XenonStack BlogMulti-Agent Platforms in 2026: Frameworks Compared

A multi-agent platform is the software layer that lets several AI agents, each with its own instructions, tools and permissions, work together on one task under shared orchestration, state, security and monitoring. In 2026 the choice is less about "which model" and more about four things: how agents are coordinated, how they connect to tools and to each other (the Model Context Protocol and the Agent2Agent protocol), how every step is traced, and where humans approve high-impact actions.

The market has consolidated. Microsoft has replaced AutoGen with Microsoft Agent Framework, Google ships the Agent Development Kit with native A2A support, AWS offers Bedrock AgentCore as a framework-neutral runtime, and open frameworks such as LangGraph, CrewAI, Strands Agents and the OpenAI Agents SDK cover the build layer. This guide compares them on their own documentation and sets out how to choose, what can go wrong and how to adopt in stages.

Disclosure: XenonStack offers Akira AI, which it describes as a multi-agent orchestration product, so we have a commercial interest in this market. The comparison below uses each vendor's own pages, read on 10 November 2026.

What is a multi-agent platform, and how is it different from a single agent?

A single AI agent is a language model in a loop with instructions and tools. Multi agent systems split work across specialised agents, for example a planner, a retriever, a checker and an executor, and add a coordination layer that decides who acts next, what state is shared and when to stop. A multi-agent framework gives developers the building blocks for that coordination. A multi-agent platform, sometimes sold as an agent orchestration platform, adds the operational layer around it: runtime, identity, memory, tool gateways, policy, evaluation and observability.

The split matters for buyers. Frameworks are mostly open source libraries your engineers own. Platforms are managed services or products that carry production concerns such as access control and audit. Most enterprises end up with both: a framework for agent logic and a platform or cloud service to run it safely. For a deeper look at how agents handle multi-step, judgement-heavy work, see our guide to AI agents for complex tasks and reasoning.

What has changed in multi-agent platforms since 2025?

• AutoGen is archived. The AutoGen repository states the project is "archived and no longer maintained" and that "new users should start with Microsoft Agent Framework", which it calls the successor to AutoGen.

• Microsoft Agent Framework merges AutoGen and Semantic Kernel. Microsoft Learn describes it as the direct successor created by the same teams, combining AutoGen's agent abstractions with Semantic Kernel's enterprise features and adding graph-based workflows. It is listed as being in public preview.

• Protocols moved to neutral foundations. The Linux Foundation announced the Agentic AI Foundation on 9 December 2025 with founding contributions including Anthropic's Model Context Protocol, Block's goose and OpenAI's AGENTS.md. The A2A protocol site states A2A was originally developed by Google and donated to the Linux Foundation, under the Apache 2.0 licence.

• Agent-specific security guidance now exists. OWASP published the Top 10 for Agentic Applications for 2026, dated 9 December 2025, alongside the existing Top 10 for LLM Applications 2025.

Which multi-agent platforms and frameworks should enterprises compare in 2026?

The table lists current AI agent platforms and frameworks in alphabetical order. Each row reflects only what the vendor's own page states. "Not stated" means we could not confirm it on the official page, not that the capability is missing.

Two protocols sit underneath this table rather than inside it. The Model Context Protocol is an open standard for connecting AI applications to external systems such as data sources and tools; its current specification is versioned 2026-07-28. A2A covers a different link: the A2A site states "MCP is for agent-to-tool communication" and "A2A is for agent-to-agent communication".

How do you choose a multi-agent platform?

Which orchestration pattern does the work need?

Start from the process, not the product. Fixed, auditable steps suit sequential or graph workflows (Microsoft Agent Framework workflows, LangGraph, ADK graph workflows). Open-ended research suits a manager agent that delegates to specialists (OpenAI Agents SDK handoffs, Strands agents-as-tools). Exploratory problem-solving can use swarm or group patterns, but these are the hardest to predict and audit. If a deterministic workflow with one agent step will do, prefer it.

Will it interoperate through MCP and A2A?

