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Advanced hands-on lab demonstrating how to build production-grade multi-agent systems using Microsoft Agent Framework, Agent-to-Agent (A2A) protocol, and Software Engineering (SWE) agents. Starting with Magentic-One architecture patterns and progressively adding MCP tools and GitHub Copilot coding agents for secure enterprise workflows.

This 300-level lab provides advanced developers with practical experience building interoperable agent systems that can collaborate across platforms, execute complex engineering tasks autonomously, and integrate with external tools securely.

Session context

LAB513 - Build A2A and MCP Systems using SWE Agents and agent-framework

Speakers:

Govind Kamtamneni (Microsoft)
Mark Wallace (Microsoft)

When: November 18, 2025, 1:00 PM - 2:15 PM PST Where: Moscone West, Level 3, Room 3007 Level: Advanced (300) Format: Hands-on lab (in-person only, embargoed content)

Session description:

"Learn to leverage agent-framework, the new unified platform from Semantic Kernel and AutoGen engineering teams, to build A2A compatible agents similar to magnetic-one. Use SWE Agents (GitHub Copilot coding agent and Codex with Azure OpenAI models) to accelerate development. Implement MCP tools for secure enterprise agentic workflows."

Key learning objectives:

Build A2A (Agent-to-Agent) compatible agents using Magentic-One patterns
Implement MCP (Model Context Protocol) tools for secure workflows
Deploy SWE agents for autonomous code development
Orchestrate multi-agent systems in production
Create interoperable agents across platforms and clouds

Lab resources:

Core technologies explained

Magentic-One: Multi-agent orchestration pattern

What it is:

Magentic-One is Microsoft's generalist multi-agent system designed for solving open-ended web and file-based tasks across domains.

Architecture:

Orchestrator agent (lead):

Directs four specialized agents
Plans task execution
Tracks progress
Recovers from errors

Specialized agents:

WebSurfer: Web navigation and interaction
FileSurfer: File system operations and document processing
Coder: Code generation and execution
ComputerTerminal: Command-line operations

Built on AutoGen:

Magentic-One leverages AutoGen's multi-agent framework, providing production-tested patterns for agent coordination.

Why this matters:

Instead of building one general agent attempting everything, Magentic-One demonstrates specialized agents coordinated by orchestrator. This pattern scales better and handles complex tasks more reliably.

Agent-to-Agent (A2A) protocol

What it solves:

Agents from different platforms, vendors, and organizations need standardized communication protocol.

A2A capabilities:

Structured communication:

Exchange goals between agents
Manage shared state
Invoke actions across agent boundaries
Return results securely

Interoperability:

Agents built with Semantic Kernel can communicate with LangChain agents
Cross-cloud agent collaboration
Cross-organizational agent coordination

Observability:

Track agent-to-agent interactions
Monitor multi-agent workflows
Debug distributed agent systems

Platform support:

Coming to Azure AI Foundry and Copilot Studio, enabling enterprise adoption of A2A patterns.

Technical insight:

Without A2A: Each agent pair requires custom integration code, creating N² integration complexity.

With A2A: Standardized protocol enables any A2A-compatible agent to communicate with any other, reducing to N integrations.

Model Context Protocol (MCP)

What it provides:

Standardized interface for connecting agents to external tools, data sources, and services.

MCP architecture:

MCP Server:

Exposes tools and data sources
Handles authentication and authorization
Manages request/response cycles

MCP Client (agent):

Discovers available tools
Invokes tools with parameters
Receives and processes results

Enterprise advantages:

Security:

Centralized authentication
Tool access controls
Audit logging of tool usage

Reusability:

One MCP server serves multiple agents
Standard tools across agent fleet
Vendor-agnostic integration

Example MCP tools:

Microsoft Learn documentation access
Internal knowledge bases
External APIs and services
Data retrieval systems

SWE Agents (Software Engineering Agents)

What they are:

AI-driven systems that assist or act autonomously on behalf of software engineers.

GitHub Copilot Coding Agent:

Capabilities:

Runs inside GitHub Actions
Picks up assigned issues
Explores repository for context
Writes code autonomously
Passes tests
Opens pull requests for review

Agent Mode:

Self-healing code:

Iterates on its own code
Recognizes errors automatically
Fixes errors without human intervention
Analyzes runtime errors

Availability:

Preview for Copilot Enterprise and Copilot Pro+ users
Integrated in VS Code, Xcode, Eclipse, JetBrains, Visual Studio

Azure OpenAI Codex:

What it enables:

Code generation from natural language
Code explanation and documentation
Code translation between languages
Code optimization suggestions

Agentic DevOps pattern:

SWE agents represent evolution of DevOps where intelligent agents collaborate with developers and with each other on code development, testing, and deployment.

