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Deploying Agentic AI at Scale: Infrastructure, Reliability, and Risk with Ran Aroussi

Ran Aroussi · February 16, 2026

Author · Entrepreneur · Artificial Intelligence · Leadership · Finance · Technology · Public Speakerin

Interview with Ran Aroussi

Deploying Agentic AI at Scale: Infrastructure, Reliability, and Risk with Ran Aroussi

Podcast: Business of Tech: Daily 10-Minute IT Services Insights

February 16, 2026 · 23 min

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Deploying Agentic AI at Scale: Infrastructure, Reliability, and Risk with Ran Aroussi

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Key topicsBeyond the Chatbot: How AI Can Turn a Business Into a Self-Driving OrganisationMoves the conversation beyond ChatGPT and individual productivity toward AI operating at the organisational level. Ran can explain his vision of businesses where work naturally moves forward: proposals are aggregated, tasks progress, people are nudged when action is needed and drafts are created without someone having to constantly tell an AI what to do.Load Bearing — Why Production AI Agents Need a Layer That Takes the WeightRan Aroussi has built the layers other people's software runs on for 30 years, from ad engines serving 3 billion ads a day to yfinance, now installed more than 30 million times a month. Most teams treat agents as a model problem and reach for another framework, when the thing that breaks in production is everything underneath: memory, orchestration, audit trails, failure handling. He walks through what an agent-native layer has to do before autonomy is safe to ship, and why he wrote Production-Ready Agentic AI and built MUXI around declarative, inspectable configs. Listeners leave able to tell which parts of their agent stack survive real traffic, and which fail quietly.Plumbing — Why AI Money Ends Up With Whoever Owns the PipesRan Aroussi has spent 30 years building the parts of software nobody sees, from ad engines serving 3 billion ads a day to yfinance, downloaded more than 30 million times a month. Most of the attention in AI goes to the models and the apps sitting on top of them, and he argues the durable returns land a level below, the way operating systems, cloud, and DevOps each pulled the value down to themselves. He walks through what that means for agents in practice: who controls memory, orchestration, observability, and the audit trail when a run goes wrong, and why he wrote Production-Ready Agentic AI and built MUXI on open, declarative configs rather than closed tooling. Listeners leave able to work out which part of their own agent stack they actually own, and which part someone else will end up charging them for.View all topics →

About Ran Aroussi

Founder of VarOps • Building software and Resident AI into companies that can't afford to guess.

35+ years Production coding experience30M+ Open-source downloads per month50K+ GitHub stars3B+ Ads delivered daily by systems he built

Ran Aroussi has spent 35 years building software infrastructure. Now he thinks we’re building AI for businesses backwards.

Most companies are adding copilots, chatbots, and agents, then asking employees to learn how to use them. Ran’s argument is almost the opposite: people shouldn’t have to adapt to AI. AI should adapt to the company.

He’s the founder of VarOps, where he’s building what he calls "Resident AI": AI that lives inside an organization, learns how it actually operates, and works through the tools and workflows people already use. No new destination. No constant prompting. Ideally, employees barely notice it’s there.

The idea grew out of a problem Ran believes the AI industry has underestimated: AI doesn’t understand organizations.

A chatbot might have access to every document, meeting, and message in a company and still not understand why a decision was made, which unwritten rule matters, who actually knows how something works, or that the official process hasn’t been followed in three years.

Retrieval gives AI information. It doesn’t necessarily give it understanding.

Ran’s work focuses on building that missing layer: a living model of the organization itself – its knowledge, decisions, relationships, processes, and unwritten operating context. He describes it as an "Organizational Language Model" (OLM) rather than another LLM with access to company data.

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About this episode

Agentic AI is being deployed as production infrastructure in enterprise settings, but prevailing frameworks remain unreliable for mission-critical operations. Dave Sobel and Ron Aroussi from Muxie underscored that while AI agents are functional—especially in non-deterministic contexts like customer support—expectations of deterministic, workflow-based reliability are not met. The move from demonstration agents to production-scale tools brings heightened attention to issues of reliability, observability, and especially risk of vendor lock-in for Managed Service Providers (MSPs) and their clients.

Operational deployment of AI agents currently gravitates toward roles with minimal operational risk, such as customer-facing chatbots or internal chief-of-staff assistants. Aroussi explained that while such agents can automate initial support tiers and internal daily briefings, their unpredictability and potential for error limit their use in processes demanding strict oversight and accountability. He identified two core use cases—external (customer support) and internal (personalized information management)—explicitly noting that agents are best positioned to augment rather than fully automate complex workflows at this stage.

A critical risk for MSPs lies in attempting to retrofit existing software frameworks to support agents, which introduces integration complexity and increases the likelihood of operational failures. Purpose-built infrastructure for agentic AI offers better alignment between AI capabilities and production requirements, with Aroussi citing drastically reduced hallucination rates and improved oversight when using native tools. Open source is identified as a foundational element for AI development, but it incurs its own risks, particularly around third-party code quality and the long-term sustainability of community-driven projects.

The practical implication for MSPs and IT service providers is clear: a cautious, incremental adoption approach focused on low-risk use cases, coupled with rigorous controls on agent permissions and robust audit trails, is essential. Decision-makers should avoid assuming agents operate with the reliability or accountability of traditional software, prioritize operational transparency, and ensure that responsibilities for agent actions are clearly defined and enforced at the implementation level. Vendor lock-in and software provenance remain significant governance concerns as agentic AI moves from experiment to infrastructure.

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