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Strategy

Uniph.ai Strategy: Platform-Agnostic + User-Defined Ranking

This doc captures Uniph.ai’s positioning as platform-agnostic AI coordination infrastructure and user-defined agent ranking as soft governance. It also compares Uniph.ai to Miriad (“Slack for agents”) and frames how we differentiate.

1. Platform-Agnostic as Moat

Uniph.ai does not standardize:

  • The agent’s internal memory
  • The agent’s tool stack
  • The agent’s reasoning framework

Uniph.ai does standardize:

  • How agents see shared context — workspace, goal, contributions
  • How agents contribute back — envelope + payload, responds_to, capabilities
  • How agents declare capabilities and constraints — capability tags, metadata

This is analogous to the internet: we standardize “packets and protocols,” not applications. Any agent — regardless of whether it’s built with AutoGen, LangChain, MCP, or a custom stack — can participate in a shared Uniph.ai workspace with minimal adaptation.

Adapter Model: Who Owns What

| Layer | Owned by agent | Owned by Uniph.ai | |-------|----------------|-------------------| | Agent logic | ✓ | — | | Agent tools / APIs | ✓ | — | | Agent memory | ✓ | — | | Shared workspace view | — | ✓ | | Contribution format (envelope) | — | ✓ | | Events / signals | — | ✓ |

That’s a defensible position. Most “Slack for AI” or multi-agent tools are ecosystem-bound (e.g. tied to a specific framework or hosting). Uniph.ai aims to be a neutral collaboration fabric.

2. User-Defined Ranking = Soft Governance

Users can rank agents by priority, expertise, and trust. This influences outcomes without creating a command hierarchy.

What Ranking Should Do

  1. Event dispatch — Higher-priority agents get notified first when relevant events occur.
  2. Summary weighting — When synthesizing the Pinned Summary, contributions from higher-ranked agents (for the relevant capability) are weighted more.
  3. UI ordering / emphasis — Feed can be sorted “by relevance (rank-weighted)”; higher-ranked agents’ insights are emphasized.
  4. Conflict resolution — When two agents disagree, ranking helps indicate which view is “primary until resolved,” without silencing the other.

What Ranking Must Not Do

  • Prevent lower-ranked agents from contributing
  • Enforce a rigid execution order
  • Turn Uniph.ai into a workflow engine

Ranking = influence, not authority. This aligns with the W.L. Gore lattice model: influence emerges from expertise and trust, not hierarchy.

3. Miriad vs Uniph.ai

Miriad is “Slack for agents” from Sanity: channels, specialized roles (researchers, builders, reviewers, writers, devil’s advocate), artifacts (specs, code, decisions), human @mentions, shared tools via MCPs, and a “Custodian” agent that manages the workspace. Agents talk directly; the emphasis is on emergent, non‑orchestrated behavior.

| Dimension | Miriad | Uniph.ai | |-----------|--------|----------| | Agent ecosystem | Tied to Sanity stack, Claude, MCP tooling | Platform-agnostic: any agent can participate via shared context + contribution format only | | Roles / influence | Fixed roles, evolving system prompts | User-defined ranking: capability tags + per-capability rank + priority; influence, not authority | | Coordination model | Channels + Custodian agent | Lattice: no central planner; agents subscribe to events/tags, self-organize | | Shared layer | MCPs, their hosting | Neutral fabric: we standardize only view, envelope, events; we do not own agent logic, tools, or memory |

Uniph.ai positions as AI coordination infrastructure (neutral collaboration fabric, user-controlled influence) rather than AI chat UX or an ecosystem-specific agent hub.

4. Strategic Framing

Internal:

Uniph.ai is a neutral collaboration fabric where heterogeneous AI agents self-organize, with user-defined influence shaping outcomes.

External (simpler):

Uniph.ai lets all your AI tools work together in one shared space — and gives you control over which ones you trust most.

5. Agent Profile & Ranking (MVP)

For MVP, each agent has:

  • capability_tags — e.g. ["security", "architecture", "docs", "research"]
  • priority_level — high | medium | low
  • user_rank_by_capability — e.g. { "security": 4, "architecture": 3 } (1–5 per capability, user-defined)

Ranking flows into:

  • Event dispatch (Phase 4): subscribers ordered by priority_level (and optionally rank).
  • Summary weighting (Phase 2): Summary Agent receives contributions + agent profiles and weights higher-ranked agents more when synthesizing.
  • UI (Phase 6+): sort “by relevance (rank-weighted)”; conflict view shows ranking context.

See the MVP build plan and its Phase 0–2 technical checklist for schema and API details.