Google vs OpenAI vs Anthropic: The Agentic AI Arms Race Breakdown

In this article we will analyze how Google, OpenAI, and Anthropic are productizing ‘agentic’ capabilities across computer-use control, tool/function calling, orchestration, governance, and enterprise packaging.

Agent platforms, not only models, now define competitive advantage. Google is aligning Gemini 2.0 with an enterprise control plane on Vertex AI and a new ‘front door’ called Gemini Enterprise. OpenAI is consolidating developer early around the Responses API, packaging agent lifecycle elements as AgentKit, and deploying a general GUI controller called the Computer-Using Agent (CUA). Anthropic is expanding Computer Use while turning Artifacts into a lightweight app-builder for rapid internal tools. ​

OpenAI: CUA for GUI Autonomy, Responses as Agent Surface, and AgentKit for Lifecycle

Computer-Using Agent (CUA)

OpenAI introduced Operator in January 2025, powered by the CUA model. CUA combines GPT-4o-class vision with reinforcement learning for GUI policies, executing using human-like early development: screen perception, mouse, and keyboard. The stated purpose is a single interface that generalizes across web and desktop tasks.​

Responses API

OpenAI repositioned Responses as the primary agent-native API. The design folds chat, tool use, state, and multimodality into one early step and is marketed as the integration surface for GPT-5-era reasoning workflow. This simplifies the historical split across Chat Completions and Assistants, formalizing hosted tools and persistent reasoning in a single endpoint.​

AgentKit

Launched in October 2025, AgentKit packages agent building blocks: visual design surfaces, connectors/registries, evaluation hooks, and embeddable agent UIs. The aim is to reduce orchestration sprawl and standardize agent lifecycle from design to deployment. ​

Risk Profile

Early third-party evaluations note brittleness on practical automations: flaky DOM targets, window focus loss, and recovery failure on layout changes. While not unique to OpenAI, this matters for production SLAs. Teams should instrument retries, stabilize selectors, and gate high-risk steps behind review. Pair CUA experiments with execution-based evaluation such as OSWorld tasks.​

Position: OpenAI is optimizing for a programmable agent substrate: a single API surface (Responses), a lifecycle kit (AgentKit), and a universal GUI controller (CUA). For teams willing to own their evaluation harness and operations, this stack provides tight control and fast iteration loops.​

Google: Gemini 2.0 and Astra for Perception, Vertex AI Agent Builder for Orchestration, Gemini Enterprise for Governance

Models and Runtime

Google frames Gemini 2.0 as ‘built for the agentic era,’ with native tool use and multimodal I/O including image/audio output. Project Astra demonstrations highlight low-latency, always-on perception and continuous assistance patterns that map to planning plus acting loops. These capabilities are intended to feed Gemini Live and the broader agent runtime.​

Vertex AI Agent Builder

Google’s control plane for building and deploying agents on GCP is Vertex AI Agent Builder. The official documentation shows Agent Garden for templates and tools, orchestration for multi-agent experiences, and integration with other Vertex components. This serves as the platform to implement policies, logging, and evaluation pipelines for GCP users.​

Gemini Enterprise

In October 2025, Google announced Gemini Enterprise as a governed front door to ‘discover, create, share, and run AI agents’ with central policy and visibility. It emphasize cross-suite context spanning Google Workspace and Microsoft 365/SharePoint, plus line-of-business integrations such as Salesforce and SAP. This is positioned as a fleet-level governance layer, not only a development kit.​

Application Surface

Google is also pushing agentic control into end-user environments. Agent Mode in the Gemini app and Project Mariner extend consumer and prosumer workflows: teach-and-repeat, multi-task management, and autonomous execution for common tasks like search and filtering. This serves as both a data source for guardrails and a proving ground for UI-safety patterns.​

Position: Google is optimizing for governed enterprise deployment with wide surface integration. If you need centralized policy/visibility across many agents, with Workspace and cross-suite context, the Gemini Enterprise + Vertex pairing offers the most prescriptive path today.​

