Enterprise AI Agent Platforms Compared (2026)

The three major enterprise agent platforms have converged, and the decision between them is now mostly a decision about where your data and identity already live. Amazon, Microsoft and Google all ship a managed agent runtime that hosts agents written against external frameworks, exposes tools through the Model Context Protocol, and gives every agent its own identity. Two years ago those were differentiators. In 2026 they are table stakes, and choosing on them means choosing on nothing.

There is a smaller point worth making first, because it tells you how quickly this space is moving: most comparison articles ranking for this topic are using product names that no longer exist. AWS moved from Bedrock Agents to Bedrock AgentCore. Microsoft’s documentation now reads Microsoft Foundry Agent Service, not Azure AI Foundry. Google’s agent runtime is documented as the Gemini Enterprise Agent Platform, not Vertex AI Agent Builder. If a comparison still uses the old names, it predates the convergence it is trying to describe.

The Three Platforms at a Glance

Every claim in this table comes from each vendor’s own current documentation, linked above. Read down the last column rather than across the middle ones - the middle columns are where they now look alike.

Platform or Framework? They Are Different Decisions

An agent framework decides how your agent thinks. An agent platform decides where it runs, what it can reach, and who can audit it. This distinction sounds pedantic until you watch a team spend a quarter arguing about the wrong one.

The framework is a library you import. It shapes control flow - the order steps run in, who arbitrates, whether a run can resume after failure. It lives inside your codebase and it is a design decision. I have written about that layer in detail in LangGraph vs CrewAI, and about the architecture patterns underneath it in the multi-agent systems guide.

The platform is infrastructure you deploy onto. It answers a different set of questions: where does session state live, what identity does this agent authenticate as, which tools is it permitted to call, and when it does something unexpected at 2am, what record exists of why. None of that is agent design. All of it is tedious, security-sensitive, and expensive to rebuild badly.

The most common failure I see is a team treating platform concerns as framework concerns. They pick a framework, discover it has no opinion about identity or audit trails, and start building those inside their orchestration code - session persistence bolted onto a graph, secrets handled in application logic, a homegrown log that nobody can query. Six months later they have an agent runtime nobody asked them to build and an agent design nobody has validated.

What Converged, and Why It Matters

Three things happened more or less simultaneously, and together they hollowed out the traditional comparison.

Bring-your-own-framework became universal. Every one of the three platforms now hosts agents written against external frameworks rather than requiring a proprietary workflow format. AWS states the position plainly - any framework, any model. Microsoft’s hosted agents accept LangGraph, the OpenAI Agents SDK, the Anthropic SDK or your own code as a container. Google’s runtime deploys ADK, LangGraph, LangChain, AG2 and LlamaIndex agents. The practical consequence is that your orchestration code is no longer the thing that binds you to a vendor.

MCP removed tool integration as a differentiator. Platforms used to compete on connector catalogues, and a longer list was a real reason to choose one. Now they consume and expose Model Context Protocol servers - Microsoft goes as far as exposing curated toolboxes behind a single managed MCP endpoint that any runtime can consume regardless of framework. An integration you build against MCP is largely portable, so catalogue size stopped being a moat.

Agent identity became first-class everywhere. Each platform now issues agents their own identity rather than having them borrow a service account. That is the single most important governance development of the past year, and it arrived on all three at roughly the same time.

Strip those three out and the remaining differences are real but narrow: where your data already sits, which identity provider you already run, and where you want agents to appear in front of staff. Those are the differences worth deciding on.

How to Actually Choose

My working rule is uncomfortable for anyone who enjoys evaluation matrices: pick the platform belonging to the cloud your data and identity provider already live in, unless you have a specific reason not to.

The reason is data gravity. An agent is only useful in proportion to what it can reach, and what it can reach is your data. Choosing a platform in a different cloud from your data means paying egress, adding latency to every retrieval, widening your compliance surface across two providers, and federating identity between them. That cost is paid on every request, forever. A feature advantage that looked decisive in a spreadsheet rarely survives contact with it.

Three situations genuinely override the rule. The first is distribution: if the agent’s job is to sit inside Teams or Microsoft 365 where your staff already work, the publishing path is worth more than the data-locality argument. The second is a hard regulatory constraint - a residency requirement one provider meets in your jurisdiction and another does not. The third is an existing orchestration investment you are not going to abandon, in which case the question becomes which platform hosts your framework most comfortably rather than which platform is best.

Notice what is not on that list: agent capability. In 2026 that is not where these platforms differ, and evaluating them as though it is produces a decision that looks rigorous and is arbitrary.

Four Questions Before You Commit

These are the questions I put to a platform decision, in the order they tend to matter. They are deliberately about operations rather than features, because features are where these platforms have converged and operations is where they have not.

