inter-hub/specs/InteractionHubFrameworkSpecification_v0.2.md

InteractionHubFramework

Inter

Interaction Hub Framework (IHF)

Formal Specification with Phased Implementation Plan

Version: 0.2 Status: Draft Extension Specification Supersedes: InteractionHubFrameworkSpecification_v0.1.md Purpose: Extend the IHF beyond Phase 8 into external platform maturity — public API surface, consumer SDKs, hub marketplace, and advanced AI federation.

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1. Definition

The Interaction Hub Framework (IHF) is a platform framework for building commentable, observable, governable, and evolvable user interaction systems.

It provides a stable core for:

• addressing UI elements as governed platform assets
• capturing user feedback and interaction signals in context
• transforming observed friction and intent into structured requirements
• connecting requirements to decisions, implementations, deployments, and outcomes
• supporting multiple evolving UI technologies without losing semantic continuity
• federating governance across hub boundaries in multi-team AI factory deployments

The IHF is intended to serve as the interaction substrate of a hub-based AI factory, especially in environments where product development, operations, governance, finance, security, and AI-assisted workflows interact continuously.

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2. Design Intent

The framework is designed to support the following long-term properties:

Stable semantic core UI technologies may change, but interaction semantics, traceability, and governance remain stable.

Antifragility The system should improve through systematically captured friction, confusion, unmet expectations, and emergent user needs.

Observability by design Interaction, feedback, and governance events are first-class operational signals.

Governance by design Requirements, decisions, policy concerns, and implementation consequences remain linked and auditable.

Agent compatibility AI agents should be able to inspect, interpret, propose, implement, review, and monitor changes within explicit boundaries.

Framework independence at the edge Any UI technology that can emit the widget envelope is a first-class participant. The IHF does not mandate a particular frontend stack.

Platform composability The IHF should be consumable by external systems, third-party tooling, and downstream hubs via stable, versioned contracts. Internal governance state must not be a closed silo.

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3. Scope

In Scope

• Widget identity, lifecycle governance, and semantic addressability
• Interaction event capture and contextual enrichment
• Annotation and structured comment threads
• Requirements distillation from raw feedback clusters
• Governance ledger: decision records linked to requirements and implementations
• Outcome observation and pre/post change comparison
• AI-assisted distillation, bounded, attributable, reviewer-controlled
• Cross-framework UI adapter protocol
• Operational observability: friction scoring, bottleneck detection, health snapshots
• Federated governance: ownership delegation, requirement routing, policy overlays, stewardship, archival
• External API surface for third-party and inter-system consumption
• Consumer SDKs for common platform integrations
• Hub registry and marketplace for reusable widget and governance patterns
• Advanced AI federation for multi-model, multi-agent governance workflows

Out of Scope

• A complete universal frontend framework or design system
• Pixel-level visual design or CSS conventions
• Full product management methodology
• Replacement for DevOps observability tooling
• Unrestricted autonomous AI decision-making on requirements
• Mandatory single UI technology for all hub surfaces
• General-purpose data warehouse or business intelligence platform

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4. Core Concepts

(Unchanged from v0.1 §4 — see InteractionHubFrameworkSpecification_v0.1.md)

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5. Phased Implementation Plan

The v0.1 specification defined Phases 0–8. Phases 0–7 are complete as reference implementation. Phase 8 (Federated Hub Maturity) is in progress.

This v0.2 specification extends the plan with Phases 9–12.

Phase 0 — Concept Foundation (complete)

Phase 1 — Minimal Interaction Core (complete)

Phase 2 — Structured Feedback and Triage (complete)

Phase 3 — Governance and Decision Linkage (complete)

Phase 4 — Outcome Observation and Antifragility Loop (complete)

Phase 5 — Agent-Assisted Distillation and Suggestion (complete)

Phase 6 — Cross-Framework UI Adaptation Layer (complete)

Phase 7 — Advanced Observability and Operational Integration (complete)

Phase 8 — Federated Hub Maturity (in progress)

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Phase 9 — External API Surface and Consumer SDKs

Goal

Make the IHF consumable by systems outside the reference IHP implementation. Phase 8 established federated governance within a single deployment. Phase 9 exposes that governance state as a stable, versioned, authenticated REST API and ships consumer SDKs that make integration a day's work rather than a project.

Scope

• Versioned REST API (/api/v2/) for all core IHF artifact types
• OpenAPI 3.1 specification generated from the live schema
• Authentication: OAuth 2.0 client credentials flow (superseding per-hub Bearer tokens)
• API key management UI for external consumers
• Consumer SDKs: TypeScript/Node, Python
• Webhook delivery for interaction events, candidate creation, and decision records
• API usage dashboard: request counts, error rates, consumer identity
• Rate limiting and quota management per consumer

Functional Capabilities

• External systems can read widget registry, interaction events, annotations, requirement candidates, decisions, deployments, and outcome signals
• External systems can submit interaction events and annotations via the API
• Downstream hubs can subscribe to governance events via webhooks
• SDK consumers get type-safe access to IHF contracts without reading the spec
• API consumers are tracked, quotaed, and auditable