Anthropic: Computer Use and App-Builder Path via Artifacts

Computer Use

Anthropic introduced Computer Use for Claude 3.5 Sonnet in October 2024, explicitly as a beta capability that requires appropriate software setup to emulate human cursor and keyboard interactions. The company has been quite transparent about error profiles and the need for careful mediation. For production, expect policy-first defaults and incremental broadening rather than a hard pivot to full autonomy.​

Artifacts → App Building

In June 2025, Anthropic extended Artifacts from an inline canvas to build, host, and share interactive apps directly from Claude. The feature targets rapid internal tools and shareable mini-apps. Developers can create apps that call back into Claude via a new API, and published app usage bills the end user rather than the author.​

Position: Anthropic is optimizing for fast human-in-the-loop creation with explicit safety posture. The combination of Computer Use and Artifacts supports a design pattern where users co-pilot agents, validate actions, and graduate prototypes into shareable internal apps without heavy scaffolding.​

Benchmarks That Matter for Agent Selection

Function/Tool Calling

The Berkeley Function-Calling Leaderboard (BFCL) V4 expands beyond single calls to multi-turn planning, live/non-live settings, and hallucination measurement. You can use BFCL for tool-routing quality, argument fidelity, and sequencing under state changes.​

Computer/Web Use

OSWorld defines a benchmark of 369 real desktop tasks with execution-based evaluations across OSes and multi-app workflows. Original results showed large human–agent gaps and identified GUI grounding as a major bottleneck. You can treat OSWorld as the minimum bar for assessing GUI agents, then layer domain-specific workflows.​

Conversational Tool Agents

τ-Bench simulates dynamic conversations where an agent must follow domain rules and interact with tools; the 2025 τ²-Bench extension adds dual-control scenarios where both the user and agent can act, increasing realism for support workflows. You can use these when you care about policy adherence, user guidance, and multi-trial reliability.​

Software-Engineering Agents

SWE-Bench family leaderboards cover end-to-end issue resolution; SWE-Bench Pro (2025) raises task difficulty and adds contamination resistance with 1,865 instances across 41 repositories. For engineering assistants, you should not rely on ‘Lite’ alone—run Verified or Pro with a locked scaffold.​​

Comparative Analysis

Model Core and Modality

OpenAI currently couples GPT-5-era orchestration via Responses with a general GUI controller (CUA). This allows one integration surface for reasoning and tools plus a controller trained with RL for on-screen actions. Google pushes Gemini 2.0 and Astra for low-latency multimodal perception with tool use, then exposes agent plumbing through Vertex and Gemini Enterprise. Anthropic advances Claude 3.5 with Computer Use, while offering Artifacts to transform prompts into shareable apps that can call the model. The differences map to strategy: programmable substrate (OpenAI), governed enterprise scale (Google), and human-in-the-loop app creation (Anthropic).​

Agent Platform and Lifecycle

OpenAI’s AgentKit is an opinionated toolkit that reduces custom scaffolds and aligns with Responses. Google’s Vertex AI Agent Builder offers multi-agent orchestration plus governance hooks in a GCP-native control plane. Anthropic’s Artifacts/app-builder anchors a rapid prototyping loop for internal tools and user-validated workflows. Select based on where you want to spend engineering effort: programmable pipelines (OpenAI), centralized IT management (Google), or fastest human-supervised iteration (Anthropic).​

Governance and Policy

Google’s Gemini Enterprise is the clearest statement of fleet-level governance: central policy, visibility, cross-suite context for Workspace and Microsoft 365, and connectors for line-of-business apps. OpenAI’s consolidation into Responses reduces integration surfaces and should simplify policy attachment, but enterprise posture varies by customer architecture. Anthropic’s default stance is cautious feature rollout with explicit policy framing and human mediation.​

Evaluation Story and External Signals

OpenAI claims strong computer-/browser-use performance for CUA, but independent harnesses like OSWorld still report significant gaps across agents. Google’s agent messaging leans on demonstrations and enterprise rollouts; verify claims on BFCL, OSWorld, and domain workloads in Vertex. Anthropic’s Artifacts provides a pathway to test-and-deploy small apps quickly, then measure them against τ-Bench-style dialogue tasks and OSWorld-style GUI tasks.

