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  • 4 min read

Generative UI: From Static Screens to Adaptive Systems

Generative UI — why now

Frictionless UX drives usage. Generative UI reduces friction by assembling interfaces at runtime.

Generative UI adaptive interface examples

Adaptive interfaces that respond to context and user signals

Generative UI: interfaces whose structure and behavior are generated on‑the‑fly by models, not hard‑coded. Principles:

  • Dynamic assembly: Models + analytics compose components in real time per user, device, and goal.
  • Prompt → UI spec: Intent becomes a typed JSON/declarative spec rendered by a client SDK (e.g., React).
  • Outcome‑oriented personalization: Designers set goals and constraints; the system adapts using user signals (preferences, behavior, environment).

Types: static (fill parameters), declarative (assemble from a registry), fully generated (raw HTML/CSS). Declarative best balances flexibility and reliability. Research flags trust, cognitive load, and fairness risks. Add constraints and a11y guardrails. Personalization can also improve readability (e.g., font/spacing per Readability Matters).

Brief: shift from interface-first to outcome-first. Define capabilities, allowlists, and must/should/never rules per individual. Personas and journeys become dynamic; invest in research, testing, and evaluation. We design outcomes and parameters—the system renders the right interface for the moment.

### Contracts before intelligence - UI schema (DSL): typed JSON/YAML describing pages, layouts, components, bindings. Treat as the API between generators and renderers. - Design system primitives: tokens, layout primitives, and a stable component library with clear props and accessibility guarantees. - Capability map: what the app can do (search, create, export). Compose only from capabilities; never invent them. - Policy & safety: allowlists, prop constraints, data access scopes, redaction, and rate limits. Reject or sanitize invalid schemas. - Observability: structured logs of inputs, chosen variants, user events, and outcomes to drive evaluation.

Proven patterns

  • Server-driven UI (schema-first): Backend returns a UI schema; client renders. Deterministic and debuggable.
  • Slot filling: Model fills copy, labels, hints, or validation messages within an approved layout.
  • Mixed-initiative flows: Assistant proposes; user approves/edits/rejects—no silent changes.

Context signals for adaptation

Front-end-only mini-CDP signals the UI can use:

  • Behavioral events: page/screen views, clicks on nav items, dwell time, scroll depth, search terms, feature usage (e.g., "FX opened", "BillPay started").
  • Derived traits: "likes FX", "frequent transfers", "explores offers", "prefers dark mode", "prefers TR locale".
  • Recency/frequency: last 5 visited menu paths, top 3 actions, last seen balances section.
  • Explicit preferences (if the user opts in): favorite quick actions, compact vs. comfy layout.

Quick-win demos (client-side only)

All achievable purely client-side with consent + first-party storage and no user identity:

  1. Remembered navigation – If the user browsed deep into Payments → Utilities, next visit shows "Pay Bill" first and collapses rarely used categories.
  2. Actionable insights – Promote "Transfer" and "FX" tiles if used frequently; demote others. Recently used beneficiaries appear inline (stored locally as hashes/aliases, not PII).
  3. Contextual nudges – If the user lingers on "Services," surface a "Set up Auto‑Save" card next visit. If they ignore a banner 3 times, suppress it for 30 days (local streak counter).
  4. Reading mode preference – Toggle compact vs. comfy density based on past toggles + dwell time; remember dark mode.
  5. Search intelligence – If they searched "exchange rates" twice in a week, pre‑expand the FX widget on load.
  6. Micro‑journeys without identity – User taps "Pay Bill," backs out; show a "Continue Bill Pay?" entry point next visit (timer‑gated, local only).
  7. Language/locale nudges – If locale is TR and consistently used, keep it sticky and prioritize TR‑first copygen banners.
  8. Quick‑action reordering – Automatically reorder top 4 quick actions based on frequency + recency.

Mirrors the behavioral part of Insider (events → segments → experiences), but not the cross‑channel/CDP pieces (email, push, journeys), which need a backend.

Guardrails and UX quality

Quality guardrails keep generation safe and consistent. - Determinism boundaries: Models may select from allowlisted components and props—never raw code or untyped HTML. - A11y by default: Components must remain accessible regardless of who (human/model) chooses them; enforce roles, labels, focus order. - Latency budgets: Cache schemas, stream renderable chunks, precompute common variants; degrade gracefully when models are slow/offline. - Consistency & theming: Only generate within tokenized design primitives; treat tokens as hard constraints, not suggestions. - Data hygiene: Validate bindings, throttle queries, and sanitize outputs; never let models emit executable code or unsafe URLs.

