Choose agent platforms based on architecture needs, not vendor hype
Enterprise teams evaluating agent platforms should start from their architectural requirements, not the latest vendor announcements. The right platform depends on multi-agent patterns, observability needs, security constraints, and integration complexity—not which cloud provider has the loudest marketing.
Evaluation criteria for enterprise agent platforms
| Criterion | Why it matters | What to look for |
|---|---|---|
| Multi-agent support | Determines whether you can orchestrate specialist agents or must build monolithic assistants | Router, supervisor, fan-out, and collaborative patterns; support for agent handoffs and shared context |
| Tool integration | Agents are only as useful as the tools they can safely access | Gateway patterns, MCP support, custom tool APIs, policy-based authorization, and rate limiting |
| Memory and state | Complex workflows require conversation memory, session management, and persistent state | Session stores, vector databases, memory primitives, and state checkpointing |
| Observability | Production agents must be debuggable, auditable, and monitorable | Execution traces, intermediate output logging, token tracking, and integration with existing monitoring |
| Security controls | Agents that access enterprise systems need strong guardrails | Tool authorization, input/output filtering, redaction, and approval workflows |
| Custom model support | You may need to use proprietary, fine-tuned, or domain-specific models | Model marketplace, custom model endpoints, and flexible inference backends |
| Deployment flexibility | Enterprise constraints may require specific hosting, regions, or compliance models | Managed runtimes, Kubernetes hosting, serverless options, and hybrid deployment support |
| Cost model | Token costs add up quickly in multi-step, multi-agent workflows | Transparent pricing, caching strategies, and model routing options |
| Ecosystem maturity | Documentation, examples, and community support accelerate development | Reference architectures, sample apps, integration guides, and active community |
Avoid lock-in through abstraction layers where practical
Design your agent logic with abstraction layers between platform-specific APIs and your core business logic. This makes it easier to migrate between clouds or frameworks as requirements evolve, though it adds upfront engineering complexity. The tradeoff is worth it when you expect multi-cloud deployment or anticipate changing platforms.
Each major cloud has distinct agent platform approaches
AWS, GCP, and Azure all offer agent platforms, but they take different architectural approaches. Understanding these differences helps you choose the platform that matches your multi-agent patterns, operational model, and enterprise integration needs.
Cloud provider agent platform comparison
| Platform | Agent orchestration | Memory/state | Tool integration | Monitoring |
|---|---|---|---|---|
| AWS | Bedrock Agents, AgentCore runtime, Step Functions for workflows, Lambda for serverless steps | Session stores in AgentCore, DynamoDB for persistence, Knowledge Bases for vector retrieval | AgentCore Gateway with policy, Lambda functions, APIs, custom MCP servers | AgentCore Observability for traces, CloudWatch for metrics, X-Ray for debugging |
| GCP | Vertex AI Agent Builder, Agent Engine for orchestration, Cloud Functions for steps, Workflows for pipelines | Agent Engine state management, Firestore for persistence, Vector Search for retrieval | Function calls, Vertex AI extensions, Cloud Functions, event-driven triggers | Vertex AI monitoring, Cloud Logging, Cloud Trace, integrated with Monitoring |
| Azure | AI Agents Service, Semantic Kernel for orchestration, Azure Functions for steps, Durable Functions for workflows | Semantic Kernel memory, Cosmos DB for persistence, Cognitive Search vector retrieval | Semantic Kernel plugins, Azure Functions, API Management, custom connectors | Application Insights, Azure Monitor, Log Analytics, OpenTelemetry support |
The practical difference shows up in how each platform handles multi-agent patterns. AWS leans heavily on Step Functions for stateful orchestration and implementing the Saga Pattern for distributed agentic transactions. Azure centers on Azure AI Agent Service with native Entra ID integration for identity-aware security across the Microsoft 365 ecosystem. GCP leverages Vertex AI Extensions and Eventarc for creating reactive, event-driven agent swarms. None of these is inherently better—the question is which fits your team's skills and your architectural patterns.
Google Cloud: Agentic AI Design Patterns
Explores single-agent and multi-agent system patterns for enterprise workflows on GCP, including Router, Parallel, and Sequential architectures.
Read the GCP design patterns guideAWS: Agentic AI Patterns
AWS prescriptive guidance detailing patterns and architectures for building scalable agentic systems using Amazon Bedrock and AgentCore.
Read the AWS prescriptive guidanceAzure: AI Agent Design Patterns
Microsoft Azure architectural guide defining sequential, concurrent, and magentic (group chat) multi-agent design patterns.
