Multi-Agent Workflow Patterns for B2B Operations: A Vendor-Neutral Comparison Across Azure, AWS, and Google

Why Mid-Market B2B Operations Need Coordinated Multi-Agent Systems Mid-market B2B operations teams often juggle fragmented automation—one bot handles order entry, another manages customer support, yet a third verifies inventory. Without coordination, these silos create handoff errors, duplicate work, and compliance blind spots. A multi-agent system that orchestrates specialized agents under a unified workflow addresses these gaps, but building it from scratch requires custom infrastructure not every team can justify. As of May 22, 2026, Microsoft’s Azure AI Foundry has reached general availability for multi-agent collaboration, offering a managed environment where enterprise teams can build coordinated agent systems without investing in custom orchestration layers. Similarly, AWS Bedrock AgentCore and Google Vertex AI Agent Builder now provide mature frameworks for multi-agent workflows.

The challenge for B2B leaders is choosing the right platform and pattern that balances latency, cost, and governance for their operational realities. The Three-Agent Pattern: Triage, Verification, Orchestration Drawing from engineering lessons inside Microsoft’s own deployments, a repeatable pattern has emerged for B2B operations: a triage agent , a verification agent , and an orchestration agent . This pattern is vendor-neutral and can be implemented on any cloud provider’s agent framework. Triage Agent : Acts as the first point of contact for incoming customer inquiries (e.g., order status, return requests, billing questions). It classifies the request, extracts key entities (order ID, customer name, issue type), and either resolves common issues directly or passes structured data to the orchestration agent. Verification Agent : Responsible for cross-referencing the triaged request ag

ainst enterprise systems—checking order accuracy, inventory availability, or customer eligibility. It returns a confidence score or a set of validated facts. Orchestration Agent : Manages the handoff between triage and verification, handles retries, enforces business rules (e.g., escalation to a human if confidence < 80%), and logs the full workflow for audit. It does not execute domain logic itself but coordinates the other agents. This pattern ensures that each agent has a narrow, observable function, making it easier to monitor, test, and govern. Microsoft’s internal teams found that separating verification logic from triage reduced false positives by 40% in their order support use case ( ). Building the Pattern on Azure AI Foundry (GA as of May 2026) Azure AI Foundry Agent Service, now generally available as of May 2026, provides a managed orchestration layer called connected agents

and an agent catalog for reusing pre-built skills. To implement the three-agent pattern: 1. Create each agent via the Foundry portal or SDK. Each agent is configured with a specific system prompt and connected to an enterprise data source (e.g., Cosmos DB for orders, Dynamics 365 for CRM). 2. Define handoff triggers using the Agent Service’s built-in orchestrator. The triage agent emits a structured JSON payload; the orchestrator agent reads it and routes to the verification agent. The orchestration agent is essentially a meta-agent that manages the state machine. 3. Add human-in-the-loop triggers via the Service’s “escalation” destination. For example, if the verification agent returns a low confidence score, the orchestration agent pauses and notifies a support agent through Teams integration. Microsoft’s Agent Framework announcement (see ) emphasizes that this design supports observab

le workflows and centralized policy enforcement, a key requirement for B2B operations teams with compliance mandates. Alternative Implementation: AWS Bedrock AgentCore AWS Bedrock AgentCore, part of the broader Bedrock service, offers a similar capability but with different defaults. To replicate the triage-verification-orchestration pattern: Agent definitions are managed via the Bedrock console or API, each with an associated foundation model (e.g., Claude 3.5 Sonnet). AWS uses action groups and knowledge bases to link agents to data sources. Orchestration in Bedrock AgentCore is handled by a “super-agent” pattern where one agent calls another via API calls. However, as of mid-2026, Bedrock does not provide a native state machine for complex multi-step handoffs—teams typically implement orchestration logic in a Lambda function or Step Functions workflow. Governance is enforced via IAM r

oles per agent. AWS CloudTrail captures all invocation logs, but the orchestration layer must be instrumented manually for end-to-end traceability. Trade-off: Bedrock gives you more control over the model and data handling (important for teams with strict data residency requirements), but it require