and why would my agency need one?
Model Context Protocol (MCP) is an architecture layer that gives AI agents safe, auditable access to backend systems without building bespoke connectors for every integration point. In GCC, an MCP server enables agents to call Graph API, PowerShell cmdlets, and internal APIs with least-privilege service accounts, policy enforcement at the tool layer, and full audit logging inside the GCC boundary. The first tool takes the longest to build; every subsequent tool inherits the full compliance stack at a fraction of the original cost.
What is a RAG pipeline and when does an agent need one?
Retrieval-augmented generation (RAG) is the pattern where an AI agent retrieves relevant content from your document sources before generating a response, grounding answers in your agency data rather than general model knowledge. An agent needs a RAG pipeline when accuracy against specific internal documents matters: policy manuals, procedure guides, contract terms, legal references. Without RAG, the agent generates from model weights; with RAG, it retrieves from your approved content and cites the source.
Can you build AI agents for both GCC and GCC-High environments?
Yes. GCC-High engagements require a pre-architecture review of feature availability, connector restrictions, and licensing differences specific to the GCC-High environment. GCC-High often lacks features or connectors available in GCC, which affects architecture decisions. We do not adapt commercial or GCC architectures to GCC-High; we design for the target environment from the start.
What does "zero-hallucination design" mean in practice?
It means the agent is architecturally constrained to return only content that can be traced to an approved source, not content generated from model knowledge. For citation-bound knowledge agents, this means verbatim retrieval with explicit source citations. For orchestrated agents, this means tool results are returned as-is without language model embellishment. When the agent cannot find an answer in your approved sources, it says so explicitly. This is enforced at the design level, not through prompting alone.
Ready to Build a Production AI Agent for GCC?
Book a 20-min scoping call. We will review your GCC or GCC-High environment and define agent architecture before any work begins.