Managed Agents Explained: Anthropic vs Google and What It Means for Builders
Anthropic and Google both ship managed agents but with opposite philosophies. Learn the key differences and which approach fits your use case.
What “Managed Agents” Actually Means
Multi-agent systems are becoming a standard architectural pattern for AI applications. But as the ecosystem matures, a new layer of abstraction has emerged: managed agents — where the cloud provider or AI company handles the execution runtime, orchestration logic, and infrastructure, so you don’t have to build it from scratch.
Two companies have staked out distinctly different positions here: Anthropic and Google. Both offer multi-agent capabilities, both have published protocols for agent-to-agent communication, and both are actively building frameworks that developers can use today. But their philosophies — and the practical implications for builders — are quite different.
This article breaks down how each company approaches managed agents, where they diverge, and what those differences mean if you’re actually building something.
Defining the Category: What Makes an Agent “Managed”?
Before comparing approaches, it helps to be precise about what “managed” means in this context.
An unmanaged agent is one where you build and own the entire stack: the orchestration loop, tool execution, state management, retries, scaling, and observability. Frameworks like LangChain or AutoGen fall here — they give you components, but you assemble and host the runtime.
A managed agent shifts some or all of that responsibility to the provider. At minimum, that means:
- Hosted execution — the agent runs on the provider’s infrastructure
- Built-in orchestration — the provider handles multi-step reasoning loops
- Tool/integration management — connections to external services are configured once and reused
- State persistence — conversation or task context is maintained across calls
- Scaling and reliability — the provider manages availability
The tradeoffs are the same as with any managed service: you get less control and more dependency, but you move faster and own less infrastructure.
Both Anthropic and Google offer varying degrees of this. Where they diverge is in how they think agents should be structured in the first place.
Anthropic’s Approach: Protocols Over Platforms
The MCP-First Philosophy
Anthropic’s most significant contribution to the agent ecosystem isn’t a hosted agent platform — it’s a protocol. The Model Context Protocol (MCP) is an open standard that defines how AI models communicate with external tools, data sources, and services.
MCP works like a standardized plug format. An MCP server exposes a set of tools or resources. Any MCP client — whether that’s Claude, a developer’s custom agent, or another AI system — can connect to it and use those tools through a consistent interface. The server handles authentication, rate limiting, and data transformation. The client handles reasoning about when and how to call it.
This is deliberately composable. Rather than locking developers into a proprietary integration layer, Anthropic bet on an open standard that others could adopt. And that bet has been paying off — MCP has seen significant adoption from tooling companies, IDE vendors, and other AI providers since its release.
What Claude Actually Does as an Agent
Claude (the model) has strong native tool use capabilities. You can pass it a list of tool definitions, and it will decide when to call them, parse results, and continue reasoning. This is the foundation of Anthropic’s agent runtime.
On top of this, Anthropic has published detailed guidance on multi-agent architectures. Their documented patterns include:
- Orchestrator-subagent: One Claude instance coordinates a set of specialized Claude instances
- Parallel agents: Multiple agents run concurrently and their results are synthesized
- Human-in-the-loop: Agents pause and request clarification before taking consequential actions
What Anthropic hasn’t built — at least not in the same way Google has — is a large, fully managed cloud platform for deploying these patterns at scale. The managed layer in Anthropic’s world is largely provided by the API and by MCP servers. The developer owns the orchestration infrastructure, or delegates it to a third party.
Safety as Architecture
Anthropic’s agent design reflects their broader safety priorities. They recommend that agents operate with minimal permissions, prefer reversible actions over irreversible ones, and include explicit human-confirmation steps for high-stakes operations.
This isn’t just guidance — it shapes the tooling they prioritize. MCP servers are designed to be constrained in scope. Claude’s system prompt architecture makes it easier to inject safety constraints at the operator level. These are opinionated choices about how agents should behave, baked into the design patterns rather than bolted on afterward.
Google’s Approach: Platform Over Protocol
Agent Builder and Vertex AI
Google’s agent strategy is platform-first. Vertex AI Agent Builder is a managed cloud service where you define agents through a configuration interface, connect them to Google’s tooling (Search, Maps, Databases, Workspace, etc.), and deploy them to a hosted runtime. Google handles the infrastructure.
Plans first. Then code.
Remy writes the spec, manages the build, and ships the app.
This is more analogous to a traditional PaaS model applied to AI. You’re not writing orchestration code — you’re configuring it. You define your agent’s behavior, select its tools, set up datastores for retrieval-augmented generation, and deploy. The platform manages state, scaling, and execution.
For enterprise buyers who already live in Google Cloud, this is appealing. The integrations with BigQuery, Cloud Storage, Workspace, and Search are native, not bolted on. Identity management, audit logging, and compliance tooling are inherited from the Google Cloud stack.
The Agent Development Kit
Alongside the managed platform, Google released the Agent Development Kit (ADK) — an open-source Python framework for building agents programmatically. ADK is more code-centric than Agent Builder and gives developers finer control over orchestration logic.