Ask whether tools are exposed through MCP so they can be reused across frameworks, and whether agents can be published or consumed over A2A so a team using ADK can call an agent built on Strands. Protocol support reduces lock-in, but check the specification version each vendor supports.

Can you see what every agent did?

Observability is non-negotiable for agent orchestration. Look for traces at the level of each model call, tool call and handoff, plus evaluations on real traffic. AgentCore Evaluations, for example, works on traces instrumented with OpenTelemetry or OpenInference, and LangSmith and the OpenAI Agents SDK include tracing.

Where are governance controls and human approval gates?

Map which actions are reversible and which are not. Irreversible or regulated actions (payments, customer communications, production changes) need a human approval step that the platform enforces, not one written into a prompt. Microsoft Agent Framework, LangGraph, CrewAI and the OpenAI Agents SDK all document human-in-the-loop mechanisms; AgentCore Policy adds deterministic boundaries at the platform layer. Pair this with separate identities and least-privilege credentials per agent.

What will it cost to run?

Multi-agent designs multiply model calls: every handoff, critique loop and retry consumes tokens. OWASP lists "Unbounded Consumption" as a top LLM risk for this reason. Set per-run token and step budgets, cache tool results, use smaller models for routing and checking, and price the runtime, memory and observability services separately from model usage. Deployment location also affects cost; our NexaStack page covers inference deployment across cloud, edge and on-premises environments.

What are the main risks of multi agent systems?

The OWASP Top 10 for LLM Applications 2025 already covers Prompt Injection (LLM01), Excessive Agency (LLM06) and Unbounded Consumption (LLM10), all of which grow when agents act on each other's outputs. OWASP's agentic guidance, announced in December 2025, names further risks including agent behaviour hijacking, tool misuse and exploitation, and identity and privilege abuse.

Security teams can extend existing controls to agents, for example with AI bills of materials and risk scoring, which XenonStack lists as focus areas for MetaSecure.

What is a sensible adoption path?

1. Pick one bounded process. Choose work with clear inputs, measurable outcomes and reversible actions, such as triage or document review.

2. Start with the simplest pattern. One agent plus deterministic steps; add agents only where specialisation measurably helps.

3. Standardise tools on MCP early. Build an approved server list so tools are reusable and reviewable.

4. Instrument before you scale. Turn on tracing and evaluations from the pilot, and define cost and quality thresholds.

5. Add governance gates. Per-agent identities, scoped permissions and human approval for irreversible actions, mapped against the OWASP lists.

6. Federate with A2A when teams diverge. Once several teams build agents on different frameworks, use A2A to connect them rather than forcing one stack.

When should you not use a multi-agent platform?

If the task is a fixed sequence that a workflow engine or a single agent can complete, extra agents add cost, latency and failure points without adding accuracy. Avoid multi-agent designs where you cannot yet trace or evaluate a single agent, where actions are irreversible and approval gates are not in place, or where one preview-stage framework would become a hard dependency for a regulated process.

Frequently asked questions

What is the difference between a multi-agent framework and a multi-agent platform?

A framework, such as LangGraph, CrewAI, Google ADK or Strands Agents, provides code-level building blocks for agents and their coordination. A platform, such as Amazon Bedrock AgentCore, adds managed runtime, identity, memory, policy and observability for production.

Is AutoGen still supported?

No. The AutoGen repository says it is archived and no longer maintained, and directs new users to Microsoft Agent Framework, which Microsoft lists as being in public preview.

How do MCP and A2A differ?

MCP connects an agent to tools and data. A2A connects agents to other agents across frameworks and vendors. Both are open, and both are now stewarded under the Linux Foundation.

Which multi-agent platform is best for enterprises?

There is no single best choice. Microsoft estates will look at Microsoft Agent Framework, AWS estates at AgentCore with Strands or LangGraph, and Google Cloud estates at ADK. Decide on orchestration pattern, protocol support, observability and governance first, then the vendor.

What security guidance applies to AI agent platforms?

Use the OWASP Top 10 for LLM Applications 2025 and the OWASP Top 10 for Agentic Applications for 2026, together with the security principles in the MCP specification on user consent, data privacy and tool safety.