Lab architecture

What you build

Multi-agent system with:

Magentic-One orchestration pattern - Coordinator agent directing specialized agents
A2A communication - Agents communicating via standardized protocol
MCP tool integration - Agents accessing external tools securely
SWE agent deployment - Autonomous code development agent
Production workflow - End-to-end agent system deployable to enterprise

Progressive lab phases

Phase 1: Magentic-One foundation

Build orchestrator agent:

Task planning logic
Agent coordination
Progress tracking
Error recovery

Build specialized agents:

WebSurfer for web interaction
FileSurfer for document processing
Coder for code generation
ComputerTerminal for system operations

Skills learned:

Multi-agent architecture patterns
Orchestrator design patterns
Specialized agent creation
Inter-agent coordination

Phase 2: A2A protocol implementation

Enable agent-to-agent communication:

Define agent capabilities
Implement A2A message protocol
Handle cross-agent requests
Manage distributed state

Skills learned:

A2A protocol specification
Interoperable agent design
Cross-platform agent communication
State management in distributed systems

Technical challenge:

How do agents discover each other's capabilities? How do they negotiate task execution? A2A protocol handles capability discovery and task delegation systematically.

Phase 3: MCP tool integration

Connect agents to external tools:

Configure MCP server
Register tool definitions
Implement tool invocation
Handle tool responses

Example tools:

Microsoft Learn MCP server for documentation
Custom enterprise knowledge bases
External APIs via MCP
Data retrieval services

Skills learned:

MCP server configuration
Tool schema definition
Secure tool access patterns
Tool response handling

Phase 4: SWE agent deployment

Deploy GitHub Copilot coding agent:

Configure agent for repository
Define code generation tasks
Set up testing requirements
Configure PR creation workflow

Autonomous code development:

Agent receives issue assignment
Explores codebase for context
Generates solution code
Runs tests to validate
Creates pull request

Skills learned:

SWE agent configuration
Repository context management
Autonomous testing patterns
PR automation workflows

Phase 5: Production orchestration

End-to-end workflow:

User submits complex task
Orchestrator analyzes requirements
Delegates to specialized agents via A2A
Agents use MCP tools for external data
SWE agent generates code if needed
Orchestrator assembles results
Returns comprehensive solution

Production considerations:

Error handling across agent boundaries
Monitoring multi-agent workflows
Cost tracking for LLM calls
Security and compliance controls

Advanced patterns demonstrated

Cross-platform agent collaboration

Scenario:

Agent A (built with Semantic Kernel) needs data from Agent B (built with LangChain) to complete task.

Without A2A:

Custom integration code required
Tight coupling between agents
Fragile when either agent updates

With A2A:

Agent A sends A2A request to Agent B
Agent B processes via standard protocol
Agent A receives response in standard format
No custom integration code needed

Enterprise implication:

Organizations can adopt best-of-breed agents from multiple vendors without integration nightmares. A2A enables heterogeneous agent ecosystems.

Secure tool access via MCP

Security challenge:

Agents need access to enterprise tools and data, but:

Different agents require different permissions
Tool access must be audited
Credentials cannot be embedded in agent code

MCP solution:

Centralized authentication:

MCP server handles authentication
Agents present credentials to MCP server
MCP server validates and proxies tool access

Fine-grained authorization:

Define which agents can access which tools
Role-based access control
Tool usage policies enforced at MCP layer

Audit trail:

All tool invocations logged
Agent identity tracked
Tool usage patterns monitored

Pattern:

Agents never call tools directly. All tool access mediated through MCP server enforcing security policies.

Autonomous code development workflow

Traditional development:

Developer reads issue
Developer explores codebase
Developer writes code
Developer tests code
Developer creates PR
Code review happens

SWE agent workflow:

SWE agent assigned issue
Agent explores codebase autonomously
Agent writes code
Agent runs tests automatically
Agent creates PR
Human reviews PR (critical step)

What changes:

Agent handles mechanical work (code exploration, generation, testing). Human reviews for correctness, architecture alignment, and business logic validation.

Production reality:

SWE agents accelerate development but don't replace code review. Human-in-the-loop remains essential for quality assurance.

What this lab reveals

Pre-release capabilities

Lab contains embargoed content showing Microsoft Agent Framework features not yet publicly documented.

