Paul Dakessian

Agentic AI - The Next Engineering Frontier The shift from reactive AI to proactive agents is accelerating faster than most anticipated. We're moving beyond single-task models to autonomous systems that can plan, execute, and adapt—fundamentally changing how we architect intelligent systems. Key trends shaping 2025: - Multi-agent orchestration replacing monolithic deployments - Tool-calling capabilities becoming standard across LLMs - Reasoning chains enabling complex problem decomposition - Human-AI collaboration patterns emerging as the sweet spot The roadmap ahead involves solving coordination complexity, establishing safety guardrails, and building robust evaluation frameworks. Benchmarks like GAIA remind us how challenging true general intelligence remains—even for seasoned engineers. As we scale these systems, the focus shifts from "can we build it?" to "how do we govern it responsibly?" The engineering challenges are immense, but so is the potential impact. Recently completed Hugging Face excellent agentic AI course—their practical approach to complex concepts is impressive. The hands-on framework really crystallizes the architectural patterns we're seeing in production #AgenticAI #Engineering #MachineLearning #TechLeadership #AIStrategy

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Dashboards don’t create accountability. They create meetings. Charts get shared. Everyone nods. Action items get “noted”. Same problems. Next quarter. Accountability doesn’t come from visibility. It comes from systems — where ownership is clear and decisions can’t be ignored. I wrote a short note on why dashboards feel productive but rarely change behaviour — and what actually does. If your org has great dashboards but the same issues every review… this will sound familiar. Dashboards Don’t Create Accountability — Systems Do: https://lnkd.in/gCGpm-VU

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If you’re a CEO/founder or PE operator and your tech org is: ❌ Shipping late ❌ Stuck in reactive mode ❌ Struggling through the Series A/B jump, or post-acquisition integration or after a major release It’s not “a delivery problem.” It’s an operating model problem. At the inflection point, the old playbook breaks: what worked at 10 engineers fails at 40… and breaks again across multiple products/teams. That’s where I come in as a fractional CTO: - Spot platform and delivery risks before they hit revenue and retention - Rebuild execution rituals (prioritization, ownership, release confidence) without derailing the roadmap - Align architecture + team structure to your value-creation plan (growth, margin, or exit timeline) If you’re feeling the strain of growth, I can help you turn “busy and reactive” into predictable shipping, and a platform that scales. #CTO #SaaS #StartupScale #PrivateEquity #TechStrategy

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Scaling isn’t just about adding headcount. It’s about focus, structure, and building systems that can handle real complexity without unnecessary overhead. On Domain Name Wire, Unstoppable CEO and founder, Matt Gould, shares how we think about growth, engineering priorities, and staying lean while building serious infrastructure.

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Everyone's talking about AI coding agents transforming productivity. Fewer are talking about what needs to be true before you can safely let them loose on your codebase. If you're working in a brownfield environment (and most of us are), the path to AI-assisted development runs through platform thinking. You don't need a dedicated platform team, but you do need platform outcomes. Skip this step and the problems stack up fast 👇 🚨 Regressions sneak in. Without solid test coverage, AI agents iterate fast and break things faster. 🚨 Manual promotion from test to production becomes a bottleneck at best, a liability at worst. 🚨 Quality issues that should be caught automatically slip through because there's nothing systematic in place to stop them. Get the foundation right and everything shifts 👇 ✅ Testing at multiple layers. Unit tests, integration tests, all acting as a safety net that catches regressions before they compound. ✅ CI checks that leave nothing to chance. Linting, formatting, type checking, security scans, dependency management. All automated, all enforced. ✅ Quality gates with teeth. Tools like SonarQube or ArchUnit mechanically enforce whatever standards you encode. That matters, because even the best models today can't match that kind of consistency. Static enforcement fills the gap. ✅ Observable deployments. Logging, tracing, monitoring. When AI-generated code introduces subtle issues, you need visibility to catch them fast. ✅ Automated rollback capabilities. Fast iteration needs a fast undo. If something slips through, you're back to a known good state in minutes, not hours. ✅ Standardized branch and merge workflows. When your platform covers this, incorporating agentic code generation becomes straightforward. Prompts become specs that produce PRs. Agents propose, humans review and merge. Your team experiments with AI-generated solutions confidently because the platform catches what slips through. The feedback loop tightens. Adoption accelerates. So where do you start? A mature platform is a multi-quarter investment. But you don't need the full picture to begin. Phase one is about building enough guardrails to experiment safely: ☑️ Pick 2-3 critical flows and add integration tests ☑️ Introduce a CI pipeline with quality checks ☑️ Look into dependency scanning if you're not doing it already ☑️ Limit agent permissions to repo-only, no prod access ☑️ Start with "agent proposes, human approves" From there, the roadmap expands: standardized dev environments, self-service infrastructure, developer portals, golden paths. Each layer of maturity unlocks more autonomy for your agents. Think of Karpathy's autonomy slider. You're not jumping straight to autopilot. You're moving the dial deliberately, one notch at a time, as trust and guardrails mature together. The organizations gaining real productivity from vibe coding aren't the ones who jumped in fastest. They're the ones who built the foundation first. 

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Anyone who is struggling with promps or understanding how to use your LLMs effectively as a Product Manager. Check out this post by Richard Hofer.

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Today we announced new capabilities for our Shop Direct and Buy for Me experiences on Amazon, enabling now feeds to facilitate external merchants to list reliably their product listings and reach millions of Amazon customers. Excited on this feature, that is a win/win experience for customers, merchants and brands. Excited to be part of such important project.

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Great insights Sanish.  What stands out is the fact that over 70% of customers are already using AI in production – that’s not experimentation, that’s real enterprise execution. Many organizations struggle to move from AI pilots to scaled adoption, and this post clearly highlights the importance of practical strategy, operational readiness, and measurable ROI. Exciting to see teams proving that AI can deliver real value today, not just tomorrow. Congratulations to the Legion team – looking forward to seeing new heights this year!

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