Something remarkable happened in the first two weeks of February 2026. Not one announcement, but a convergence — four of the most significant enterprise AI platforms launched within days of each other, and together they signal the clearest shift in enterprise technology since the cloud revolution.
On February 5, OpenAI launched Frontier — an end-to-end enterprise platform for building, deploying, and managing AI agents as "digital coworkers." The same day, Anthropic released Claude Opus 4.6 with Agent Teams — the ability to orchestrate multiple AI agents working in parallel across complex tasks. On February 10, Cisco unveiled AgenticOps at Cisco Live EMEA, an agent-first IT operating model built across networking, security, and observability. And this week, Coveo launched its hosted MCP Server, becoming one of the first enterprise platforms to offer production-ready Model Context Protocol infrastructure for connecting AI agents to enterprise data at scale.
These aren't competing announcements. They're interlocking pieces of the same transformation: enterprise AI is graduating from isolated tools and experiments into integrated, production-grade platforms that connect to the systems businesses actually run on.
For enterprise leaders, this is the moment the conversation shifts — from "should we try AI?" to "which platform architecture gives us the strongest foundation?"
What Actually Launched — And Why It Matters Together
Each of these announcements would be significant on its own. Together, they define the enterprise AI platform stack for 2026:
OpenAI Frontier: The Agent Operating System
OpenAI's Frontier is designed to be the enterprise layer where AI agents live, work, and improve. It connects siloed data warehouses, CRM systems, ticketing tools, and internal applications into a shared business context that every agent can reference. Agents get identity, permissions, and guardrails — just like employees. They learn through onboarding and feedback loops, improving over time.
The results from early adopters are striking. A major manufacturer reduced production optimisation work from six weeks to one day. A global investment company freed up over 90% more time for salespeople to spend with customers. An energy producer increased output by up to 5%, adding over a billion in additional revenue.
OpenAI's Denise Dresser, Chief Revenue Officer, captured the positioning: "What's really missing still for most companies is just a simple way to unleash the power of agents as teammates that can operate inside the business without the need to rework everything underneath."
Frontier is initially available to organisations including Uber, State Farm, Intuit, HP, Oracle, and Thermo Fisher Scientific, with broader availability planned for the coming months.
Anthropic Claude Opus 4.6: Agent Teams and the Knowledge Work Shift
Anthropic's release goes beyond a model upgrade. Opus 4.6 introduces Agent Teams — the ability to split complex tasks across multiple AI agents working in parallel, each owning its piece and coordinating directly with the others. Instead of a single agent processing tasks sequentially, you now have coordinated teams that operate the way high-performing human teams do.
The model itself sets new benchmarks. Opus 4.6 achieves the highest score on Terminal-Bench 2.0 for agentic coding, leads all frontier models on Humanity's Last Exam for complex reasoning, and outperforms GPT-5.2 by 144 Elo points on GDPval-AA — a benchmark for economically valuable knowledge work in finance, legal, and other professional domains. The 1-million-token context window means agents can process up to 1,500 pages of text, 30,000 lines of code, or over an hour of video in a single session.
Scott White, Anthropic's Head of Product, described the shift: "We are now transitioning almost into vibe working — where the model can handle real significant work, not just answer questions." Enterprise adoption data from Andreessen Horowitz confirms the trajectory: Anthropic's share of enterprise production deployments has surged from near-zero in March 2024 to 44% by January 2026. Average enterprise spending on large language models reached $7 million in 2025, with projections of $11.6 million in 2026.
Cisco AgenticOps: The Infrastructure to Run It All
While OpenAI and Anthropic build the agent layer, Cisco is building the infrastructure to run agents securely at scale. AgenticOps — unveiled at Cisco Live EMEA in Amsterdam — is an agent-first IT operating model that uses cross-domain telemetry from Cisco's networking, security, and observability platforms (including Splunk) to detect issues, perform contextual analysis, and propose remediation across entire enterprise environments.
Combined with the AI Defense platform's new MCP Catalog, AI Bill of Materials, and real-time agentic guardrails, Cisco is addressing the critical question every enterprise must answer before deploying agents at scale: how do you secure, govern, and operate AI systems that interact autonomously with your most sensitive business data?
MCP: The Standard That Connects Everything
The Model Context Protocol — originally introduced by Anthropic in late 2024 — has become the de facto standard for connecting AI agents to enterprise systems. OpenAI, Google DeepMind, Microsoft, and Red Hat have all adopted it. Anthropic donated it to the Linux Foundation's Agentic AI Foundation in December 2025. By 2026, 75% of gateway vendors are expected to integrate MCP features.
Coveo's hosted MCP Server launch this week is a practical milestone. It enables organisations to connect enterprise data sources to models like ChatGPT Enterprise and Claude via a standardised, production-ready protocol — without custom integrations or additional infrastructure. Coveo CEO Laurent Simoneau put it clearly: "We are already working closely with 10 customers leveraging our hosted MCP server to enhance leading models such as Anthropic's Claude and OpenAI's ChatGPT."
MCP solves the integration problem that has stalled most enterprise AI deployments. Before MCP, every AI-to-system connection required custom development. With MCP, developers build once and connect to any AI model — the same way USB-C standardised device connectivity.