ADK supports a mix of agent patterns: sequential, parallel, and loop-based. It has native support for Gemini models and provides integrations for common tools. It also plays well with LangGraph, which many developers are already using for complex multi-agent graphs.
The ADK sits between Anthropic’s raw API approach and Agent Builder’s no-code configuration model. It’s the right choice for developers who want to stay in Google’s ecosystem but need more flexibility than a managed config-based service provides.
Agent2Agent Protocol
In response to the traction MCP gained, Google introduced the Agent2Agent (A2A) protocol. Where MCP focuses on model-to-tool communication, A2A is designed for agent-to-agent communication — specifically, for agents built by different vendors to coordinate tasks.
A2A uses an HTTP/JSON model where agents expose an “agent card” describing their capabilities, and other agents can discover and invoke them. The protocol handles task delegation, progress updates, and result retrieval across agent boundaries.
A2A and MCP are complementary rather than competing. MCP handles the model-to-tool layer; A2A handles cross-agent coordination. Google has positioned A2A as an open standard, but the initial momentum and ecosystem around MCP is currently larger.
Google’s Multimodal and Search Advantages
One area where Google’s managed agent approach has a genuine edge: native integration with Google Search and multimodal capabilities. Agents built on Vertex AI can invoke grounded search — meaning factual queries are resolved against live web results automatically, not through a separate tool call that the developer has to wire up.
For use cases that depend on up-to-date factual knowledge (news monitoring, research, due diligence workflows), this native grounding is a meaningful practical advantage.
Head-to-Head: Key Differences
| Dimension | Anthropic | |
|---|---|---|
| Primary philosophy | Protocol-first, composable | Platform-first, integrated |
| Hosted execution | Limited (API + partner hosting) | Full managed platform (Vertex AI) |
| Open standards | MCP (widely adopted) | A2A (growing), ADK (open-source) |
| Tool connectivity | MCP servers, custom tools | Native Google services + tool use |
| Multi-agent patterns | Developer-built or MCP-composed | Agent Builder config or ADK code |
| Safety emphasis | Explicit, opinionated | Present but less foregrounded |
| Enterprise integration | Any cloud, model-agnostic | Deep Google Cloud integration |
| Learning curve | Lower for developers, higher infra burden | Lower infra burden, steeper platform lock-in |
| Best model strength | Nuanced reasoning, instruction-following | Multimodal, search grounding, speed |
Where Anthropic Wins
Anthropic’s approach is better when:
- You need to run agents across multiple cloud providers or on-premises
- You want to avoid vendor lock-in at the infrastructure layer
- You’re building applications where safety constraints and permission scoping are critical
- Your team prefers composing from primitives rather than configuring a managed platform
- You need the best raw reasoning quality for complex, multi-step tasks
Everyone else built a construction worker.
We built the contractor.
One file at a time.
UI, API, database, deploy.
Where Google Wins
Google’s approach is better when:
- You’re already invested in Google Cloud
- Your agents need real-time web grounding or deep integration with Google Workspace
- You want to minimize the infrastructure you manage yourself
- Your team is more comfortable with cloud-managed services than with building orchestration from scratch
- You need enterprise compliance tooling out of the box
What This Means for Builders in Practice
The Orchestration Question Is the Key Decision
Both companies solve the model-reasoning problem well. Where the real decision lies is orchestration: who builds and maintains the logic that coordinates agents, sequences steps, handles failures, and persists state?
With Anthropic’s approach, that’s largely your problem — or you use a third-party orchestration layer. With Google’s approach, Agent Builder absorbs a lot of that, at the cost of flexibility and portability.
Neither answer is wrong. But underestimating the orchestration problem is a common mistake. Multi-agent systems fail in production not because the AI is bad at reasoning, but because the glue code is fragile.
MCP Has Become a De Facto Standard
Regardless of which company’s model you prefer, MCP has established itself as the emerging standard for tool connectivity. Major IDE vendors (Cursor, Zed, VS Code), data platforms, and SaaS companies have shipped MCP servers. Claude, GPT-4o, and third-party agent frameworks all support it.
If you’re building an agent that connects to external tools, designing your integrations as MCP servers is probably the right move. It makes your integrations reusable across models and frameworks, and it reduces the amount of custom plumbing you write per use case.
Model Choice Isn’t Permanent
One underappreciated consequence of the protocol-first vs. platform-first divide: if you build against Anthropic’s API + MCP, you can swap in a different model without rebuilding your integrations. If you build deeply into Vertex AI Agent Builder, switching the underlying model requires working within Google’s configuration model.
This matters because model capabilities are improving fast, and the best model for a given task may not be the same model six months from now. Portability has real long-term value.
Latency and Concurrency Still Matter
Managed agent platforms abstract infrastructure, but they don’t eliminate latency. Multi-agent systems involve multiple sequential (and sometimes parallel) model calls. For user-facing applications, this adds up quickly.