What attendees see first:

A2A protocol implementation details
MCP integration patterns for enterprise
Magentic-One orchestration in production
SWE agent deployment patterns

Strategic insight:

Microsoft betting heavily on multi-agent systems as fundamental enterprise pattern. Investment in standardized protocols (A2A, MCP) signals long-term commitment beyond single-vendor lock-in.

Production-ready patterns

Unlike simpler labs, LAB513 demonstrates complete production workflow:

Not just agent creation - Full orchestration including error recovery, monitoring, and security

Not just demos - Deployable systems with RBAC, audit logging, and compliance controls

Not just theory - Actual code running in GitHub Actions, processing real repository issues

Enterprise validation:

Patterns demonstrated here work at scale. Magentic-One architecture proven in Microsoft Research. A2A protocol designed for cross-organizational agent collaboration. MCP security model enterprise-grade.

Critical assessment

What works exceptionally well

Magentic-One orchestration pattern:

Specialized agents coordinated by orchestrator scales better than monolithic general agent. Clear separation of concerns enables:

Independent agent development
Focused optimization per agent type
Graceful degradation when agent fails

Official Microsoft Learn definition: Magentic orchestration is "designed for open-ended and complex problems that don't have a predetermined plan." Manager agent dynamically:

Maintains shared context across specialist agents
Tracks workflow progress
Adapts workflow in real-time based on task evolution
Iteratively refines solutions through agent collaboration

Production deployment: Per Microsoft, these patterns (originally AutoGen research prototypes) now operate with "production-grade durability and enterprise controls" in Agent Framework. Deploy to Azure AI Foundry with built-in observability, approvals, security, and long-running durability.

A2A protocol standardization:

Solves real interoperability problem. Enterprises building agents don't want vendor lock-in. A2A enables heterogeneous agent ecosystems where best tools win, not single vendor.

Official status: Microsoft announced May 7, 2025 adoption of Google's Agent2Agent (A2A) protocol. Coming to Azure AI Foundry and Copilot Studio. Semantic Kernel Python samples available demonstrating cross-agent collaboration.

Key A2A capabilities (per spec at a2aprotocol.ai):

Agent Discovery: Machine-readable "Agent Card" (JSON) advertising capabilities, endpoints, auth requirements
Task Management: Structured interactions around discrete tasks with well-defined lifecycles
Message Exchange: Standardized messaging for context, replies, artifacts
Content Negotiation: Format and UI capability negotiation between agents

MCP security model:

Centralized tool access control addresses legitimate enterprise concern: how do we govern what agents can do? MCP provides answer: policy enforcement at tool gateway.

SWE agents for code velocity:

GitHub Copilot coding agent demonstrably accelerates development for mechanical tasks. Issue → PR workflow automation is real productivity gain for well-scoped tasks.

What remains challenging

Orchestration complexity at scale:

Lab demonstrates 4-5 agents. Production systems might coordinate dozens or hundreds. How does orchestrator scale? What happens when agents conflict? How do you debug distributed agent failures?

A2A adoption timeline:

Protocol coming "soon" to Azure AI Foundry and Copilot Studio. Until broadly available, enterprises can't build production systems depending on A2A. Chicken-and-egg adoption problem.

MCP tool proliferation:

Who builds MCP servers for enterprise tools? Microsoft provides some, but enterprises have hundreds of internal tools. Building and maintaining MCP servers becomes operational burden.

SWE agent scope limitations:

Coding agent works for well-defined issues. Complex architectural changes requiring judgment across codebase still need human developers. Risk: Organizations overestimate agent capabilities and assign inappropriate tasks.

Cost modeling uncertainty:

Multi-agent systems make many LLM calls. Orchestrator planning, specialized agent execution, A2A communication, MCP tool usage - costs accumulate quickly. Lab doesn't address cost optimization strategies.

Microsoft documentation addresses this:

Agent Framework includes monitoring dashboards with:

Token consumption over time visualization
Cost estimation and daily spending
Per-agent breakdown to identify resource-heavy agents
Optimized context management to reduce AI costs

Production best practices:

Implement token usage monitoring from day one
Optimize context windows (only include relevant agents)
Use simplest orchestration pattern for task
Track cost per successful interaction, not just total cost

Production considerations not covered

Multi-tenant agent systems

Challenge:

How do you deploy agent orchestration serving multiple customers with:

Data isolation between tenants
Resource limits per tenant
Cost allocation per tenant
Security boundaries enforced

Lab scope:

Single-tenant development scenario.

Production gap:

Enterprise SaaS providers need multi-tenant agent systems. Architectural patterns unclear.

