Future Radar: AI, GenUI, and Agents
Generative UI component registries
| Lens | Guidance |
|---|---|
| What it is | A controlled set of components and schemas that AI systems can choose from to produce interface fragments. |
| Why architects should care | It turns open-ended generation into bounded product composition. |
| Maturity level | Adopt for serious GenUI work; do not let models invent arbitrary UI. |
| Adoption signal | AI must render task-specific controls, summaries, comparisons, forms, or approvals. |
| Risks | Schema drift, inaccessible combinations, unsafe props, version mismatch. |
| When to use | Copilots, dynamic task surfaces, internal tools, support workflows. |
| When to avoid | Simple chat answers or static UI where deterministic components are enough. |
| What to learn now | Schema validation, component versioning, accessibility contracts, telemetry. |
| Connected chapters | Part XIII GenUI architecture and React implementation patterns. |
MCP
| Lens | Guidance |
|---|---|
| What it is | A protocol pattern for connecting AI systems to external tools and data sources. |
| Why architects should care | It standardizes integration shape but does not remove the need for permission, audit, and infrastructure controls. |
| Maturity level | Adopt carefully. Treat servers and tools as privileged integration surfaces. |
| Adoption signal | Multiple tools/data sources need consistent discovery and invocation semantics. |
| Risks | Prompt injection, unsafe local execution, tool over-permissioning, supply-chain risk, weak audit. |
| When to use | Internal tool orchestration, controlled data access, developer or operations workflows. |
| When to avoid | Public untrusted tool marketplaces without hard isolation and review. |
| What to learn now | Tool schemas, permission propagation, sandboxing, transport security, audit logs. |
| Connected chapters | Part XIII MCP/A2A integration boundaries. |
A2A
| Lens | Guidance |
|---|---|
| What it is | An agent interoperability protocol for delegation and communication between independent agents. |
| Why architects should care | It introduces multi-agent boundaries that look like distributed systems, not simple function calls. |
| Maturity level | Watch or pilot. Use for clear delegation boundaries, not because multi-agent sounds advanced. |
| Adoption signal | A workflow needs specialized agents owned by different systems or vendors. |
| Risks | Accountability gaps, cascading failure, unclear user consent, trace fragmentation. |
| When to use | Specialized agent delegation with explicit task contracts and audit. |
| When to avoid | Single-product workflows where one orchestrator and tools are simpler. |
| What to learn now | Task contracts, agent identity, delegation limits, observability correlation. |
| Connected chapters | Part XIII agentic workflows and MCP/A2A boundaries. |
Evals as release infrastructure
| Lens | Guidance |
|---|---|
| What it is | Test suites and judgment workflows that block or guide AI releases. |
| Why architects should care | AI systems change behavior without code diffs; release gates need examples, adversarial cases, and production feedback. |
| Maturity level | Required for production AI features. |
| Adoption signal | AI output affects decisions, user actions, compliance, or customer trust. |
| Risks | Narrow fixtures, unowned datasets, subjective thresholds, missing regression history. |
| When to use | Every serious RAG, GenUI, tool-calling, or agentic feature. |
| When to avoid | Never avoid; scale the eval to risk. |
| What to learn now | Golden sets, red-team prompts, retrieval metrics, tool safety tests, human review. |
| Connected chapters | Part XIII production GenUI playbook and capstone. |
Source lens
- A2A protocol specification
- Model Context Protocol ecosystem guidance
- GenUI chapters in Part XIII