• What does this agent authenticate as, and who granted it that? If the answer is a shared service account, you have no meaningful audit trail and no ability to scope one agent differently from another. Per-agent identity is available on all three platforms now; not using it is a choice.
• When an agent does something wrong, what record exists of why? Not that it failed - why it chose the step it chose. Step-level tracing is the difference between diagnosing an agent and re-running it hopefully.
• How are tool credentials held, and can access be scoped per agent? An agent that can reach a tool can be steered toward that tool by anything it reads, so treat retrieved content as data and never as instructions. That is an architectural commitment, not a platform setting.
• What is the migration surface if we leave? The agent code is the easy part. The tool connections and their auth, the identities and policies, the evaluation history - that is the real cost, and it is worth knowing its shape before you accumulate three years of it.

Where the Lock-In Actually Lives Now

Because orchestration code became portable, it is tempting to conclude that agent platforms stopped being sticky. They did not - the stickiness moved somewhere harder to see.

What binds you now is the accumulated operational surface. Every tool connection carries configured credentials and scopes. Every agent identity carries access policies that someone reasoned about and nobody documented. Every evaluation run is history you would lose. And the data those agents reach is, by design, the data you were never going to move casually.

This is a better kind of lock-in than the old sort, because it is at least visible and proportional to value received. But it does mean the migration question is worth asking early rather than late. Teams that plan portability around the agent code are protecting the one asset that is already portable.

Frequently Asked Questions

What is an enterprise AI agent platform?

An enterprise AI agent platform is managed infrastructure for running agents in production: a hosted runtime, session and memory storage, a governed way to expose tools, an identity for each agent, and tracing over what the agent actually did. It is a different layer from an agent framework. A framework such as LangGraph or CrewAI decides how your agent thinks - the control flow, who decides what runs next. A platform decides where that code runs, what it is allowed to reach, and who can audit it afterwards. You generally need both, and on the current platforms you can pair almost any framework with any platform.

What is the difference between an agent platform and an agent framework?

Scope of responsibility. The framework is a library you import; it shapes your orchestration logic and lives inside your codebase. The platform is infrastructure you deploy onto; it handles hosting, scaling, identity, tool governance and observability. Teams that conflate the two usually end up either rebuilding platform concerns inside their framework code - session persistence, audit logs, secret handling - or picking a platform for orchestration features they were always going to write themselves.

Which AI agent platform should an enterprise choose?

In almost every case, the one belonging to the cloud where your data and identity provider already live. All three major platforms now host agents built with external frameworks, expose tools through MCP, and give each agent a first-class identity, so the capability gap that used to drive this decision has largely closed. What has not converged is data gravity: the cost, latency and compliance overhead of moving enterprise data to a different cloud, and the work of federating identity across two providers. If your organisation runs on Microsoft 365, Foundry starts ahead on grounding and publishing; if your data is in S3 and Redshift, AgentCore does; if it is in BigQuery, Gemini Enterprise does.

Do agent platforms lock you in?

Less than they did, and the lock-in has moved. Because each platform now runs agents written against external frameworks, your orchestration logic is comparatively portable - that code is a container or a package, not a proprietary workflow definition. What is not portable is everything around it: the tool connections and their authentication, the agent identities and access policies, the evaluation history and traces, and the data those agents reach. Plan migration around that surface rather than around the agent code, which is usually the easiest part to move.

How does MCP change the agent platform decision?

It removed tool integration as a differentiator. Before the Model Context Protocol, each platform had its own connector catalogue, and the breadth of that catalogue was a genuine reason to choose one. Now the platforms consume and expose MCP servers, so the integrations you build are largely portable between them and a long connector list is no longer a moat. The remaining questions about tools are governance ones - how credentials are held, whether tool access can be scoped per agent, and whether tool versions can be rolled forward centrally rather than rewired agent by agent.

Should we use a managed agent platform or self-host?

Self-host when you have a specific constraint the managed platforms cannot meet - a regulated environment with no acceptable cloud region, an existing orchestration layer you are not going to abandon, or latency requirements that rule out a hosted runtime. Otherwise start managed. The infrastructure these platforms provide - session persistence, per-agent identity, tool governance, tracing - is genuinely tedious to rebuild, and rebuilding it is not where an agent project earns its value. The failure I see most often is a team spending its first quarter building an agent runtime and its second discovering the agent design was wrong.

The Decision Is Smaller Than It Looks

Enterprise agent platforms spent two years differentiating and then converged on the same answer: run any framework, expose tools through MCP, give every agent an identity, trace everything. That convergence is good news, because it means the platform decision is no longer the one that determines whether your agent project succeeds.

What determines that is the architecture - whether the work genuinely needs agents at all, whether the retrieval underneath them is sound, and whether a human sits at the checkpoints that matter. I have written about when agents are the wrong answer in agentic AI explained, and if you are weighing whether to build this in-house or bring in help, my AI agent development page covers how I work with teams on it.

Pick the platform your data already lives next to. Spend the argument you saved on the architecture instead.