Most agent demos look impressive for about ten minutes. Then you start asking "what happens when this tool call times out?" or "how does the orchestrator know the sub-agent failed?" and the whole thing quietly falls apart. The Google for Startups AI Agents Challenge surfaced something worth paying attention to: the strongest submissions stood out for foundational software engineering, and model choice or prompt cleverness had little to do with it.

I've been building multi-agent systems on my own time. AgentReview does PR review with C#/.NET and MCP, and I've been using The 10X Engineer Toolkit as a Claude Code plugin for agent-assisted development, so this hit close to home. Below are the four patterns the report highlights and what each one takes to implement.

Pattern 1: Bidirectional MCP Is Not Optional at Scale

The winning submissions consistently used bidirectional MCP (Model Context Protocol) for inter-agent communication rather than one-way tool calls. This is a bigger deal than it sounds. A unidirectional setup means your orchestrator fires a request at a sub-agent and waits. Bidirectional means the sub-agent can push state back, signal partial progress, or surface a blocking condition without being polled.

When I built AgentReview, I ran into exactly this problem. The reviewer agents needed to signal when they were waiting on context from another agent before they could finish their analysis. With a purely request-response pattern, the orchestrator either sat idle or timed out. Bidirectional communication turns that into a proper event-driven handshake: the sub-agent says "I need X before I can give you Y," and the orchestrator can route accordingly instead of guessing.

If you're building on MCP today, design your server interfaces to support streaming and push notifications from day one. Retrofitting bidirectional communication into a unidirectional architecture is painful.

Pattern 2: Async Task Queues Are the Architecture

The challenge report called out async execution as a core pattern, not a performance tweak. Agent tasks have wildly variable latency. A sub-agent doing a web search might return in two seconds. A sub-agent calling an external API might take twenty. If your orchestrator is synchronous, the slow agent becomes a ceiling for every other agent in the system.

The pattern is treating each agent invocation like a job on a queue: fire it, get a task ID, check back or subscribe to completion. This is exactly how you'd architect a background job system in a .NET application with something like Hangfire or Azure Service Bus. The concept isn't new; what's new is that agent frameworks are finally being built to expect it rather than paper over it.

The submissions that held up under load went past try/catch to dead-letter queues and retry policies. If an agent call fails, you want it to re-enter the queue with backoff instead of silently disappearing. That's the difference between a system that's resilient and one that works until it doesn't.

Pattern 3: Structured Outputs Are Your Contract, So Treat Them Like One

One pattern that showed up repeatedly in the winning architectures: agents communicating through typed, validated structured outputs rather than freeform text. This sounds obvious, but a lot of agent pipelines have the orchestrator parsing natural language from a sub-agent response and hoping the format holds. That's a support ticket waiting to happen.

Structured outputs give you a contract between agents, the same way an API contract gives you a stable interface between services. If your reviewer agent is supposed to return a list of findings with severity levels and line numbers, define that as a schema and validate it on receipt. When it breaks, you'll know immediately and exactly where.

In C#/.NET this maps cleanly to strongly typed response objects deserialized from JSON. In Python you'd reach for Pydantic. Either way, untyped agent-to-agent communication is wishful thinking dressed up as an architecture. The best submissions treated inter-agent message schemas with the same discipline they'd apply to a REST API.

Pattern 4: Observability Has to Be First-Class

The last pattern is the one most teams skip until something breaks in production: full observability across the agent graph. Every agent invocation, every tool call, every hand-off between agents needs to be traceable, with correlation IDs that let you reconstruct exactly what happened in what order. Logging alone doesn't get you there.

This is something I've been thinking about a lot with AgentReview. When three agents review the same pull request and the synthesis agent produces a weird output, you need to walk backwards through the graph and find where the bad reasoning entered. Without structured tracing, you're guessing. With it, you're debugging.

Instrument your agent calls the same way you'd instrument microservice calls: OpenTelemetry spans, structured logs with trace context, and a way to visualize the execution graph. Azure Monitor handles this well if you're already in that ecosystem. The cost of setting this up early is low. The cost of retrofitting it after something fails in production is very high.

What the Results Add Up To

Multi-agent systems are a distributed systems problem wearing an AI hat. The teams that won treated them that way. They reached for message queues, typed interfaces, bidirectional communication, and observability tooling, and left the smarter-model arms race to everyone else. None of it is glamorous. All of it holds up.

If you're building agents right now and you haven't thought through failure modes, retry policies, and how you'll trace a bad output back to its source, start there before you add another agent to your graph. The architecture decisions you make early are the ones you'll be living with when things get weird at 2am.