Agent versioning and rollback

Challenge:

Agent behavior changes when:

Underlying model updates
Tool definitions change
Orchestration logic modified
A2A protocol evolves

How do you version agents? How do you rollback when new version misbehaves?

Lab scope:

Single version, no rollback demonstrated.

Production gap:

Enterprises need agent lifecycle management comparable to software release management.

Performance SLAs

Challenge:

Multi-agent orchestration involves:

Orchestrator planning time
Agent execution time
A2A communication overhead
MCP tool latency
LLM inference time

How do you guarantee response time SLAs when so many variables?

Lab scope:

No performance testing or SLA discussion.

Production gap:

User-facing agent systems require predictable latency. Multi-agent orchestration makes this harder to guarantee.

Microsoft Learn guidance:

Per AI Agent Design Patterns, production deployments should:

Implement observability and performance monitoring
Design for proper error handling
Manage context windows carefully to reduce token usage
Use simplest orchestration pattern that solves the problem (avoid unnecessary complexity)

Compliance and auditability

Challenge:

Regulated industries require:

Complete audit trail of agent decisions
Explainability of agent reasoning
Compliance with data residency rules
Human oversight of critical operations

Lab scope:

MCP provides audit logging, but comprehensive compliance story incomplete.

Production gap:

Financial services, healthcare, government need compliance validation before agent deployment.

Who should take this lab

Advanced developers building production agent systems:

If you're architecting multi-agent solutions for enterprise, this lab provides concrete patterns you can implement immediately.

Platform engineers evaluating agent orchestration:

Hands-on experience with Magentic-One, A2A, and MCP informs build-vs-buy decisions for agent platforms.

SRE teams planning agent operations:

Understanding agent-to-agent communication, tool access patterns, and orchestration failures helps plan operational support.

Not ideal for:

Beginners - 300-level assumes familiarity with agent concepts, LLM fundamentals, and distributed systems
Non-technical roles - Hands-on coding required throughout
Simple agent use cases - If you need single agent with basic tools, simpler labs more appropriate

What to explore after the lab

Scale orchestration complexity

Add more specialized agents:

Database query agent
API integration agent
Data visualization agent
Notification agent

Test orchestrator at scale:

How many concurrent agent tasks?
What happens when agents conflict?
How does error recovery work with 10+ agents?

Build custom MCP servers

Enterprise tool integration:

Internal knowledge base MCP server
CRM system MCP server
Custom API gateway MCP server

Security hardening:

Implement fine-grained RBAC
Add audit logging
Enforce rate limiting

Deploy SWE agent to real repository

Production code development:

Assign real issues to coding agent
Measure PR quality and acceptance rate
Optimize for specific coding patterns

Measurement:

Track velocity improvement
Monitor test pass rates
Measure code review time

Implement cross-platform A2A

Heterogeneous agent system:

Semantic Kernel agent
LangChain agent
Custom agent framework
All communicating via A2A

Validate interoperability:

Can they actually coordinate?
What breaks at integration points?
Where does protocol need extension?

The strategic implication

Microsoft's multi-agent bet

This lab reveals Microsoft's strategic direction: Enterprise future runs on orchestrated multi-agent systems, not monolithic AI assistants.

Evidence:

Agent Framework unification - Merging Semantic Kernel and AutoGen signals long-term platform commitment

A2A protocol standardization - Open protocol prevents vendor lock-in, encouraging ecosystem adoption

MCP integration - Solving enterprise tool access problem systematically, not one-off integrations

Magentic-One patterns - Research-to-production pipeline demonstrating Microsoft validating multi-agent architectures internally

SWE agent investment - GitHub Copilot evolution toward autonomous agents shows Microsoft betting on agentic DevOps

What enterprises should watch

A2A adoption velocity - If Azure AI Foundry and Copilot Studio ship A2A support quickly, validates strategic importance

Magentic-One expansion - Watch for additional specialized agent types and orchestration patterns

MCP server ecosystem - Third-party MCP servers for enterprise tools indicate market validation

SWE agent capabilities - GitHub Copilot coding agent evolution shows maturity of autonomous development

Multi-agent pricing models - How Microsoft prices orchestrated agent systems reveals economic viability

Learn more

Lab repository:

LAB513 GitHub Repository - Lab instructions and code samples
Spec-to-Agents Reference - Detailed implementation patterns

Official resources:

Technologies demonstrated:

Microsoft Agent Framework (.NET and Python)
Magentic-One orchestration pattern
Agent-to-Agent (A2A) protocol
Model Context Protocol (MCP)
GitHub Copilot SWE agents
Azure OpenAI Codex

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