Deployment Guidance for Technical Teams

1) Lock the Runner Before the Model

You can adopt execution-based, state-aware harnesses. For GUI control, use OSWorld’s verified setups and task scripts. For tool orchestration, use BFCL V4’s multi-turn and hallucination components. For policy-bound dialogues, prefer τ/τ²-Bench. For engineering assistants, add SWE-Bench Verified or Pro. Keep the runner constant while iterating on models, prompts, and retries.​

2) Decide Where Governance Lives

If you need centralized visibility across many agents plus Workspace and Microsoft 365 context, Google’s Gemini Enterprise combined with Vertex AI Agent Builder provides the most prescriptive governance plane. If you want a programmable substrate and will own policy integration yourself, OpenAI’s Responses + AgentKit stack is coherent. Anthropic’s approach favors human-in-the-loop controls with clear policy boundaries through the product surface.​

3) Design for GUI Failure and Recovery

Selectors drift, window focus changes, and visual similarity confuses detectors. You can build retries, add ‘are we on the right page’ checks, and gate irreversible actions behind review. This guidance applies to OpenAI CUA and Anthropic Computer Use alike, and the gaps are documented in OSWorld results.​

4) Optimize for Your Iteration Style

If you prototype many small internal tools, Anthropic’s Artifacts/app-builder minimizes scaffolding and lets non-specialists contribute. If you need deeply programmable pipelines with hosted tools and memory, Responses plus AgentKit offers the most consolidated primitives today. For governed, fleet-level rollouts, Google’s Vertex + Gemini Enterprise stack is designed for IT-managed scale.​

Bottom Line by Vendor

OpenAI: A programmable agent substrate: Responses as the unifying API, AgentKit for lifecycle, and CUA for GUI autonomy. This stack is attractive when you want direct control over tools, memory, and evaluation and are prepared to operate your own runners. You can validate GUI tasks on OSWorld and dialogue planning on τ-Bench.​

Google: A governed enterprise plane: Vertex AI Agent Builder for orchestration and Gemini Enterprise for organization-wide policy, visibility, and cross-suite context. This may be the clearest route to standardized agent operations in large estates using Workspace or hybrid 365 environments. You can test tool quality on BFCL and GUI reliability on OSWorld before scaling.​

Anthropic: A human-in-the-loop path: Computer Use plus Artifacts/app-builder for rapid creation and sharing of internal apps. This works well for teams that want fast iteration with explicit checkpoints and policy framing. You can use τ-Bench to assess policy adherence and user guidance, and OSWorld to check GUI action reliability.​

Editorial Comments

The agentic AI landscape of 2025 reveals three fundamentally different philosophies that will likely define the next phase of enterprise AI adoption. OpenAI’s bet on a unified, programmable substrate reflects their developer-first DNA, but risks overwhelming teams without strong engineering capabilities. Google’s enterprise governance play is strategically sound given their Workspace dominance, yet feels bureaucratic compared to the nimble iteration cycles that define successful AI deployments. Anthropic’s human-in-the-loop approach appears most aligned with current organizational realities—where trust, not just capability, remains the bottleneck for AI adoption. The real winner may not be determined by technical superiority alone, but by which vendor best navigates the gap between AI possibility and enterprise practicality. With 95% of generative AI pilots failing to reach production according to MIT research, the platform that solves deployment friction rather than just model performance will likely capture the largest share of the projected $47.1 billion AI agent market by 2030.

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