Today, Samsung launched the Galaxy S26. The hardware upgrades are incremental. The AI architecture is not.
For the first time, a mainstream consumer device ships with three distinct AI agents operating at the system level: Google Gemini, Perplexity (activated by saying “Hey Plex”), and a redesigned Bixby. Each handles different tasks. Each is embedded across native apps — Notes, Calendar, Gallery, Reminders. Each can be summoned independently. Users choose the right agent for the right task instead of being locked into a single AI provider.
Samsung says nearly 8 in 10 users now rely on more than two AI agents daily. The company's response isn't to pick a winner. It's to build a multi-agent ecosystem where the intelligence layer is interchangeable and the device layer is the constant.
This isn't a phone story. It's an architecture story. And it has direct implications for how enterprises should be building their AI infrastructure right now.
Why Samsung's Multi-Agent Bet Matters Beyond Phones
Samsung could have gone exclusive with Google. Gemini is already deeply integrated into Android. Instead, Samsung added Perplexity at the OS framework level — powered by Perplexity's Sonar API, built into the operating system, not just installed as an app. Bixby was redesigned as a conversational device agent handling settings, on-device tasks, and local operations.
The result is a phone where each agent has a clear role:
Gemini handles general intelligence — reasoning, research, creative tasks, deep integration with Google's ecosystem.
Perplexity handles real-time search and information retrieval — up-to-date answers, citations, research across the web, integrated into Notes and Calendar for context-aware assistance.
Bixby handles device control — phone settings, on-device actions, local operations that don't need cloud processing.
This is model-agnostic architecture at the consumer level. No single AI provider controls the experience. Each agent is selected for what it does best. The orchestration layer — Samsung's One UI 8.5 — manages the interactions and lets users switch between agents seamlessly.
Samsung also revealed on-device image generation in under a second through a feature reportedly called EdgeFusion — entirely local processing, no cloud required. Combined with a new Privacy Display that limits screen visibility to nearby observers, the message is clear: AI that runs locally, with user control, across multiple providers.
The enterprise parallel is exact. The best AI deployments don't depend on a single model or a single provider. They use different models for different tasks, orchestrated through an infrastructure layer that manages switching, governance, and data flow. What Samsung is doing for 800 million mobile devices is what enterprises should be doing for their operations.
The Pentagon Just Proved What Happens Without Multi-Agent Architecture
Hours before Samsung's announcement, the consequences of single-provider AI dependence played out in the starkest terms possible.
Defense Secretary Pete Hegseth met Anthropic CEO Dario Amodei at the Pentagon this morning and delivered a Friday deadline: agree to let the military use Claude for “all lawful purposes” — or face contract termination, a supply chain risk designation, and potential invocation of the Defense Production Act to force compliance.
The meeting was, by all accounts, tense. A senior Defense official called it “not warm and fuzzy at all.” Hegseth told Amodei that when the government buys Boeing planes, Boeing doesn't dictate how they're used — and the same should apply to Claude. Amodei reiterated Anthropic's position: the company supports national security use cases but won't remove safeguards against mass domestic surveillance and fully autonomous weapons.
Anthropic has no plans to budge. The Pentagon has set a Friday 5:01 PM deadline.
Here's the critical detail: Claude is the only AI model currently deployed on the military's classified networks. The Pentagon's $200 million contract with Anthropic represents deep, single-provider dependence — and it's now a crisis. Pentagon officials acknowledged that replacing Claude would be an “enormous pain” because “they are that good.” Competing models are described as “just behind” for specialised government applications.
Meanwhile, xAI — Elon Musk's AI company — has been approved to deploy its Grok model in classified settings with no usage restrictions. OpenAI and Google have already agreed to remove safeguards for unclassified military systems. The Pentagon is actively building multi-provider AI capability — precisely because single-provider dependence gave Anthropic leverage that officials now find unacceptable.
The lesson isn't about who's right in the Pentagon-Anthropic dispute. It's about architecture. Any organisation — military or commercial — that depends entirely on a single AI provider for mission-critical operations has created a vulnerability that goes beyond pricing and features. It's a governance vulnerability, a continuity vulnerability, and a negotiating vulnerability.
