Microsoft Multi-Agent AI Solutions

Each agent needs a clear role, a limit on what it may decide alone, and the narrowest permissions that let it work. You set these deliberately, because an over-trusted agent is the quickest way a multi-agent system causes harm.

• Writing an agent persona that holds its role
• Levels of autonomy and who decides
• Least-privilege access to tools and data
• Separating planners from executors
• Documenting each agent’s contract

You build A set of agent personas, each with its autonomy level and permission scope.

Agents that share a task must share what they know without sharing everything. You design short-term, long-term and shared memory, decide what is stored, summarized or forgotten, and keep each agent’s context within its limits.

• Short-term, long-term and shared memory stores
• What to store, summarize or forget
• Scoping memory per agent and per user
• The context window as a budget
• Testing that memory helps and does not leak

You build A memory design for an agent team, with scopes and retention rules.

Agents run on identity, compute, state and telemetry that someone has to specify. You choose these components on Azure and Microsoft Foundry before building, so that security, scaling and observability are designed in and not added later.

• Identity for agents, users and tools
• Choosing compute for long-running agent work
• Where state, queues and caches live
• Observability components from the start
• Zero Trust applied to an agent platform
• Reading a reference architecture critically

You build An architecture diagram of identity, memory, compute and observability for one solution.

A prompt is the interface between your design and the model’s behavior. You write instructions per role, shape the context each agent receives at each step, and test prompts against awkward inputs so behavior stays steady as the system grows.

• System prompts for specialist roles
• Examples and output contracts
• Assembling context per step, not per session
• Structured output passed between agents
• Prompt injection hidden in retrieved text
• Versioning prompts like code

You build A versioned prompt set for three agent roles, with tests for awkward inputs.

Agents act on what they can reach. You ground them with retrieval-augmented generation, then expose business systems through MCP servers with clear tool descriptions, typed inputs and sensible behavior when a call fails.

• Retrieval pipelines for agent grounding
• Chunking, embeddings and hybrid search
• Writing an MCP server for an internal system
• Tool descriptions a model can choose between
• Typed inputs, errors and idempotent actions
• Controlling which agents see which tools

You build An MCP server and a retrieval index that two agents use on the same task.

Hub-and-spoke, sequential, parallel and peer-to-peer patterns each fit different work. You implement them, learn what each costs in latency and debuggability, and place approval steps where a person must stay in charge.

• Hub-and-spoke with a coordinating agent
• Sequential pipelines and their handoffs
• Parallel fan-out and merging results
• Peer-to-peer collaboration and its risks
• Approval steps and resumable runs
• Choosing a pattern from the shape of the task

You build The same task orchestrated in two patterns, with an approval step and a written comparison.

Agents from different teams or vendors need a shared way to talk. You work with A2A, add caching to cut repeated work, and use middleware for the cross-cutting concerns every call should pass through.

• How agents discover and call each other with A2A
• Message contracts and versioning
• Caching model calls and tool results
• Middleware for logging, limits and policy
• Timeouts, retries and backoff between agents
• Avoiding loops and runaway delegation

You build Two agents on different frameworks that cooperate over A2A, with caching and middleware.

Agent output varies, so testing needs more than assertions. You build test sets for memory, tools and prompts, use LLM-as-a-judge with rubrics checked against human review, and turn failures into regression tests.

• Test sets for memory, tools and prompts
• Unit, trajectory and end-to-end tests
• LLM-as-a-judge: rubrics and bias
• Calibrating a judge against human review
• Turning production failures into tests
• Running evaluations inside the pipeline

You build An evaluation suite with a calibrated judge that runs on every change.

A live agent team is hard to see. You trace every run through Foundry, watch for drift in quality, and track token use and cost per agent, so that slow or expensive behavior is found and fixed before it reaches users.

• Tracing a run across agents and tools
• Reading a trace to find the failing step
• Detecting drift in quality and behavior
• Token budgets per agent and per task
• Cost attribution and alerts
• Tuning latency without losing quality

You build A dashboard and alert set covering quality, latency, tokens and cost for a live team.

Every agent acts as someone, so identity comes first. You give agents and tools their own identities, keep credentials in Key Vault, and apply Zero Trust so that no single compromised agent can reach everything.

• Managed identities for agents and tools
• Secrets and keys in Azure Key Vault
• Verify explicitly: authorizing every tool call
• On-behalf-of access for user-scoped actions
• Network isolation for agent workloads
• Auditing who and what acted

You build An identity and secrets layout for an agent team that shares no credentials.

Guardrails belong at every point an agent touches. You filter inputs, check outputs, and validate tool calls before they run, then test each layer against injection and misuse attempts.

• Input filtering and prompt injection defenses
• Output checks for safety and policy
• Validating tool calls before execution
• Limits on spend, scope and rate
• Red-teaming an agent team
• Handling a blocked action gracefully

You build A layered guardrail set, with a red-team script that tries to get past it.

Agent changes can alter behavior in ways tests miss. You release through pipelines with blue/green and canary strategies, compare old and new behavior on live traffic, and keep a rollback ready.

• Pipelines for prompts, tools and agent code
• Blue/green releases for agent services
• Canary releases and traffic splitting
• Comparing versions on live traffic
• Rollback and feature flags
• Managing model version changes

You build A release pipeline that canaries an agent change and rolls back when quality drops.

Design, build, evaluate, secure and release one multi-agent solution for a real problem. You defend the decomposition, the orchestration pattern, the guardrails and the cost model as you would to a review board.

• Scoping the problem and its constraints
• Designing the agent team and its platform
• Building and orchestrating the system
• Evaluating, tracing and securing it
• Releasing it and defending the design

You build A working multi-agent system with its design record, evaluation results and release plan.