Design Time

Build Studio

Your AI Agent Architect

An intelligent 14-step design wizard that transforms business requirements into deployable agent packages — complete with compliance validation, governance planning, and engineering specifications. No manual coding. No ambiguous handoffs. No compliance surprises.

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Design Steps

Compliance Checks

Cloud Platforms

Governance Domains

Overview

What Build Studio Does

Build Studio is the design-time engine of Agent Accelerator. It guides your team through a structured, AI-assisted process that transforms business requirements into deployable agent packages — complete with compliance validation, governance planning, and engineering specifications.

The 14-step wizard is divided into two phases: a Design Phase (Steps 1–7) that captures requirements and builds the architecture, and a Generation Phase (Steps 8–14) that produces deployment-ready packages, compliance reports, and engineering specs.

Design Phase

From Requirements to Architecture

A collaborative design session where an AI architect helps you define every aspect of your agent.

Step 1

Initialization

Set up the project, name the agent, and capture the business context. Build Studio detects existing designs for continuation and loads accumulated knowledge from previous agent builds.

Step 2

Agent Identity

Define who the agent is: its name, persona, expertise areas, and communication style. Configure identity providers (Entra ID) for automatic user context population — the agent knows who it's talking to without asking.

Step 3

Capabilities & Tools

Using AI-powered research against official documentation (Microsoft Learn, platform docs, industry patterns), Build Studio recommends the optimal set of tools and capabilities. You review and refine; Build Studio researches and proposes.

Step 4

Workflows & Orchestration

Define how the agent conducts conversations. Build Studio recommends Generative Orchestration — where AI dynamically decides which tools to invoke based on natural conversation — over rigid menu-driven interactions.

Step 5

Engineering Dependencies

Automatically classifies every integration as either platform-native (deploys with the solution) or an engineering dependency (needs custom development). Engineering dependencies get detailed specifications, not vague requirements.

Step 6

Platform Selection

Choose your deployment targets: Microsoft Copilot Studio (production-ready), AWS Bedrock (POC), Google Vertex AI (POC), or all three from a single design.

Step 7

Design Review

Comprehensive validation of the complete agent design before entering the generation phase. Your team reviews and approves every decision.

Generation Phase

From Architecture to Deployment

Your validated design becomes production-ready packages, compliance reports, governance plans, and engineering specifications.

Step 8

Generation Mode

Select which platforms to generate for. One DSL, multiple outputs.

Step 9

Copilot Studio Package

Generates a complete, importable Microsoft Power Platform solution: bot configuration, GPT instructions, custom MCP Gateway connector, Power Automate flows, Adaptive Cards, knowledge base config, and topic definitions.

Step 10

Compliance Validation

Automatically scans the generated package against 73 enterprise security checks across 8 compliance domains. Maps findings to GDPR, SOC 2, HIPAA, and ISO 27001 with specific remediation guidance.

Step 11

Governance Planning

Generates a comprehensive operational governance plan covering 8 domains: ownership, deployment strategy, DLP, sharing controls, responsible AI monitoring, licensing, incident response, and documentation.

Step 12

AWS Bedrock Generation

Produces Bedrock agent definitions, Lambda function scaffolds for each action group, S3 knowledge base configuration, and CloudFormation/CDK deployment templates. POC

Step 13

Google Vertex AI Generation

Produces Vertex AI agent configuration, Cloud Function scaffolds, Dialogflow CX configs (where applicable), and Terraform deployment templates. POC

Step 14

Engineering Specs & Completion

Auto-generates detailed engineering specs for every custom dependency: MCP server plugin specifications with tool definitions, authentication requirements, error handling patterns, testing strategies, and sprint-level development roadmaps.

The DSL

Your Platform-Agnostic Source of Truth

At the heart of every agent design is a single YAML file — the Agent DSL. This file captures everything about your agent in a format that is platform-agnostic, human-readable, machine-parseable, version-controlled, and extensible.