Why This Convergence Matters More Than Any Single Launch
The significance isn't any individual platform. It's what they mean together.
For the first time, enterprises have access to a complete, interoperable AI platform stack: agent execution environments (Frontier, Claude Cowork), multi-agent orchestration (Agent Teams), infrastructure operations (AgenticOps), security and governance (AI Defense, MCP Catalog), and standardised connectivity to enterprise data (MCP).
This stack didn't exist six months ago. It's now available, from production-ready platforms backed by the largest AI companies on the planet, accessible across AWS, Azure, and Google Cloud.
Three implications for enterprise leaders:
The pilot-to-production gap is closing. The number one reason enterprise AI pilots fail isn't model capability — it's the inability to connect AI to real business systems and data. MCP standardisation, Frontier's shared business context, and Cisco's AgenticOps together address exactly this gap. Enterprises that struggled to move beyond proof-of-concept now have platform-level solutions designed specifically for production deployment.
Multi-agent workflows are production-ready. Anthropic's Agent Teams and OpenAI's Frontier both enable scenarios where multiple AI agents collaborate on complex tasks — one handling research, another analysing data, a third producing outputs — all coordinated in parallel. This is a fundamental shift from "one chatbot answers questions" to "a team of AI agents executes work." For enterprises processing high volumes of documents, managing complex customer interactions, or coordinating workflows across departments, multi-agent capability is the unlock.
Model-agnostic architecture is now the default. OpenAI's Frontier explicitly supports agents from multiple vendors, including Anthropic and Google. MCP works across every major model provider. Cisco's security and governance tools are model-independent. The platform era reinforces what the smartest enterprises already know: the winning strategy isn't picking one AI vendor — it's building infrastructure that lets you use the best model for each task, and switch as the landscape evolves.
Engineering Perspective: What This Means for Enterprise Architecture
— Lynt-X Engineering, AI Research Team
From an engineering standpoint, the convergence of these platforms creates a new reference architecture for enterprise AI that we're already seeing in production deployments:
The connectivity layer is now standardised. MCP eliminates the custom integration bottleneck that has stalled most enterprise AI projects. With MCP servers available for CRM, ERP, data warehouses, and document management systems, AI agents can access enterprise data through a single protocol rather than dozens of bespoke API integrations. This reduces deployment time from months to weeks.
Agent orchestration is no longer experimental. Anthropic's Agent Teams and OpenAI's Frontier execution environment both provide production-grade multi-agent coordination. In our implementations, we're deploying agent teams where one agent handles document extraction, another validates data against business rules, and a third generates outputs — all running in parallel with coordinated handoffs. The throughput improvement over sequential processing is substantial.
Security must be agent-native. Cisco's AI Defense suite — particularly the MCP Catalog and AI Bill of Materials — represents the kind of security infrastructure that must be in place before agents access sensitive enterprise data. We recommend every enterprise deploying AI agents implement an agent identity framework, explicit permission boundaries, and continuous monitoring of agent interactions before going to production.
The practical recommendation: start with MCP. Connect your highest-value data sources to a standardised MCP server. Deploy a focused agent workflow against a specific, measurable business task — document processing, data validation, customer query routing. Validate in production. Then scale horizontally by adding more MCP connections and more specialised agents to the same architecture.
The platform infrastructure is now available. The engineering path from pilot to production has never been clearer.
"For the first time, enterprises have access to a complete AI platform stack — from agent execution to multi-agent orchestration to standardised connectivity to enterprise data. The era of isolated AI experiments is over. The era of AI platforms has begun."
What to Do This Week
The platform era creates specific, immediate opportunities:
Evaluate your integration readiness. MCP is the standard. Assess which of your enterprise systems — CRM, ERP, document management, data warehouses — have MCP servers available or can be connected through existing MCP infrastructure. This is the foundation every AI agent deployment will build on.
Map your first multi-agent workflow. Identify a process in your business that involves multiple steps across multiple systems — invoice processing, customer onboarding, compliance review, sales pipeline management. Design a multi-agent workflow where specialised agents handle each step in parallel. The platforms to execute this are now available.
Engage the competition. OpenAI and Anthropic are both aggressively courting enterprise customers. Both offer forward-deployed engineering support, pilot programmes, and increasingly competitive pricing. Frontier works with agents from multiple vendors. Claude Opus 4.6 is available across every major cloud. The smart play is to run comparative pilots on the same use case, measure results, and build model-agnostic architecture that captures the best of both.
The Platform Era
There's a pattern in enterprise technology. First come the tools — individual products that solve individual problems. Then come the platforms — integrated systems that connect those tools into something greater than the sum of their parts. Then come the ecosystems — communities of builders, vendors, and enterprises that create compounding value on top of those platforms.
Enterprise AI just entered the platform era. The tools phase — individual chatbots, standalone copilots, one-off automations — delivered value, but it was fragmented. The platform phase — integrated agent environments, standardised connectivity, multi-agent orchestration, enterprise-grade security — delivers compounding value.
The enterprises that build on this platform architecture now will compound their advantage with every new model release, every new MCP server, every new agent capability that becomes available. The infrastructure is ready. The standards are converging. The platforms are live.
The question isn't whether to build on AI platforms. It's how quickly you can start.