If you are preparing to put autonomous AI agents to work across your business, chances are your stack will run through three well-known names: Microsoft, Nvidia and Google. That is the conclusion of a new 360 Quadrant analysis from Research and Markets, which has put these companies at “the center” of an emerging market for agentic AI — computer systems that can plan how they will act on a task or tasks and then iterate on those decisions without much human intervention.
Why these three are the anchor points for the agentic AI stack
Agentic workflows are easily lost across models, tools and data. They still need orchestration, secure connectivity, and scalable prowess — as well as tight integration with everyday productivity apps. Microsoft, Nvidia and Google dominate those bases: one in the workplace surface (Microsoft), one on the compute layer (Nvidia) and then a collaboration player combined with cloud-native AI services (Google).
Research and Markets’ market share estimates in the high single digits for each leader — Microsoft at 8–10%, Nvidia at 7–9% and Google at 6–8% — indicate complementary strengths, rather than a winner-take-all dynamic. Meanwhile, analysts such as Gartner have placed AI agents on their innovation roadmaps and put an exclamation point on enterprise interest in systems that can accurately perform multi-step tasks.
Microsoft: Copilot as the Work Operating Layer
For Microsoft, that is an advantage, for the company’s strengths align closely with our daily workflows. Copilot is integrated into Microsoft 365, Dynamics, GitHub and Azure to enable agents to draft content, update records, recap meetings and take action without users switching tools. That adjacency is what makes orchestration even possible: the agent is right there with the work, permissions and governance already established.
Under the hood, Azure OpenAI Service connects customers to forward-edge models with enterprise controls, and Safety Kernel serves as a blueprint for planning, tool use and memory — critical functions for multi-step autonomy. For software teams, GitHub Copilot Enterprise adds repository-aware assistance and secure code suggestions that can be chained into CI/CD pipelines.
The resulting system represents a plausible stepping stone from simple copilots to task-specific autonomy: begin with embedded help within Outlook or Teams, then move on to agents that resolve conflicts with CRM entries or triage service tickets. Research and Markets lists the breadth of integration as one of Microsoft’s central advantages.
Nvidia: The compute engine for agentic AI agents
Heavy lifting is needed somewhere by every capable agent — training custom models, performing large-context inference or serving tool-using pipelines at low latency. Nvidia’s GPUs, software stack and networking still constitute what is considered the reference architecture for that job. From CUDA and TensorRT to Triton Inference Server and DGX systems, the company’s product stack in different stages of a model lifecycle (experimentation > production) reflects that range.
As businesses transition from pilot to scale, at-scale efficiency is what counts: batching, quantization and multi-node scheduling can slash your inference cost by orders of magnitude. Nvidia’s strategy of end-to-end optimization — hardware, compiled runtimes, and microservices for model serving — has kept it the go-to for architects of agent backends across industries: think logistics planners, autonomous customer-service workflows.
Research and Markets attributes Nvidia’s share to that end-to-end performance story, and its role in enabling a new wave of autonomous systems — smarter chat but agents observing, deciding and acting across operational systems.
Google: Gemini woven into collaboration workflows
Google’s sweet spot is combining AI with real-time collaboration and cloud-native data. It is woven into Gmail, Docs and Meet while being surfaced via Google Cloud for developers creating domain-specific agents. That combination will enable its customers to get their starts where their information exists — in documents, messages and knowledge bases — before moving to Vertex AI for governance, monitoring and bespoke tooling.
For builders, Vertex AI’s model garden, evaluation tooling and agent-building services ease the transition from demo to production. For end users, Workspace integrations allow agents to draft, route and summarize in familiar UIs while surfacing cloud data securely. And the report attributes this growth to Google’s “close to content” design.
And critically, Google’s approach gives primacy to context: the grounding in enterprise data and a commitment to provenance and permissions. These ingredients are essential as agents take actions — making artifacts, updating records or calling APIs — in the context of a collaborative environment.
What this means for buyers adopting agentic AI
Plan to put together, not just purchase. One frequent stack is productivity-layer agents (from Microsoft or Google), GPU-backed inference and fine-tuning (using infrastructure from Nvidia), and an orchestration layer for planning, tool use and guardrails. Interoperability — whether through APIs, event buses or identity — will decide whether agents remain useful assistants or get promoted to trusted digital coworkers.
Three tactical tricks to speed time-to-value:
- Choose high-signal use cases with clear KPIs (invoice exception handling, knowledge retrieval, sales email drafting).
- Enforce governance through known standards such as the NIST AI Risk Management Framework.
- Instrument all the things — latency; cost per task; error classes; human-in-the-loop outcomes.
Finally, plan for portability. Decouple prompts, tools and memory abstractions from individual models or vendors. Anchor the outputs in your data with retrieval-augmented generation. And keep an “exit path” for core parts so you can switch models or infrastructure as pricing, performance and policy landscapes change.
Bottom line: Agentic AI is transitioning from concept to capability, and the gravity well that is Microsoft, Nvidia and Google will influence most enterprise deployments. “The things that win will be the quiet agents that run and audit and improve your most critical workflows at scale,” he predicted; the winners won’t be the flashiest demos.