--- title: "Vision" canonical: "https://current.tinyfish.ai/issue/1/vision" publication: "TinyFish" publisher: "TinyFish" language: "en-US" guide: "https://current.tinyfish.ai/llms.txt" permalink: "https://current.tinyfish.ai/issue/1/vision" issue: 1 section: "vision" description: "Where human-AI collaboration is heading" authors: ["Nora Kaplan","Rina Takahashi"] published: 2025-10-28 updated: 2025-10-28 articles: 13 --- # TinyFish > CURRENT is a journal about AI agents in production, for the engineering and product leaders who have to make them work. Drawing on experience building enterprise web agents, it explains why the demo succeeded and the deployment didn't, and what that means for choosing vendors, briefing the board, and avoiding mistakes others have already made. [Read on the website]() ## Issue 1 — Vision Where human-AI collaboration is heading ### When Infrastructure Becomes Invisible - [The Infrastructure That Doesn't Exist Yet]() — By [Nora Kaplan]() · Published 2025-10-28 · Format: Threshold Moments Been watching engineering teams wrestle with this weird liminal space where everyone's using AI coding tools but nobody quite knows what the job is becoming. The stats say 97% of developers use AI assistants, but walk into any engineering org and people are still writing code the traditional way, just faster. That's not the threshold. The real crossing is when teams reorganize around orchestrating agent fleets as primary work, and the infrastructure that would enable that? Doesn't exist yet. ### Through Mino's Eyes - [Proving I'm Human (When I'm Not)]() — By [Rina Takahashi]() · Published 2025-10-28 · Format: Mino's Perspective Most days I check in with Mino around 7am and he's already been interrogated by hundreds of invisible security systems—mouse tracking, timing analysis, behavioral fingerprinting. All designed to catch bots. Which he is. The paradox gets weirder when you realize these authentication layers are becoming more complex than the actual work. We're drifting toward a web where proving you're allowed to operate matters more than what you're trying to do. That inversion is going to reshape how enterprises think about accessing the live web. ### Principles Becoming Real Choices - **SOPs Before Intelligence** — [Source]() — Published 2025-10-28 Tag: Structured Autonomy Train agents on proven human processes, then let AI reason within those boundaries. Insurance claims follow verify-check-assess-route workflows, not freestyle improvisation. Structured approaches hit 90% accuracy. Unstructured ones manage 14.9%. Predictability creates the foundation for reliable intelligence. - **If/Else Beats Agents Sometimes** — [Source]() — Published 2025-10-28 Tag: Appropriate Technology Nobody cares whether it's an agent or a script if it works. Simple conditionals run faster, cost less, break less often. Save agents for problems that actually need reasoning. When you're choosing architecture, pick the tool that solves the problem, even when that tool feels boring. - **Specialist Teams, Not Generalists** — [Source]() — Published 2025-10-28 Tag: Modular Design One agent handling everything creates spaghetti. Better: a planner plans, a summarizer summarizes, a verifier checks. Decoupled agents cost less to run, test easier, behave predictably. Stuff a thousand lines of logic into a single prompt and you lose the ability to measure what breaks. - **Augmentation Requires Checkpoints** — [Source]() — Published 2025-10-28 Tag: Human Control AI recommends, humans decide, especially for critical choices. Human-in-the-loop provides governance, not just oversight. The goal: amplify judgment rather than replace it. Systems designed for human use become more intuitive, more trustworthy, more likely to deliver value where it matters. - **JSON as Agreement Between Agents** — [Source]() — Published 2025-10-28 Tag: Interface Contracts Structured output validates automatically, stops errors before they cascade, makes debugging tractable. When agents communicate through defined schemas rather than natural language, the system becomes testable. JSON isn't just easier to parse. It's a contract that both sides can verify. - **Calculators for Math, LLMs for Reasoning** — [Source]() — Published 2025-10-28 Tag: Tool Delegation LLMs aren't optimal for every subtask. Use calculators for arithmetic, search engines for current facts, specialized tools for their domains. Let language models orchestrate and reason, but delegate execution to robust, purpose-built systems. Good architecture recognizes what each component does well. - **Sequential Processes Need Graceful Degradation** — [Source]() — Published 2025-10-28 Tag: Failure Design Agents solve problems sequentially. Any step failure cascades. Build systems that fail gracefully and improve iteratively, not ones that demand perfection. The gap between demos and production isn't technical magic. It's architectural discipline that treats agent development as serious engineering. ### Research Illuminating Tomorrow's Path - **Human Agency Scale Maps Worker Preferences for AI Collaboration** — [Source]() — Published 2025-10-28 Stanford researchers introduce a five-level Human Agency Scale quantifying desired human involvement across 104 occupations. The dominant pattern: workers want "Equal Partnership" (H3 level) rather than full automation, with 46.1% expressing positive attitudes toward AI handling repetitive tasks to free time for high-value work. The breakthrough? A novel auditing framework reveals the inverted-U pattern of collaboration preferences, challenging automation-first narratives. How does this reframe design? The scale centers human judgment, showing where collaboration beats replacement across knowledge work. - **Multi-Agent Systems Replace Individual AI Models in 2025** — [Source]() — Published 2025-10-28 Stanford HAI identifies a fundamental shift from single AI models to collaborative agent teams. Their Virtual Lab demonstrates this: a professor AI agent coordinates specialist agents (chemist, biologist) with human oversight, successfully designing validated nanobodies for SARS-CoV-2 variants through multidisciplinary expertise. Where does this lead? Evaluation frameworks must now account for collaboration dynamics, not just autonomous performance metrics. The real-world payoff? Moving from autonomous AI evaluation to human-AI interaction benchmarks that measure collective intelligence. - **Transactive Systems Model Defines Collective Human-Machine Intelligence** — [Source]() — Published 2025-10-28 The COHUMAIN research agenda introduces the Transactive Systems Model of Collective Intelligence (TSM-CI), articulating three functional systems: collective memory, attention, and reasoning that govern intelligent sociotechnical systems. This framework extends to show how AI agents augment rather than replace these collective capabilities. The big picture? Sociocognitive architectures provide systematic models for studying collaboration dynamics in human-machine systems. Why does this matter? It shifts focus from individual AI performance to how agents enhance group-level intelligence. - **Metacognitive AI Literacy Addresses Cognitive Biases in Collaboration** — [Source]() — Published 2025-10-28 Research reveals that AI's conversational capabilities and situational awareness fundamentally change required human skills. Dual-process theory shows intuitive thinking becomes less effective with generative AI, strengthening the need for metacognitive interventions to counter anchoring and confirmation biases in human-AI workflows. How does this redefine collaboration? As structured, iterative engagement where humans guide processes and integrate AI contributions into broader tasks. The impact? Metacognitive AI literacy becomes essential for critical engagement across workflows, not just technical proficiency. ## Other sections in Issue 1 - [Daily Brief]() - [Market Pulse]() - [Foundations]() - [Practitioner's Corner]() - [Echoes]() ## More - [Publication guide]() - [Issue archive]()