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The world of artificial intelligence is quickly moving past monolithic models into a lively ecosystem of specialized, collaborative AI agents. In this shifting landscape, the tools we use to build these agents and the protocols that enable their communication are vital. As I’ve explored in my ARC Advisory Group blog series on the rise of A2A (Agent-to-Agent) communication and its role in completing the Industrial AI Protocol Stack alongside OPC UA and the potential Model Context Protocol (MCP), clear roles and interoperability are essential.
Enter AWS Strands, an open-source AI agent SDK from Amazon Web Services. Rather than being just another competing standard, Strands is shaping up as a powerful and complementary piece of this puzzle. Its focus? Empowering developers to craft the intelligent actors that will populate this interconnected future.
AWS Strands: The Loom for Crafting Intelligent Agents
At its heart, AWS Strands is a developer-focused SDK that simplifies and accelerates the creation of AI agents. Its philosophy is elegant: connect a large language model (LLM) with a set of tools—APIs, functions, data sources—much like the two strands of DNA, to build an intelligent agent.
Key aspects of Strands include:
Model-Agnostic Approach: Strands isn’t locked to a single LLM. It supports models from Amazon Bedrock, Anthropic, Ollama, and others via LiteLLM, giving developers flexibility.
Emphasis on Tooling: It makes it easy to integrate a rich ecosystem of tools, including support for MCP, which standardizes how agents interact with external tools and data—essential for industrial contexts where OPC UA provides secure data access.
Simplified Development: Strands reduces boilerplate code and orchestration logic, allowing the chosen LLM to handle more of the planning and execution.
Open Source: This fosters community collaboration and reduces vendor lock-in.
Built-in Observability: With tracing, logs, and metrics included, it’s production-ready out of the box.
Strands, in essence, is the loom for developers to weave sophisticated AI agents capable of performing complex tasks.
A2A: The Common Language for Agent Collaboration
The Agent-to-Agent (A2A) protocol, as detailed in my ARC series, standardizes how these agents—no matter how they were built—communicate, coordinate, and collaborate. If Strands helps build the “actors,” A2A provides the “script” and “stage directions” for their interactions.
A2A standardizes:
How agents discover each other’s capabilities.
How they assign and manage tasks.
How they exchange information and updates.
This is critical to realizing a multi-agent ecosystem where specialized agents (like predictive maintenance or energy management agents) work together to drive smarter industrial automation.
Complementary, Not Competing: Strands and A2A in the Protocol Stack
The relationship between AWS Strands and A2A is fundamentally complementary. AWS has stated that Strands will support A2A, which means:
Developers can build robust, tool-augmented AI agents with Strands.
These Strands-built agents can then use A2A as a communication backbone to collaborate with other agents, regardless of the frameworks those agents were built on.
In the Industrial AI Protocol Stack:
OPC UA ensures secure, semantic data access from OT assets.
MCP (supported by Strands and other tools) standardizes tool and data interactions for agents.
AWS Strands provides the SDK to build diverse agents that leverage MCP.
A2A enables those agents to collaborate and orchestrate complex industrial tasks.
This layered architecture—from data acquisition (OPC UA) to agent collaboration (A2A)—is crucial for building intelligent, autonomous industrial systems.
AWS Strands and the Value of Openness for Interoperability
When considering different paths in AI development, including Microsoft’s comprehensive Azure AI Studio (now part of Azure AI Foundry), AWS Strands stands apart with its open-source foundation. This openness sets Strands apart, making it more than just another toolkit—it’s a strong contribution to achieving interoperability in the AI agent ecosystem.
This isn’t about a direct feature-for-feature battle; rather, it’s about strategic differences. By being open source, AWS Strands encourages a more collaborative development environment, welcomes community contributions, reduces the risk of vendor lock-in, and allows agents built with Strands to operate and communicate across a wider range of platforms that also adopt open standards. This approach is another important building block for creating a genuinely interconnected ecosystem, rather than isolated proprietary solutions.
Weaving the Future, Strand by Strand
AWS Strands is a strong addition to the AI developer’s toolkit. Its open-source foundation and dedicated focus on simplifying agent creation, paired with its support for protocols like MCP and A2A, make it a natural fit within the vision of a collaborative, multi-agent Industrial AI Protocol Stack.
By offering the tools to create intelligent “actors” and embracing the standards for their “communication,” Strands has the potential to help weave the intricate tapestry of next-generation industrial AI—a future rooted in collaboration, openness, and interoperability. This open-standards mindset is especially significant as AWS strengthens its presence and offerings in the industrial sector.
The company’s strategy, as I discussed in “Choosing Your Industrial Future: Navigating the AWS vs. Azure Platform Showdown,” often centers on offering foundational, flexible services that empower customers to build. Strands fits neatly within this approach, marking a promising step in a direction that contrasts with more comprehensive, proprietary platforms and aligns well with the need for a flexible and robust industrial AI ecosystem.
Engage with ARC Advisory Group
For ARC Advisory Group recommendations for Navigating the AI Wars, Closing the Digital Divide by Embracing Industrial AI, assembling your Industrial-Grade Data Fabric, and governing and guiding major decisions about enterprise, cloud, industrial edge, and AI software, please contact Colin Masson at [email protected] or set up a meeting with me, or my fellow Analysts at ARC Advisory Group.