Agentic AI in Big Tech and Enterprise

*Disclaimer - this post was rewritten with AI based of my brain dump. Yet, I find it inspirational and useful. A firsthand experience from a guy who runs Research & Development teams in large enterprise companies. Let me know if I need to update my AI to get to the point shorter :D* # A Longread For context, I manage enterprise software development in Life Sciences. Around 50 engineers across several projects for massive companies. The kind with 100k+ employees, billions in revenue, endless compliance requirements, and layers of process nobody fully understands anymore. What’s happening inside these companies right now is interesting. Top management split into two groups: people who understand what AI is doing, and people who think they understand what AI is doing. Both groups look at the same layoffs and productivity reports and come to completely different conclusions. The reality is that most giant enterprises were already heavily overstaffed long before AI. Too many parallel initiatives, too much legacy software nobody wants to touch, entire departments preserving systems that stopped generating meaningful revenue years ago. So companies cut overhead, free up millions, and redirect that money into AI transformation initiatives. The problem is that a lot of executives now think smaller teams plus AI automatically means 20-30% productivity gains. In practice, when you actually assess these teams internally, the gains usually come from removing coordination overhead. Fewer people means fewer meetings, fewer collisions, less idle time, less approval paralysis. That improvement could have happened without AI. Yes, some engineers genuinely became 2-3x faster. But something funny happens after that. Once people finish their normal work faster, they start doing all the things they used to neglect because there was never enough time. Better documentation. Better testing. Refactoring. Validation. Cleanup. So overall throughput barely changes. Dashboards wiggle around a few percent and leadership starts hallucinating revolutions from noise. I’ve spent the last year helping teams adopt Claude, Codex, Cursor, agents, all of it. The biggest surprise is how few people actually understand what these tools are. Giving Claude to an average employee is like giving a smartphone to a child. They press buttons for a bit, get bored, then go back to basics. Give the same device to a good entrepreneur or trader and suddenly entire businesses appear from thin air. Most enterprise AI adoption is failing because companies never demonstrate real workflows. Every AI townhall is the same: "Productivity increased here" "Claude helped there" "Cursor accelerated development" But nobody actually shows HOW. Nobody walks people through real examples step by step. Employees leave those meetings thinking: "Cool story. Could’ve been an email." Recently I showed a group of business consultants how to take Claude, drop it into a folder with their consulting proposal, and turn it into a multi-stage research and validation pipeline. Extract claims. Research supporting evidence. Find contradictions. Run another validation pass. Rebuild the migration proposal with new findings. The whole thing was driven by 3 markdown files and one long instruction prompt. Their minds were blown. Then I checked back a week later. Nobody was using it. Too much reading. Too much setup. Existing workflow felt comfortable enough. Software development is even worse. Some AI enthusiasts are shipping their 20th side project with Cursor and now think enterprise engineers are idiots because they can’t deliver major regulated features in two weeks. These people still don’t understand where enterprise development time actually goes. Writing code was never the bottleneck. The hard part is architecture. Stable abstractions. Cross-team alignment. Compliance. Validation. Testing. Long-term maintainability. That’s where months disappear. I pushed hard into agent workflows myself. BMAD, multi-agent pipelines, architecture-driven prompts, all of it. After a few weeks it became obvious: even top-tier models constantly fail to follow enterprise architecture correctly. The code works. Until it doesn’t. One out of ten approaches produces something solid. The other nine turn into endless regeneration loops, partial rewrites, rollback commits, and prompt archaeology trying to convince the model to think like the engineer wanted in the first place. Meanwhile upper management is panic-drinking whiskey while demanding AI transformation because they built a landing page in Lovable during lunch. Any pushback gets interpreted as resistance, incompetence, or sabotage. The disconnect between executives and engineering has honestly never been this bad. Now here’s the uncomfortable part: AI absolutely CAN accelerate development 2-10x. But only if you accept the tradeoff. Current agents are not producing enterprise-grade maintainable systems consistently. So the only way to fully exploit them is to stop treating code quality as sacred. Engineers hate hearing this. But if you want maximum speed, you stop reviewing every line manually and start building systems around validation instead. Benchmarks. Tests. Sub-agents reviewing architecture. Automated verification loops. If the code passes benchmarks and doesn’t explode in production, management usually doesn’t care how elegant it is. That’s the real shift happening right now. Not AI replacing engineers. AI replacing the importance of clean human-readable implementation details in certain product categories. The question becomes: Do you want fast and risky, or slow and reliable? For some products, speed matters more than maintainability. Especially when validating a business hypothesis quickly. Would I build aircraft autopilot software this way? Obviously not. Would I build a messy enterprise data aggregation platform this way? Absolutely. Half those systems already produce questionable data even with fully human teams anyway. Humanity spent decades building gigantic enterprise spaghetti factories and now acts shocked when probabilistic machines produce spaghetti faster. Incredible species. One more thing nobody talks about: Enterprise AI coding is already expensive. Real multi-agent development workflows easily burn $20-100/hour in tokens. 10-40 million tokens per hour is becoming normal once you add context, validation, sub-agents, SDLC flows, and verification loops. But economically it still makes sense. A US software engineer can easily cost a company \~$200k/year fully loaded. Right now I have a tiny 2-person AI-heavy team costing roughly: * $32k/month engineering cost * $4-5k/month token spend And they perform roughly like a traditional 5 person team that would cost closer to $80k/month. So yes, the savings are real. But they come with risk: technical debt, maintainability collapse, and the possibility of catastrophic future rewrites. Management needs to consciously choose that tradeoff instead of pretending AI somehow removed it.