Read the Azure architecture guideEnterprise Agentic Architecture and Design Patterns
Salesforce Architect guide presenting a pattern-based methodology. The "Open Ecosystem" design principle and the Multi-Vendor A2A orchestration archetypes illustrate how the Salesforce approach to interoperability compares with other cloud agent platforms.
Read the Salesforce design patterns guideVendor pattern guides reveal different design centers
The fastest way to misunderstand cloud agent platforms is to compare product names instead of pattern vocabularies. AWS and Azure publish different taxonomies because they optimize for different design questions. AWS starts by classifying the agent itself and the execution boundary around it. Azure starts by classifying the orchestration relationship between multiple agents.
How the major vendor guides frame the design problem
| Vendor guide | Primary question | Core pattern lens | What that means in practice |
|---|---|---|---|
| AWS Prescriptive Guidance | What kind of agent and execution boundary are you building? | Reasoning agents, function-calling, tool servers, workflow orchestration, memory, collaboration | Good fit when you need to decide how much runtime isolation, delegation, and stateful control the agent needs |
| Azure Architecture Center | How should specialized agents coordinate to solve the task? | Sequential, concurrent, group chat, handoff, magentic | Good fit when orchestration style, security trimming, human checkpoints, and coordination cost dominate the architecture choice |
| Google Cloud Architecture | Which design pattern matches the task shape and operating model? | ReAct, coordinator/router, sequential, parallel, swarm | Useful for choosing an initial workflow shape and then refining runtime details separately |
This difference matters during platform evaluation. A team building isolated tool runtimes and policy-heavy execution boundaries will usually learn more from the AWS pattern set. A team choosing between pipeline, handoff, and group-discussion behavior will usually learn more from the Azure guide. Mature enterprise architectures end up using both lenses: first choose the coordination pattern, then choose the execution and governance boundary for each agent and tool surface.
The implementation guidance also differs. Azure now points developers toward Microsoft Agent Framework for pattern-level workflow orchestration and positions Foundry Agent Service as a managed option for simpler connected-agent cases. AWS instead maps each capability to cloud primitives such as Bedrock, Lambda, EventBridge, Step Functions, DynamoDB, and SQS. That means AWS guidance is often more explicit about the surrounding runtime topology, while Azure guidance is stronger on orchestration tradeoffs, security trimming, and HITL checkpoints.
Open-source options give teams full control at the cost of operational overhead
Open-source agent frameworks make sense for enterprises that need full control over hosting, want to avoid vendor lock-in, or have specialized requirements that managed platforms cannot meet. The tradeoff is significant platform engineering overhead—you are responsible for hosting, scaling, monitoring, and security.
Major open-source agent frameworks
| Framework | Approach | Multi-agent support | Tool ecosystem | Production readiness |
|---|---|---|---|---|
| LangChain / LangGraph | Component library and graph-based orchestration framework | LangGraph supports complex multi-agent graphs with stateful routing | Extensive tool ecosystem, hundreds of integrations, strong community | Widely adopted but rapidly evolving—plan for version churn |
| CrewAI | Role-based multi-agent framework with collaborative crews | Built for multi-agent from the ground up with role specialization | Growing integration ecosystem, focused on practical tool use | Younger framework but production use is growing; evaluate current state |
| AutoGen | Conversational multi-agent framework with agent-to-agent dialogue | Core strength is multi-agent conversation and collaboration patterns | Fewer prebuilt integrations; often requires custom tool wrappers | Microsoft-backed, rapidly evolving; check current production support |
| Semantic Kernel | Lightweight orchestration layer with kernel, plugins, and planners | Supports multi-agent through process frameworks and plugin composition | Strong plugin model, especially for Microsoft ecosystem integration | Microsoft-supported, mature for production use with Azure integration |
Open-source frameworks are most compelling when you have a platform engineering team, need custom hosting or security controls, or want to build a reusable agent platform across multiple cloud providers. Managed platforms are usually better for teams that want to focus on agent logic rather than infrastructure, or who need prebuilt enterprise features like observability and policy controls.
Open-source frameworks evolve rapidly—evaluate the current state, not blog posts from six months ago
The open-source agent landscape is moving quickly. Features, APIs, and best practices from six months ago may be outdated. When evaluating frameworks, check the current documentation, recent releases, and active community discussions—not historical blog posts or tutorials.
Microsoft AutoGen
A programming framework for building agentic AI with multiple agents that can converse with each other to solve tasks.
Explore AutoGen DocumentationKnowledge Check
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