Intuz

We built a single AI agent to handle 6 enterprise tasks. It worked in demos. It collapsed in production. Here's what failed — and the 4 architecture patterns that actually work at scale 🧵

Full breakdown with architecture deep-dives, enterprise tradeoffs, and a decision framework you can use this week 👇 pub.towardsai.net/langgraph-vs-c…

What I didn't expect going in: The *philosophy* gap between these frameworks is bigger than the feature gap. LangGraph makes you think in state machines. CrewAI makes you think in org charts. AutoGen makes you think in conversations. The wrong mental model costs you more than the wrong feature.

Microsoft just moved AutoGen to maintenance mode. If you're picking an AI agent framework for enterprise in 2026, the landscape just shifted. LangGraph vs CrewAI vs #AutoGen — full breakdown 🧵

Full breakdown with architecture diagrams, production code, communication protocols, and the complete decision framework 👇 🔓 pub.towardsai.net/multi-agent-ai… @towards_AI — appreciate you publishing this one.

Real-world result from one enterprise deployment: 15,000 tickets/day 62% resolved without human intervention (up from 41%) Avg resolution time: 47 min (down from 4.2 hrs) 💰 Cost per ticket: –38% Confident-but-wrong rate: 12% → under 3% The QA agent — the one most teams skip — delivered the highest ROI.

We built a single AI agent to handle 6 enterprise tasks. It worked in demos. It collapsed in production. Here's what failed — and the 4 architecture patterns that actually work at scale 🧵

Ouch — the "silent stale model" problem. 😬 Happens more often than anyone publishes about. The fix isn't complicated, but it has to be intentional: Model registry as a non-negotiable Version tags surfaced in every deployment log Drift monitoring so production doesn't quietly degrade We've helped teams untangle exactly this kind of chaos. If you're building out your MLOps layer, happy to share what's actually worked in the field.

@IntuzHQ Version control chaos hit us hard. Three team members running different model versions in staging, nobody set up a model registry. Production was serving a 2-month-old version before anyone noticed. MLOps isnt glamorous but it literally saves companies.

Most AI startups don’t fail because of bad models. They fail because of bad MLOps. Everyone talks about: • LLM performance • Model accuracy • Fine-tuning Almost no one talks about: • Version control chaos • Deployment bottlenecks • Monitoring blind spots

Full comparison — architecture, pricing, security, and decision matrix: medium.com/intuz/perplexi… Which approach are you betting on? 🔁 RT = Perplexity (managed) ❤️ Like = OpenClaw (open-source) 💬 Reply = Claude Code (developer-first)

The bigger picture: All 3 are converging toward the same vision — AI that takes objectives, not instructions. But the winner won't be the tool with the most models. It'll be the one that earns trust. 2026 is when "AI agent" stops being a buzzword and becomes a budget line item.

Yesterday, Perplexity launched "Personal Computer" — a 24/7 AI agent that runs on your Mac mini with 19 models. We now have 3 completely different approaches to AI agents in 2026. I compared all 3. Here's what businesses need to know: 🧵👇