Exit Criteria

• All core IHF artifact types are readable via /api/v2/
• Interaction events and annotations are writable via /api/v2/
• OpenAPI spec is generated and accurate
• TypeScript SDK and Python SDK published (as static files or packages)
• Webhook delivery confirmed for at least two event types
• API usage dashboard renders correctly
• OAuth token flow works end-to-end

Data Artifacts Introduced

ApiConsumer, ApiKey, WebhookSubscription, WebhookDelivery

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Phase 10 — Hub Registry and Widget Marketplace

Goal

Enable reuse of proven widget patterns, governance templates, and hub configurations across deployments. Phase 9 made the IHF externally consumable. Phase 10 makes it composable: hubs and widgets can be discovered, rated, adopted, and evolved as shared platform assets.

Scope

• Hub registry: a catalog of registered hubs with public metadata, capability declarations, and health summaries
• Widget pattern library: reusable widget definitions that can be instantiated into any hub
• Governance template library: requirement distillation and decision templates that can be cloned across hubs
• Widget ratings and adoption tracking: which widgets are in use where, with aggregated friction scores across deployments
• Pattern versioning: widget patterns have explicit versions; hubs can pin or follow-latest
• Marketplace dashboard: browse, search, and adopt patterns

Functional Capabilities

• Hub operators can publish a widget pattern to the shared library
• Hub operators can adopt a published pattern into their hub
• Governance templates (requirement categories, decision checklists) can be cloned across hubs
• Widget adoption across hubs is tracked for aggregate friction and outcome analysis
• Pattern authors receive friction and outcome feedback from all adopter hubs (opt-in anonymised)

Exit Criteria

• Hub registry renders all registered hubs with capability metadata
• Widget pattern library lists published patterns with version history
• A pattern can be published from one hub and adopted into another
• Adoption tracking shows which hubs use which patterns
• Governance template cloning works end-to-end
• Marketplace dashboard renders search and browse

Data Artifacts Introduced

WidgetPattern, WidgetPatternVersion, PatternAdoption, GovernanceTemplate, GovernanceTemplateClone

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Phase 11 — Advanced AI Federation

Goal

Move AI assistance from single-model, single-agent operation to a federated, multi-model, multi-agent architecture. Phase 5 introduced bounded AI support for distillation and suggestion. Phase 11 introduces agent specialisation, model routing, inter-agent delegation, and collective AI governance — while keeping humans firmly in control of final decisions.

Scope

• Agent registry: named, versioned AI agents with declared capabilities and trust levels
• Model routing: select model per task type based on cost, capability, and policy
• Inter-agent delegation: an agent can spawn or invoke a specialist sub-agent with a bounded scope
• Agent collaboration records: when multiple agents contribute to a single proposal, all contributions are attributed
• Collective proposal review: proposals that require multi-agent consensus before human review
• AI governance policies: per-hub rules controlling which agents may operate, on which artifact types, with what authority
• Agent performance tracking: acceptance rate, revision rate, confidence calibration over time

Functional Capabilities

• Hub operators can register and configure specialised agents (triage agent, synthesis agent, policy agent, implementation agent)
• Agent tasks are routed to the lowest-cost model that meets capability and policy requirements
• A delegating agent can hand off a subtask with a capped token budget and scoped context
• All inter-agent delegations are recorded and auditable
• Humans can inspect the full provenance of any AI output: which agents contributed, which models were used, in what order
• AI governance policies can restrict specific agents to read-only or advisory-only roles per hub

Exit Criteria

• Agent registry lists all registered agents with capability and trust metadata
• Model routing selects the correct model for at least three task types
• Inter-agent delegation creates an auditable delegation record
• Collective proposals show all contributing agents
• AI governance policy blocks an out-of-scope agent action
• Agent performance dashboard renders acceptance and calibration metrics

Data Artifacts Introduced

AgentRegistration, ModelRoutingPolicy, AgentDelegation, CollectiveProposal, AiGovernancePolicy, AgentPerformanceRecord

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Phase 12 — Platform Memory and Continuous Learning

Goal

Close the longest feedback loop in the IHF: from deployed outcome signals and accumulated governance history back to improved distillation, better routing, and sharper AI proposals. Phase 12 makes the IHF a learning platform, not merely a record-keeping one.

