InteractiveAI @InteractiveAI_

An AI demo proves nothing about how a system holds up under real volume, real data, and real consequences. The only truth is production. Yet, enterprises still buy the illusion. Systems that perform flawlessly in a pitch collapse under volume, messy edge cases, and compliance rules. The solution is to take control of your stack. Evaluate on your terms: - On your data: Not a sanitized vendor sample. Use your real operational datasets with the noise, gaps, and long-tail anomalies. If it can't handle your mess, it can't run your business. - On your timeline: Scope pilots in weeks, not quarters. If a pilot requires a year and a custom pipeline, it’s not a pilot—it’s an architectural trap. - With your teams: Embed governance and traceability before production, not bolted on after. If you can't deploy on your data, your own timeline, and control the logic, you're probably not building an AI system ready for production. The teams winning right now aren't deploying chatbots. They are building governed, agentic systems directly into their own stack. Are you buying a demo or actually building your own AI engine? #EnterpriseAI #InteractiveAI

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The fastest way to break a production AI agent? tell it exactly what to do. Two opposite ways to get authoring wrong. Score only the final answer → the agent passes and you have no idea why. The false pass ships, and the bill lands three weeks later. Script every step → you encode every case the expert imagined, and it breaks on the first one nobody saw. Edge cases are unforeseeable by definition. Production AI works differently. You don't write the script. You set the destination and the fence, then split the work: — the expert defines the outcome and the boundaries: what's allowed, when a human takes over — the engineer builds the integrations and infrastructure — the system finds its own path inside the boundaries, measured step by step Procedure-driven automation cracks the first time reality doesn't match the script. An agent built on goals and boundaries keeps working and tells you exactly where it bent.

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That's the difference between hoping your next release is better, and knowing exactly what to fix. #AIAgents #AISystems

When each step is scored on its own, a failure points straight to where it broke. No guessing. End-to-end scoring tells you an agent failed. Step-level tells you why.

The most unreliable AI agent isn't the one that fails. It's the one that passes for the wrong reasons. The evaluation gap most teams miss with production AI agents 🧵

Where does AI actually belong in a hospitality operation? On June 4, our team will be at the Global Revenue Forum 2026 in Madrid to be part of that conversation. Vikas Bajaj, our Leader of GTM Strategy & Growth in Hospitality, will be at Espacio Maldonado for the day. He's meeting with the commercial leaders shaping the next generation of hospitality. If you are attending, reach out to him! #GRFMadrid2026 #GlobalRevenueForum #InteractiveAI #Hospitality #EnterpriseAI

Most enterprises don't have an AI strategy. Their AI vendor does. Relying on black-box "embedded AI" leaves you completely dependent on someone else's architecture. You are left with low visibility, low control over the data loop, and few ways to optimize performance. That isn't a strategy. It's an outsourced capability. True enterprise AI value comes from owning everything around the model: context, data, governance, and logic. The model itself is just a plug-and-play component. InteractiveAI was built to give you that exact autonomy. It’s not a developer toolkit or a simple router, but a complete production runtime to manage the entire AI lifecycle end-to-end. The industry's models will inevitably shift, but your business logic, context, and evaluation history remain secure inside your own boundaries. Intelligence accumulates inside your stack, not a vendor's API. Instead of buying isolated black boxes, put in place the operating layer for how your company runs AI across the board. #InteractiveAI #EnterpriseAI #AIOps #AIOrchestration #TechStrategy

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The teams that do this make sharper calls along the way. What to scale, what to kill, what to fund next. No retrofitting. And they keep getting funded. #EnterpriseAI #AIROI #InteractiveAI

The fix isn't better monitoring. It's a document, written and agreed before anyone writes code. Establishing a clear performance baseline, explicit business targets, and the instrumentation to measure the outcome before anyone writes code.

Six months into an AI project, someone from finance asks: "what is this actually doing for the business?" The team pulls up dashboards that were built to monitor cost, traces and latency, not to answer that question. Half a year running an AI system and someone from the CFO's office drops the question: "how is this moving the needle?" The team opens their observability stack — built to track errors, response times and infrastructure spend. None of that answers the actual question.

🚨 We’re hiring a Senior Full-Stack Developer (Madrid 🇪🇸 & Lisbon 🇵🇹). You will build core features across both the frontend and backend of our platform: shipping elegant interfaces, robust APIs, scalable services, and reliable tooling used by enterprise customers and internal teams. This is a hands-on, high-ownership engineering role: you will architect systems, write production-grade code, design efficient data flows, and ship end-to-end features that directly shape our product experience. #Hiring #FullStack #FS #AIJobs #MadridJobs #LisbonJobs #InteractiveAI

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Rewriting a prompt is debugging the wrong layer. Reliability lives in a dynamic context that carries your whole business logic across turns. Build your context system, the prompt with static instructions will only take you so far. #ContextEngineering #DynamicContext #PersistentContext

Your AI program is dead on arrival if the subject matter expert gets consulted once at kickoff and the engineers are left to ship the rest alone. It works the other way: experts and engineers in the same loop, every run, so a correction from the floor becomes logic the model inherits next time.

🚨 We’re hiring Forward Deployed Engineers (Madrid 🇪🇸 & Lisbon 🇵🇹). You will operate at the frontier of autonomous systems and real-world business impact. You will be deploying production-grade agentic solutions that solve critical business bottlenecks and drive measurable impact across a diverse range of industries. Your mission is to bridge the gap between abstract reasoning and production-grade reliability. You will own the end-to-end journey: from deeply understanding a customer’s unique business case and identifying technical pre-requisites to deploying our proprietary agentic framework and platform to resolve the business challenges at hand.
 #Hiring #FDE #ForwardDeployedEngineer #ML #AIJobs #MadridJobs #LisbonJobs #InteractiveAI

Production AI either compounds or quietly decays. The difference isn't the model. It's whether something is reading every trace your agent produces. One agent in production generates more traces in a day than a team can review by hand. Humans in the loop still matter but they can't be the ones reading every trace. That's where compounding stops. So you put an evaluator agent on it. It reads every trace, flags where the production agent is weakening, and proposes corrections. Drift gets caught by the system, not by a customer. The team still sets direction and approves what ships. Humans haven't left the loop — they've stopped being the rate limit on review. Intelligence reviewing intelligence, on a cadence the team controls. #InteractiveAI