Google’s infrastructure, backed by global edge capacity, generally offers strong low-latency performance. Anthropic’s API is fast but the orchestration overhead — which lives in your infrastructure or a third party’s — adds to your total latency budget. Plan accordingly.
Where MindStudio Fits
Building on either Anthropic or Google’s managed agent approach still leaves a gap: you need to configure tools, connect integrations, define orchestration flows, and handle deployment. For developers, that’s manageable. For most business teams, it’s a barrier.
MindStudio addresses this directly. It’s a no-code platform that lets you build multi-agent workflows visually, with access to both Claude and Gemini (plus 200+ other models) without needing separate API accounts or infrastructure setup.
Remy doesn't write the code. It manages the agents who do.
Remy runs the project. The specialists do the work. You work with the PM, not the implementers.
The relevant angle here: MindStudio abstracts the orchestration layer that both Anthropic and Google leave to the builder. You can create agents that call Claude for reasoning, Gemini for grounded search, or any other model — and connect them to 1,000+ pre-built integrations (HubSpot, Salesforce, Google Workspace, Slack, and more) without writing orchestration code.
If you’re trying to evaluate whether Anthropic’s or Google’s approach fits your use case, building a prototype in MindStudio is a practical way to test the integration patterns before committing to a deeper infrastructure investment. The average build takes 15 minutes to an hour.
You can try it free at mindstudio.ai.
For teams that do want to go deeper on the code side, MindStudio also offers an Agent Skills Plugin — an npm SDK that lets any existing agent (LangChain, CrewAI, Claude Code, custom agents) call MindStudio’s 120+ typed capabilities as simple method calls, handling rate limiting, retries, and auth automatically.
Frequently Asked Questions
What is a managed agent?
A managed agent is an AI agent where the provider — rather than the developer — handles key parts of the execution infrastructure: hosting, orchestration, state management, and scaling. This is in contrast to self-hosted or framework-based agents, where the developer owns the entire runtime stack. Both Anthropic and Google offer managed agent capabilities, though with different scope and emphasis.
What is Anthropic’s Model Context Protocol (MCP)?
MCP is an open standard published by Anthropic that defines how AI models communicate with external tools and data sources. It works through MCP servers — lightweight services that expose tools via a standardized interface — and MCP clients, which are AI models or agent frameworks that call those tools. MCP has seen broad adoption and is now supported across multiple AI providers and developer tools, not just Claude.
What is Google’s Agent2Agent (A2A) protocol?
A2A is an HTTP-based protocol developed by Google for agent-to-agent communication. While MCP handles model-to-tool connectivity, A2A is designed to allow agents built by different teams or vendors to delegate tasks to each other. Agents expose an “agent card” describing their capabilities, and other agents discover and call them through standardized requests. A2A and MCP are designed to be complementary.
Should I use Anthropic or Google for building agents?
It depends primarily on your infrastructure context and integration needs. If you’re already on Google Cloud and need native search grounding or Workspace integration, Google’s managed platform is a natural fit. If you want model portability, prefer composing from primitives, or need strong safety guardrails built into the architecture, Anthropic’s API + MCP approach gives you more flexibility. Many production systems use both — Claude for reasoning tasks, Gemini for search-grounded tasks — with a third-party orchestration layer connecting them.
What is Vertex AI Agent Builder?
Vertex AI Agent Builder is Google’s managed cloud platform for deploying AI agents. It allows teams to define agent behavior through configuration, connect to Google services and external APIs, set up retrieval-augmented generation with enterprise datastores, and deploy to Google’s managed infrastructure. It’s designed for enterprise teams that want to minimize the infrastructure they own and maintain.
Can I use both Claude and Gemini in the same multi-agent system?
Coding agents automate the 5%. Remy runs the 95%.
The bottleneck was never typing the code. It was knowing what to build.
Yes. There’s no technical barrier to routing different tasks to different models within the same agent workflow. This is actually a common pattern in production systems — using each model where it performs best. The key architectural consideration is where your orchestration layer lives. If it’s in a provider-specific platform (like Vertex AI Agent Builder), routing to a different provider’s model adds friction. If it’s in a neutral orchestration layer or a platform like MindStudio, the routing is straightforward.
Key Takeaways
- Anthropic and Google both address multi-agent systems, but with opposite starting points: Anthropic leads with open protocols (MCP), Google leads with a managed cloud platform (Vertex AI Agent Builder).
- MCP has achieved broad adoption as a de facto standard for tool connectivity, regardless of which underlying model you use.
- Google’s approach offers stronger native integration with its own services and managed infrastructure — a real advantage for Google Cloud shops.
- Anthropic’s approach offers greater model portability and composability, with safety constraints built into the architectural patterns.
- The orchestration layer — the glue that sequences agents, handles failures, and persists state — is the most underestimated part of building multi-agent systems in production.
- For most builders, the right choice depends on where your integrations live, how much infrastructure you want to own, and whether you need to stay portable across model providers.
- Platforms like MindStudio let teams prototype and deploy multi-agent workflows without committing to a single provider’s infrastructure, supporting both Claude and Gemini across the same orchestration layer.
