{ design@tive } information design

Is belief part of being Brazilian, or just how we set ourselves up for heartbreak? Tonight, Brazil plays Japan. By the end, Brahma’s “Tá Liberado Acreditar” will feel either painfully naive — or exactly right. #advertising #football #inspiration

Users don't experience AI models. They experience the harness around them. Context, memory, tools, planning, verification, and governance determine whether AI becomes reliable, governable, and trustworthy in practice. #AIHarnessEngineering #ArtificialIntelligence #HumanAgentCenteredDesign

The greatest risk in agentic systems isn't that humans disappear. It's that they remain involved while nobody can clearly explain who was accountable for the outcome. #ArtificialIntelligence #AgenticAI #AIEthics #HumanAgentCenteredDesign

What if the biggest coordination risk in agentic systems isn't too little information, but too much? As agents summarize, prioritize, and mediate collaboration, how do teams maintain common ground instead of drifting into fragmented understanding? #AgenticAI #ArtificialIntelligence

We keep asking whether #AI can explain itself. But in agentic systems, the bigger challenge may be visibility. The issue isn't understanding decisions after they happen—it's knowing what the system is doing while work is still unfolding. Transparency matters, but awareness enables trust. #AgenticAI

#Accessibility failures in agentic systems do not always look like exclusion. More often, they look like missing disagreement, silent participants, incomplete organizational memory, or teams mistaking workflow silence for real alignment. #AgenticAI #ArtificialIntelligence #AIUX #UXforAI #Usability

Most #AI #UX discussions still treat feedback as a #usability feature. But once agents help prioritize, synthesize, and coordinate work, feedback becomes governance infrastructure. The challenge is designing adaptive systems. that remain governable without turning people into full-time supervisors.

We often discuss “trustworthy AI” as if trust is the goal. But what if systems become reliable before humans can properly inspect or intervene? The challenge may not be trust, but calibrated reliance. #AgenticAI #AI #TrustBuilding #HumanAgentCenteredDesign #TrustCalibration

Most organizations think they preserve human control because someone still approves the #workflow. Meanwhile, #orchestration systems already shape what people see, prioritize, escalate, or ignore. This article argues that control is about governable delegation — not approval screens. #AgenticAI

Most #AI UX still treats conversation as #interface polish. But to shape dialogue patterns in agentic systems is really to design #coordination, #escalation, #oversight, and organizational #trust. What happens when dialogue becomes operational infrastructure? #AgenticAI #HumanAgentCenteredDesign

Most #AI transparency discussions assume a single model explaining a single output. But what if #reasoning involves multiple layers, workflows, and #agents? The challenge might not be explainability but interrogability. #AgenticAI #ArtificialIntelligence #InteractionDesign #HumanAgentCenteredDesign

What if the most important product decisions in #AI systems no longer live in #interfaces, #PRDs, or #journeymaps? They now live inside routing logic, memory systems, escalation policies, and #orchestration pipelines. Are product teams reviewing those layers? #AgenticAI #ArtificialIntelligence

Engineers run the AI evals. But who decides what “good” actually means? If your criteria only measure what’s easy, your product will optimize for the wrong things. Should designers and PMs own eval criteria? Let’s debate. #AIEvals #ProductDesign #UX #AgenticAI #DesignLeadership #UXforAI #AIUX

We talk a lot about what AI agents should do. But who defines what they’re allowed to do? Workflow encapsulation shifts design from interfaces to boundaries and makes design leadership a governance issue. Are you ready for that shift? #DesignLeadership #AgenticAI #UXStrategy #DesignManagement #AI

#AgenticAI breaks traditional requirements. If systems interpret goals, are outcomes enough—or do we need explicit intent? Intent documentation in agentic #ProductDesign may be the missing layer. Curious how teams should adapt? #ArtificialIntelligence #DesignManagement #DesignStrategy

We can trace everything our AI systems do—but can we tell if it’s actually good? Evaluation in agentic AI is not about more metrics. It’s about defining what “good” means—and testing it continuously. Are your #evals designed—or just observed? #AgenticAI #ProductStrategy #UXStrategy #ProductDesign

We’ve focused on explainability—but is that enough? If you can explain a decision but can’t see how the system behaves over time, can you really trust it? Maybe the real gap in AI design isn’t reasoning… it’s observability. #AI #UX #ProductStrategy #HumanAgentInteraction #Observability #Metrics