Metadata

Name, description, version, author, target platforms

Identity

Persona, expertise, communication style, identity provider

Capabilities

User-facing capabilities with AI research rationale

Tools

Complete tool definitions with parameters, outputs, integration type

Workflows

Conversation orchestration, phases, triggers, fallback behaviors

Dependencies

Engineering dependencies with specifications and priority

Platform Hints

Platform-specific configuration (channels, knowledge sources, models)

Enhancements

Optional capabilities (memory, proactive behaviors, personalization)

Companion Design Artifacts

Every DSL is accompanied by documentation that ensures when you revisit an agent months later, you understand not just what was built, but why.

requirements.md — Original business requirements preserved for future reference
design-decisions.md — Architecture Decision Records (ADRs) capturing why choices were made
research-notes.md — AI research findings with documentation sources

Design Patterns

Intelligent Design Patterns

Build Studio encodes battle-tested enterprise agent patterns into every design.

Generative Orchestration

Instead of rigid decision trees ("Press 1 for support, 2 for billing"), agents use AI to understand intent and invoke the right tools dynamically. Users say what they need in natural language; the agent figures out the rest.

Silent KB Deflection

Before creating a support ticket or escalating, the agent automatically searches knowledge bases. If a strong match is found (above configurable confidence threshold), it presents the solution. Tickets only created when KB can't help.

Smart Entity Resolution

When a user says "my ticket" or "that issue," the agent uses AI to disambiguate — calling lookup tools automatically, caching resolved entities in conversation context, and never asking for information it should already know.

Implicit Confirmation

The agent confirms through action, not dialog. Instead of "Would you like me to create a ticket?", it creates the ticket and reports: "Ticket INC0123456 created, assigned to Network Team, P2 priority." Fewer round-trips, faster resolutions.

Progressive Information Gathering

Start with minimum required information. Infer what you can from context, identity provider, and conversation history. Ask only for what's genuinely missing. Never present a form when a conversation will do.

Cascading Resolution Pipeline

For complex scenarios: Intent Check, then Knowledge Base Search, then Service Catalog Search, then Graceful Fallback. The agent never leaves a user with "I don't understand" — it always has a next step.

Output

What Gets Generated

From a single DSL, Build Studio produces platform-specific packages and comprehensive engineering documentation.

Microsoft Copilot Studio

Production Ready

A complete Power Platform solution package ready for import:

Bot configuration with Generative AI Actions
GPT instructions with tool orchestration
Custom MCP Gateway connector (OpenAPI)
Power Automate flows
Adaptive Card templates
Knowledge base & topic definitions

AWS Bedrock

POC

Scaffolded agent definitions and deployment templates:

Agent definition JSON with action group mappings
Lambda function scaffolds (one per action)
S3 knowledge base configuration
CloudFormation / CDK templates
Deployment guide

Google Vertex AI

POC

Scaffolded agent configuration and deployment templates:

Agent configuration YAML
Cloud Function scaffolds
Dialogflow CX flow definitions
Terraform deployment templates
Deployment guide

Engineering Specifications

For every dependency that can't be deployed with the platform solution, Build Studio auto-generates:

MCP Server Plugin Spec

Tools, parameters, auth, errors

API Documentation

Endpoint docs and contracts

Infrastructure Requirements

Hosting, scaling, networking

Development Roadmap

Sprint-level planning

Testing Strategy

Acceptance criteria included

Deployment Checklist

Step-by-step go-live guide

Real Results

Before and after Agent Accelerator

Metric	Before	With Agent Accelerator
Design to deployment	8–12 weeks	1–2 days
Compliance validation	Manual audit (2–4 weeks)	Automated (73 checks, instant)
Platform rebuild effort	Full rewrite per platform	Regenerate from DSL (minutes)
Engineering handoff	30-page requirements doc	Auto-generated specs with roadmaps
Governance documentation	Often skipped entirely	Generated automatically
Design decisions preserved	Lost in meeting notes	Tracked in ADRs

Ready to design your first agent?

Build Studio transforms weeks of requirements gathering, architecture meetings, and compliance reviews into a single guided design session. Start with a Proof of Concept and see the difference.

Start a Proof of Concept Explore MCP Gateway

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