--- title: "Foundations" canonical: "https://current.tinyfish.ai/issue/11/foundations" publication: "TinyFish" publisher: "TinyFish" language: "en-US" guide: "https://current.tinyfish.ai/llms.txt" permalink: "https://current.tinyfish.ai/issue/11/foundations" issue: 11 section: "foundations" description: "Conceptual clarity earned from building at scale" authors: ["Nora Kaplan","Rina Takahashi"] published: 2026-01-11 updated: 2026-01-08 articles: 11 --- # 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 11 — Foundations Conceptual clarity earned from building at scale ### What Building Teaches Builders - [Three Learning Modes That Separate Successful Agent Teams From Struggling Ones]() — By [Nora Kaplan]() · Published 2026-01-07 · Format: Field Guide When Harvard researchers tracked 758 consultants working with AI, they discovered a puzzle: teams using identical technology saw quality jump 40% or drop 19 points. The technology wasn't the variable. ### Tools & Techniques - [Schema Validation Catches Breaks Immediately—At a Maintenance Cost]() — By [Rina Takahashi]() · Published 2026-01-07 · Format: Tools in Context Schema validation gives you something rare in web scraping: immediate certainty when extraction breaks. Define your rules once, catch violations instantly. A required field disappears, a price becomes text instead of numbers—you know within seconds. But every site change means someone's updating validation rules, often at 2am when they break. That clarity about what failed comes with constant maintenance work, and at scale, the burden becomes real operational overhead. - [Statistical Validation Adapts to Scale—But Misses Sudden Breaks]() — By [Rina Takahashi]() · Published 2026-01-07 · Format: Tools in Context Build baselines from actual extraction history, let them adapt as sites evolve—statistical validation handles the web's constant changes without manual rule updates. A/B tests and gradual shifts don't trigger false alarms because the system learns what normal variation looks like. But when a site completely redesigns overnight, statistical methods take days to flag the problem. The adaptation that makes this approach scale effortlessly also means missing what schemas catch instantly: sudden structural failures. ### Pattern Recognition - [Reasoning Models Changed How Agents Actually Work]() — Published 2026-01-07 Late 2024 brought reasoning models. Everyone expected better math. What actually happened: reliable tool calling at scale. ### Questions Worth Asking Seventy percent of agent deployments fail. The questions that predict which side of that statistic you'll land on have nothing to do with capabilities or features. They're about operational realities that only become obvious after you've shipped to production and watched things break in ways traditional software never does. We've learned to ask these questions early. Observability becomes an emergency retrofit. Coordination overhead consumes the benefits of parallelization. Audit requirements surface after architecture is locked. These patterns repeat across teams because the right questions reveal what actually predicts success at scale. - **When Does Observability Get Built?** — [Source]() — Published 2026-01-07 Tag: Observability Infrastructure Most teams add observability after deployment. Agents operating as black boxes make diagnosis impossible when issues emerge. You'll need comprehensive tracing eventually. The real choice is building it from day one or retrofitting it during your first production incident. - **What Happens When Tests Pass Differently?** — [Source]() — Published 2026-01-07 Tag: Testing Strategy Traditional CI/CD expects identical inputs to produce identical outputs. Agents generate different responses to the same query based on temperature, context windows, or model updates. Pass/fail testing catches complete failures but misses quality variations that actually matter. - **Where Does Coordination Cost More Than Parallelization Provides?** — [Source]() — Published 2026-01-07 Tag: Architecture Decisions Multi-agent systems incur coordination overhead that scales non-linearly with agent count. Each handoff adds latency through serialization, network transfer, and state synchronization. Beyond certain thresholds, coordination consumes more resources than the parallelization provides. - **Can You Track Per-Request Costs Across Providers?** — [Source]() — Published 2026-01-07 Tag: Cost Management Agents rely on LLM calls billed per token and external APIs with varying pricing. Without granular tracking across models and providers, costs spiral unpredictably at scale. You need to identify which requests, which models, and which workflows drive your costs. - **What Audit Trail Exists for Agent Decisions?** — [Source]() — Published 2026-01-07 Tag: Governance Requirements Regulated industries require comprehensive logs showing why agents made specific recommendations. Without detailed traces of reasoning and actions, compliance becomes impossible. High-impact decisions need human approval until reliability is proven. Can you reconstruct the complete decision path months later? - **Does Your Storage Handle Unpredictable Agent Workloads?** — [Source]() — Published 2026-01-07 Tag: Data Architecture Agents spin up thousands of short-lived workloads, run parallel experiments, and tear down datasets unpredictably. Traditional data platforms built for stable, predictable demands struggle with this pattern. Most teams discover this requirement after architecture decisions are locked in. ### In Dialogue With Complexity - [An Interview With the Fundamental Problem Every Standards Body Faces]() — Published 2026-01-07 We're sitting in what Coordination describes as "the space between good intentions and actual interoperability." Given that we're discussing the newly formed Agentic AI Foundation, this feels appropriate. Coordination has been around since humans first tried to work together on anything, and has watched countless attempts to standardize emerging technologies. When the Linux Foundation announced the Agentic AI Foundation in late 2025 [source 1](), Coordination perked up with the weary interest of someone who's seen this movie before but keeps buying tickets anyway. ## Other sections in Issue 11 - [Daily Brief]() - [Market Pulse]() - [Practitioner's Corner]() - [Echoes]() - [Vision]() ## More - [Publication guide]() - [Issue archive]()