Wealthsimple Blog: How AI speeds up large-scale migration

AI agents are poised to revolutionize infrastructure deployment, but what's the real deal? Dive into the details and see how AI is reshaping the tech landscape. https://buff.ly/JPjuTTT #AIRevolution #TechInnovation #FutureOfWork

AI agents are poised to revolutionize infrastructure deployment, but what's the real deal? Dive into the details and see how AI is reshaping the tech landscape. https://buff.ly/JPjuTTT #AIRevolution #TechInnovation #FutureOfWork

We all know that AI can’t deploy infrastructure, not today. This isn’t because AI can’t generate IaC, it’s because infrastructure is so much more than IaC. And the free-form, hallucinatory side of AI can't be unleashed on your critical infra. But there is a future where AI Agents can and will deploy infrastructure. It isn’t that far off, and environment orchestration can deliver that by providing agents with: - A controlled set of pre-approved options, not infinite possibilities - Clear dependency and deployment sequences; and - Standards and guardrails https://lnkd.in/dRYFjVEn

When engineers can simply chat with their infrastructure and say something like "I need a new cache cluster for staging and production" and that just happens. No ticket, no code change, no fighting for priority, it just happens.

Introducing the Nosana Deployment Manager 🧩 It’s the next step toward making decentralized compute more powerful and flexible for AI workloads. With Deployment Manager, developers can now: - Manage persistent endpoints - Scale replicas automatically - Enable load balancing - Extend or stop jobs without manual setup This release is part of our work to make Nosana easier to use for real-world AI applications (i.e. deploying models, running experiments, or managing production workloads) 🤝 Learn more about how it works and what’s coming next ↓ https://lnkd.in/dADb_uev #Deployment #developers #AI #AIworkloads #decentralizedAI

Why do your AI agents keep losing context? Because you're designing them like human teams instead of stateless processors. Here's what I learned rebuilding my FinTech AI agent system: The "limitations" in multi-agent architectures—isolation, statelessness, hub-and-spoke coordination—aren't bugs. They're features that force architectural discipline. When subagents can't communicate directly, you're forced to think precisely about information flow. When they can't persist state, you build explicit state management. When coordination must go through a hub, ownership becomes crystal clear. The payoff for financial services? Systems that are more transparent, more debuggable, and more reliable. Plus significantly lower token costs from eliminating context bloat. These patterns work across platforms—I discovered them with Claude Code, but they apply to any multi-agent system. My latest article details the hub-and-spoke patterns that emerged: https://lnkd.in/enwKBDk9 What constraints have forced better design in your AI systems? Drop a comment—I'm actively exploring this space and would value your perspective. #AIArchitecture #FinancialTechnology #EnterpriseAI #SystemDesign

The code compiled. The tests passed. Then production crashed. This is the nightmare scenario CTOs and engineering managers face when considering AI-assisted migrations. I've been deep in the world of Objective-C to Swift migrations, and there's a critical gap most teams miss: AI tools excel at syntax conversion but fundamentally misunderstand how memory management works differently between Objective-C and Swift. I've identified five systematic mistakes that emerge in AI-assisted migrations: → Nullability assumptions that break implicit contracts → Forgotten observer cleanup creating memory leaks → Misunderstood closure captures causing retain cycles → Notification patterns that introduce memory leaks → Over-aggressive @objc annotations killing performance These aren't edge cases. They're predictable patterns from how AI approaches code conversion. The solution? Let AI handle the mechanical work, but pair it with experienced code review from someone who understands both memory models. I wrote up the details with examples for each pattern: https://lnkd.in/eztqPh6P It's worth a read if you're considering migrating from Objective-C to Swift.

Anthropic just changed the game for AI developers with Agent Skills and this is a big deal for anyone building real, workflow-integrated AI systems. Agent Skills are reusable, filesystem-based modules that give Claude domain-specific expertise on demand. Think of them as plug-and-play knowledge packs such as code, context, and logic that Claude can automatically use when relevant. What makes this design stand out is its progressive disclosure architecture. It doesn’t just dump everything into context. It works in layers: ✅ Metadata – always loaded, provides lightweight context ✅ Instructions – triggered only when relevant ✅ Resources & Code – pulled in on demand for execution Here’s the twist that changes everything: Skills can include executable code that Claude can run autonomously inside a contained environment. With access to filesystem operations, bash commands, and tool execution, Claude can now perform not just respond. Why this matters: • Composable architecture: Combine skills like building blocks for complex workflows • Efficient context management: Keeps token usage minimal and focused • LLM-agnostic: Can be adapted for other models and frameworks • Reusable domain expertise: Package institutional knowledge once, scale it across teams This is what real agentic AI looks like. Modular, efficient, and directly useful for production workflows. 🧠 Blog: https://lnkd.in/gGj3huhz 💻 Repo: https://lnkd.in/gD99NCfk

I found the following article particularly interesting... Scale isn't the problem, it is complexity, because we keep adding layers of abstraction, microservices, orchestration, and AI, but not enough ways actually to observe what's going on under the hood. Observability isn't enough. We need understandability. Therefore, teams don't need to measure everything; they need to understand how systems behave without drowning in data. AI is the problem, and the solution, because AI-generated code is a black box, but AI is the only thing that's really going to be able to deal with the firehose of logs and traces and events that we're generating today. For AI systems, don't look at the guts. I/O is more important than internals, because looking inside the model yields diminishing returns, while consistent, deep monitoring of inputs and outputs in the wild is more actionable. Automation can't just be about alerting, because remediation is going to have to be a first-class capability, and handwritten runbooks won't scale, therefore, AI-assisted remediation is going to be the default. The next competitive advantage is explainability and trust, because teams that blend human intuition with the pattern recognition of AI, using the "centaur model", will understand systems faster and respond more effectively. https://lnkd.in/dBuMz6eZ

The future of AI tools won’t be new apps — it’ll be new interfaces of accountability. Anthropic’s “Agent Skills” system hints at a world where models no longer improvise — they execute defined workflows through safe, permissioned APIs. That means operations teams become AI orchestration teams — designing, approving, and auditing what an agent can do, not what it might say. We’re building the operational governance layer of AI right now, whether we realize it or not. https://lnkd.in/eyvrD7hQ