Multi-Agent Is Now the Default Architecture
Two events on the same day. Samsung ships three AI agents on a phone because consumers need flexibility. The Pentagon scrambles to add AI providers because single-provider dependence creates unacceptable risk.
The pattern is consistent across every layer of the technology stack:
Consumer devices: Samsung Galaxy S26 with Perplexity + Gemini + Bixby. Motorola shipping Moto AI alongside Perplexity and Gemini. Apple integrating multiple AI models in its ecosystem. No major device manufacturer is betting on a single AI provider.
Enterprise platforms: Anthropic's Model Context Protocol (MCP) — now under the Linux Foundation's Agentic AI Foundation — lets AI agents connect to external tools regardless of which model powers them. OpenAI, Microsoft, and Google have all embraced MCP. The connective layer is becoming model-neutral.
Cloud infrastructure: AWS Bedrock, Azure AI, and Google Cloud Vertex AI all offer multi-model access. The cloud providers themselves are building orchestration layers that support switching between foundation models.
Defence and government: The Pentagon awarding parallel $200 million contracts to Anthropic, OpenAI, Google, and xAI — and now rushing to reduce dependence on any single one.
The architecture that started as an enterprise best practice has become the universal standard. Multi-agent isn't optional. It's table stakes.
What This Means for Enterprise AI Deployment
For enterprises building or scaling AI operations, today's events crystallise three practical requirements.
1. Build the Orchestration Layer, Not Just the Agent
Samsung's Galaxy AI isn't powerful because it has Perplexity. It's powerful because it has an orchestration layer (One UI 8.5) that routes tasks to the right agent, manages context across apps, and lets users switch without friction.
Enterprise AI works the same way. An AI agent that automates invoice processing is useful. An orchestration layer that routes invoices to the best extraction model, escalates exceptions to a human reviewer, logs every decision for audit compliance, and switches models when a better option becomes available — that's infrastructure.
This is the architecture behind our Minnato platform: AI agents that deploy in minutes and scale across enterprise operations, with the orchestration intelligence to use the right model for each task. The agents are interchangeable. The orchestration layer is the moat.
2. Treat Provider Flexibility as a Governance Requirement
The Pentagon-Anthropic dispute makes the governance dimension impossible to ignore. Anthropic's safety position — no mass surveillance, no autonomous weapons — is a usage policy. Every AI provider has usage policies. Those policies can change when leadership changes, when commercial pressures shift, or when regulatory frameworks evolve.
If your enterprise AI infrastructure depends on a single provider's usage policies remaining stable, you have a governance gap. The solution isn't to pick the most permissive provider — it's to build architecture that works across providers, so no single provider's policy decisions create operational risk.
For voice AI, this means systems like Dewply that can process customer conversations using the best available speech and language models without being locked into a single provider's terms. For document intelligence, it means systems like Vult that extract data from invoices, contracts, and forms regardless of which underlying model powers the extraction — and can switch when better options emerge.
3. Start Thinking About Agent Governance Now
Samsung's multi-agent phone surfaces a question that enterprises will face at much larger scale: when multiple AI agents operate across your systems, who governs what each agent can do?
On a phone, the stakes are manageable — a wrong calendar reminder, a misinterpreted search query. In enterprise operations, the stakes are higher. An AI agent that processes contracts needs different permissions than one that handles customer conversations. An agent that accesses financial data needs different audit requirements than one that manages scheduling.
Multi-agent enterprise AI requires agent-level governance: what data each agent can access, what decisions each agent can make autonomously, what audit trails each agent generates, and what escalation paths exist when an agent encounters something outside its boundaries. The enterprises that define these governance frameworks now — before multi-agent deployments scale — avoid the retrofitting costs that will hit everyone else later.
The Convergence Point
A $1,200 phone and a $200 million defence contract arrived at the same conclusion today: multi-agent AI architecture is the only approach that scales.
Samsung proved it from the consumer side — flexibility, choice, and the best agent for each task. The Pentagon proved it from the infrastructure side — single-provider dependence creates risk that no amount of capability can offset.
For enterprises, the implications are practical and immediate. Build orchestration layers that manage multiple AI agents. Design governance frameworks that control what each agent can do. Architect infrastructure that can switch providers without operational disruption.
The phone in your pocket now runs three AI agents. The question for enterprise leaders: does your business?