Custom AI agent development
When an agent needs more than Copilot Studio defaults: MCP servers, retrieval pipelines, Microsoft Foundry models, and policy enforced at the tool layer.
Short answer
Custom AI agent development covers agents that go beyond standard Copilot Studio configuration: Model Context Protocol servers for governed tool access, retrieval pipelines over large document collections, Microsoft Foundry models, and small local models for classification. Puget Sound AI builds them at a fixed price for commercial, GCC, and GCC High environments.
What we engineer.
The agent type follows the use case, the accuracy requirement, and the cloud.
Copilot Studio agents
Generative orchestration, citation-bound retrieval, approval flows, and Teams deployment.
MCP servers
Governed, audited access to backend systems through one tool layer that every agent reuses.
Retrieval pipelines
Chunk-and-embed pipelines over SharePoint content, indexed in Azure AI Search inside your boundary.
Vector retrieval
Hybrid keyword and vector search with reranking, tuned for accuracy over large libraries.
Local small models
Classification, extraction, and routing on approved compute, with no external API calls.
Regulated designs
Architectures for CMMC Level 2 environments, with data flow documentation for your security office.
How an agent engagement runs.
- 20 minutes
Scoping call
Use case, cloud, connectors, and a fixed-price quote.
- Week 1
Architecture
Agent type, tool routing or retrieval design, authentication, policy layer, and logging, signed off first.
- Build
In your tenant
All engineering inside your environment, with weekly demos.
- Before launch
Validation
Edge cases, adversarial inputs, and accuracy checks against your sources.
- Handoff
Document and train
Diagrams, source, MCP tool definitions, a runbook, and training.
What government clouds change.
Every design is checked against your actual cloud before architecture is committed.
- Connectors: GCC and GCC High catalogs are smaller than commercial. We confirm availability first.
- Boundaries: every data flow stays inside the authorized boundary, documented before and during the build.
- Feature timing: Copilot Studio and Foundry features reach government clouds later. We do not assume commercial documentation applies.
- Authorization: each build includes a data flow record and service account review for your authorization package.
Custom agent questions
When should an agent be built in Copilot Studio and when in code?
Copilot Studio fits most agents that live in Teams and Microsoft 365 and is easier for your team to maintain. Coded agents on Microsoft Foundry fit when you need specific models, custom pipelines, or agents inside your own applications.
What is an MCP server?
Model Context Protocol is a standard way to give AI agents governed access to tools and data. One MCP server can serve many agents, with authentication, logging, and policy in one place.
What is retrieval-augmented generation?
The agent retrieves passages from your documents before answering, so answers come from your content rather than general model knowledge. It matters most for policy, procedure, and contract questions.
How do you handle government cloud limits?
We confirm connectors, models, and endpoints available in your cloud before design. What works in a commercial lab does not always carry over to GCC or GCC High.
Train your team first.
Agents last longer when the people around them understand them. Power users and admins can train before or after the build.
Train your team on the AI you already pay for.
Tell us who needs training and we will send a recommended program and a fixed price. Or book 20 minutes and talk it through.