Engineering checklist

  • Define the DSL: Types, versioning, validation (JSON Schema + runtime checks).
  • Build the renderer: Deterministic schema → component mapping; exhaustive prop validation and safe defaults.
  • Write policy: Allowlists, prop ranges, PII controls, auth scopes; reject on breach with actionable errors.
  • Offline-first: Cached templates and non-model fallbacks; never block critical paths on generation.
  • Evaluation harness: Golden tasks, screenshot diffs, a11y tests, latency/error SLOs, canary rollouts.

  • Telemetry & feedback: Capture edits/aborts, success metrics, and model rationales to improve selection over time.

  • GenAIUI Whitepaper (2024)

  • 1 min read

Local PII Pre-Filter with Microsoft Presidio + Qwen 2.5

Place a small, local PII pre-filter in front of any LLM by combining deterministic, explainable detection from Microsoft Presidio (GitHub, docs) with a tiny CPU SLM such as Qwen 2.5 to catch fuzzy or implicit PII—packaged in containers to run on laptops or servers with no GPU. This pre-ingest/pre-prompt guard blocks, redacts, or annotates content before the LLM sees it, emitting auditable spans, types, and confidences to reduce exfiltration risk and deliver compliance-by-construction. It fits as a pre-ingest, pre-prompt, or guardrail-loop step, forwarding only clean text to downstream copilots/LLMs. Live demo: pii-checker.com.

  • 1 min read

AI Paradigm Shifts and their Data Requirements

Below is from a presentation I delivered at the hub mapping AI paradigm shift's data requirements to Data. Data narrative is mostly about unification and organization.

What I am really happy about is Microsoft embracing Graphs in GenAI context within Fabric to be able to build better AI products.

What these shifts demand from Data slide

Agentic AI

  • Unify your Data
  • Organize your Data
  • Build data flywheels
  • Agent Memory

AI Assisted Coding

  • Unified SDK
  • Semantic Layer

AI Security & Governance

  • Unified Security Posture
  • Unified Policy & Risk
  • Lineage & Provenance

Microsoft Fabric Unifies your Data and Graph support organizes it by turning Data -> Knowledge.

  • 1 min read

Copilot Studio

Copilot Studio

Copilot Studio is the SaaS agent builder from Microsoft. It is a no-Code and can be a low-Code tool and in essence lets you integrate your data with llm's and create agents with NLP input without writing code.

Key Features

  • MCP & A2A Integration
  • Bing Grounding
  • Message moderation - Automated Prompt / Generation filtering
  • Query Optimization
  • 2 min read

Forward Deployed Engineering for building AI Agents

Forward Deployed Engineering Playbook

AI agents delivers an outcome by coordinating across many tools whereas traditional product categories are single-tool boxes. Think of a healthcare intake agent. It captures a referral, reads EHR notes and orders, checks eligibility and prior authorization with payers, assembles required documentation, schedules the patient, gets consents, sends reminders, and updates both the EHR and billing—end to end. The value is a resolved intake: “patient scheduled with auth in place, docs filed, denial risk reduced.” Agent does the whole work end-to-end delivering an outcome. Users don't live in the UI trying to push workflows forward. It is almost like a self-driving car that does the whole journey end-to-end (AI Agents) vs a navigation system in the car that only guides you (traditional software).

Therefore AI Agents don't map into an incumbent product category. In order to grow business, you must do product discovery from inside the enterprise with domain experts as well as rapid prototyping with deployed engineers.

FDE program isn’t just a sales tactic; it’s how you 1. Discover the real product, 2. Prove ROI fast, and finally 3. Convert that into bigger, outcome-priced contracts and reusable “agent core” IP.

This is a two-role operating system: - Echo (embedded analysts / “heretics”): insiders who know the domain and want to change it. - Delta (deployed engineers): Prototype under pain & time pressure; throwaway is acceptable.

Other defining characteristics of an FDE program engagement are... - Contract value is continously pushed up by the FDE team uncovering more use-cases and value. - Prototyping is used heavily to inspire or reveal "real user desire".

Ultimately FDE is about doing things that don't scale and turning them into scalable methods and assets that you improve as you go along. e.g. applying the same assets and solutions in other customers.

FDE talk from Bob McGrew (Check it out: FDE talk from Bob McGrew) really resonated with me as it aligns well with my earlier "Frontier Labs" initative at Microsoft Digital Natives org which was mostly about working with customer product as well as engineering managers to inspire them with quick but functional prototypes that demonstrate the potential and value with a product-led approach.

  • Ozgur Istanbul, September 11th 2025