Human Ai Collaboration

figmaApr 29, 20262 min readCurated summary

How to Design Agentic Tools for Work | Figma Blog

Gemini Enterprise is designed to make complex, multi-agent business workflows feel simple without hiding AI’s role. Its core principle is to keep users focused on goals while making intervention, accountability, and data context visible. The result is an agentic system that supports not only individual productivity but shared team intelligence. ## A Familiar Brand with Business-Specific Capabilities - Gemini Enterprise shares Gemini’s visual language, including the sparkle icon, gradients, rounded shapes, and motion. - Its enterprise experience emphasizes integrations with tools such as Google Workspace, Jira, and Notion. - Connectors are made prominent in the prompt experience so agents can access the business context needed to produce useful results. ## Moving Beyond Chat with the AI Inbox - Enterprise work often involves multiple tools, data sources, deadlines, and agents working simultaneously. - The AI Inbox provides a visual overview of: - Tasks agents are currently handling - Completed work - Items requiring human intervention - Deliverables awaiting review - This dashboard is intended to feel more like a team status check-in than a sequence of chat messages. ## Collaborative Projects as Shared Workspaces - Gemini Enterprise replaces isolated chat threads with persistent, shared project spaces. - AI participates as a visible team member by: - Performing tasks - Summarizing discussions - Finding project files - Answering questions about shared material - Requests are attributed to individual team members, improving accountability and helping others understand the context behind an agent’s actions. - Shared spaces reduce information silos by allowing teammates to discover and use one another’s uploaded materials. - The assistant becomes a single source of truth and a “team intelligence amplifier,” rather than merely a personal productivity tool. ## Multiple Modes of Team Interaction - Teams can communicate with AI in group chats within Collaborative Projects. - In Canvas Mode, the assistant can generate and edit documents. - These modes allow AI to remain embedded in ongoing team workflows instead of being limited to isolated prompts. Gemini Enterprise’s design recommendation is to combine powerful orchestration with clear visibility and human control. Agents should work proactively, but their actions, sources, status, and opportunities for intervention must remain understandable to the people responsible for the outcome.

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Human Ai Collaboration AI Jira Multi Agent Systems+4

googleAug 11, 2025Original article

Enabling physician-centered oversight for AMIE (opens in new tab)

Guardrailed-AMIE (g-AMIE) is a diagnostic AI framework designed to perform patient history-taking while strictly adhering to safety guardrails that prevent it from providing direct medical advice. By decoupling data collection from clinical decision-making, the system enables an asynchronous oversight model where primary care physicians (PCPs) review and finalize AI-generated medical summaries. In virtual clinical trials, g-AMIE’s diagnostic outputs and patient communications were preferred by overseeing physicians and patient actors over human-led control groups. ## Multi-Agent Architecture and Guardrails * The system utilizes a multi-agent setup powered by Gemini 2.0 Flash, consisting of a dialogue agent, a guardrail agent, and a SOAP note agent. * The dialogue agent conducts history-taking in three distinct phases: general information gathering, targeted validation of a differential diagnosis, and a conclusion phase for patient questions. * A dedicated guardrail agent monitors and rephrases responses in real-time to ensure the AI abstains from sharing individualized diagnoses or treatment plans directly with the patient. * The SOAP note agent employs sequential multi-step generation to separate summarization tasks (Subjective and Objective) from more complex inferential tasks (Assessment and Plan). ## The Clinician Cockpit and Asynchronous Oversight * To facilitate human review, researchers developed the "clinician cockpit," a web interface co-designed with outpatient physicians through semi-structured interviews. * The interface is structured around the standard SOAP note format, presenting the patient’s perspective, measurable data, differential diagnosis, and proposed management strategy. * This framework allows overseeing PCPs to review cases asynchronously, editing the AI’s proposed differential diagnoses and management plans before sharing a final message with the patient. * The separation of history-taking from decision-making ensures that licensed medical professionals retain ultimate accountability for patient care. ## Performance Evaluation via Virtual OSCE * The system was evaluated in a randomized, blinded virtual Objective Structured Clinical Examination (OSCE) involving 60 case scenarios. * g-AMIE’s performance was compared against primary care physicians, nurse practitioners, and physician assistants who were required to operate under the same restrictive guardrails. * Overseeing PCPs and independent physician raters preferred g-AMIE’s diagnostic accuracy and management plans over those of the human control groups. * Patient actors reported a preference for the messages generated by g-AMIE compared to those drafted by human clinicians in the study. While g-AMIE demonstrates high potential for human-AI collaboration in diagnostics, the researchers emphasize that results should be interpreted with caution. The workflow was specifically optimized for AI characteristics, and human clinicians may require specialized training to perform effectively within such highly regulated guardrail frameworks.

Human Ai Collaboration AI Generative AI Natural Language Processing+4

figmaJun 17, 20243 min readCurated summary

Navigating the Promise and Pitfalls of AI | Figma blog | Figma Blog

AI’s promise is real, but useful AI products will emerge through experimentation rather than a race to ship features. Figma’s research suggests AI is already transforming individual workflows—especially for developers—while having a smaller effect on collaboration and foundational design work. To deliver lasting value, teams must focus on how AI reshapes products, industries, and group work, not just on model capabilities. ## Research and Methodology - Figma surveyed more than 1,800 designers, executives, and developers. - Participants came from the US, Canada, Australia, the UK, Japan, France, and Germany. - The survey ran from February 26 to March 3, 2024. - The report combines survey findings with discussions involving AI and design experts. - Its central premise is that AI’s impact depends heavily on product design and user experience—not only on the power of large language models. ## AI’s Uneven Transformation of Workflows - Developers were 60% more likely than designers to say AI had transformed the products they work on. - Developers use AI for daily tasks such as generating starting points and translating between programming languages. - AI-generated output is currently perceived as more reliable for developer workflows. - Designers may use AI to turn mockups into code, but much of design’s foundational work still involves: - Understanding user needs - Exploring problems nonlinearly - Learning about the broader problem space - AI initiatives increasingly originate outside design, with programmers, subject-matter experts, and stakeholders contributing ideas. ## AI Must Improve Collaboration, Not Just Individual Productivity - Eighty-five percent of respondents said AI had affected their personal productivity or workflows. - Common uses include text and image generation, brainstorming, and using AI as a sounding board or thought partner. - Respondents were three times more likely to report significant changes to individual workflows than to collaborative ones. - AI has not substantially changed group activities such as alignment or meeting facilitation. - Truly transformational AI products will need to support how teams work together, rather than focusing only on isolated tasks performed by individuals. ## Long-Term Effects Across Industries - Respondents in technology, professional and business services, and retail most often expected significant AI-driven changes to their products and services: - Technology: 41% - Professional and business services: 40% - Retail: 39% - Healthcare, energy and utilities, and telecommunications respondents expected the least impact over the following 12 months. - The report argues that realizing AI’s full potential requires considering how major institutions and essential services—not just software products—will evolve. ## Experimentation Before the Product Race - AI development is still characterized by experimentation, play, and research. - Teams face pressure to release new AI features quickly as new products, applications, and research appear constantly. - The recommended approach is to embrace uncertainty, iterate thoughtfully, and determine which ideas genuinely create value. - As the technology matures, the most successful products will likely come from careful exploration rather than simply adding AI features because of market hype.

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Human Ai Collaboration AI Large Language Models Machine Learning+2