Scope

• Long-term outcome correlation: connect deployment outcomes to the original requirement and annotation signals that drove them, across quarters
• Pattern performance memory: track which widget patterns, governance templates, and routing rules have historically produced good outcomes
• Adaptive friction thresholds: friction score weights and bottleneck thresholds adjust based on observed hub-specific outcome data
• Institutional knowledge capture: significant decisions, their context, and their outcomes are distilled into a queryable knowledge base
• Retroactive lineage enrichment: as new outcomes arrive, earlier records in the traceability chain are enriched with outcome metadata
• Learning dashboard: shows what the platform has learned — best-performing patterns, most-predictive signals, longest-value decisions

Functional Capabilities

• The platform can answer "which types of annotation predicted a negative outcome in this hub?"
• Widget patterns are ranked by historical outcome quality, not just adoption
• Friction thresholds for a hub can be calibrated from its own outcome history
• Governance decisions are enriched with outcome summaries when signals arrive
• A knowledge base of significant decisions is queryable by agents and operators
• The learning dashboard shows platform-level insights with evidence links

Exit Criteria

• Long-term outcome correlation produces at least one actionable hub-specific insight from test data
• Pattern performance ranking changes after seeding outcome data
• Adaptive thresholds update friction scores after calibration run
• Institutional knowledge base is queryable and returns relevant decisions
• Retroactive lineage enrichment updates at least one decision record with outcome metadata
• Learning dashboard renders all panels with correct evidence links

Data Artifacts Introduced

OutcomeCorrelation, PatternPerformanceRecord, AdaptiveThresholdConfig, InstitutionalKnowledgeEntry, LearningInsight

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6. Revised Phased Summary

Phase	Title	Status
0	Concept Foundation	complete
1	Minimal Interaction Core	complete
2	Structured Feedback and Triage	complete
3	Governance and Decision Linkage	complete
4	Outcome Observation and Antifragility	complete
5	Agent-Assisted Distillation	complete
6	Cross-Framework UI Adaptation	complete
7	Advanced Observability	complete
8	Federated Hub Maturity	in progress
9	External API Surface and Consumer SDKs	planned
10	Hub Registry and Widget Marketplace	planned
11	Advanced AI Federation	planned
12	Platform Memory and Continuous Learning	planned

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7. Dependency Graph (Phases 9–12)

Phase 8 (Federated) ──→ Phase 9 (External API)
                              │
                              ▼
                        Phase 10 (Marketplace)
                              │
Phase 7 (Observability) ──→ Phase 11 (AI Federation)
Phase 5 (Agent Assist)  ──┘       │
                                  ▼
                        Phase 12 (Platform Memory)
                              ▲
Phase 4 (Outcomes) ───────────┘

• Phase 9 requires Phase 8 (stable federated schema, OAuth replaces per-hub Bearer tokens)
• Phase 10 requires Phase 9 (marketplace API is built on v2 API surface)
• Phase 11 requires Phase 5 (agent model) and Phase 7 (observability signals needed for model routing and performance tracking)
• Phase 12 requires Phase 4 (outcome signals), Phase 7 (friction/health history), and Phase 11 (agent performance as a learning input)

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8. Risks and Failure Modes (v0.2 additions)

8.1 API Surface Becoming a Governance Bypass

Risk: external consumers write interaction events or annotations without honouring the IHF envelope contract, polluting the governance record. Mitigation: the /api/v2/ write endpoints validate against the active InteractionReportingContract and EnvelopeEmissionContract. Writes that fail validation return 422 with contract-referenced errors.

8.2 Marketplace Pattern Divergence

Risk: adopted widget patterns diverge from their source over time, breaking the aggregate friction and outcome correlation. Mitigation: PatternAdoption records the version pinned at adoption time. Pattern authors can issue new versions; adopters choose whether to migrate. Aggregate metrics are version-scoped.

8.3 AI Federation Complexity Outpacing Auditability

Risk: multi-agent delegation chains become too deep to audit meaningfully. Mitigation: AgentDelegation has a max depth field enforced at creation time (default: 2). Chains deeper than the hub's AiGovernancePolicy.max_depth are rejected with an audit record of the refusal.

8.4 Learning Feedback Loops Reinforcing Bias

Risk: outcome correlation training on biased historical data produces systematically wrong friction weight adjustments. Mitigation: AdaptiveThresholdConfig records the data window and sample size used for each calibration. Calibrations below a minimum sample threshold are flagged as low_confidence and do not apply automatically.

8.5 Knowledge Base Becoming Stale

Risk: InstitutionalKnowledgeEntry records reflect decisions that were later superseded, misleading agents and operators. Mitigation: entries link to their source DecisionRecord. When a decision is superseded (a new decision is linked to the same requirement), the knowledge entry is flagged superseded and a new entry is created for the successor.

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9. Success Criteria (v0.2 additions)

The Interaction Hub Framework at v0.2 maturity is successful when:

• external systems can read and write IHF state via stable, versioned contracts
• widget and governance patterns are reusable without copy-paste
• multiple AI agents collaborate on proposals without reducing accountability
• the platform visibly learns from its own outcome history
• hub leaders can make evidence-based decisions about pattern adoption and agent configuration without reading source code
• the IHF can be deployed across an organisation with clear ownership, policies, and audit trails at every layer

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10. Short Strategic Summary

The Interaction Hub Framework v0.2 extends the governed interaction substrate into a composable, federated, learning platform.

Where v0.1 asked: can we make interaction observable and governance auditable?

v0.2 asks: can the platform teach itself, share what it knows, and remain accountable while doing so?

The answer depends on making every new capability — API surface, marketplace patterns, AI federation, outcome correlation — subject to the same contract discipline and human oversight that governs the core.

That continuity of principle is what makes the framework trustworthy at scale.