Enterprise AI / Project note
Enterprise Agent Navigator
A routing and discovery layer that matches enterprise users with the right specialist AI workflow based on intent and context.
- Stage
- Portfolio capability · Concept
- Primary users
- Enterprise employees / Procurement teams / Knowledge workers
Evidence first
What is proven, what I changed, and what remains open.
A 90-second evidence map for hiring review. Synthetic product views explain mechanics; they never count as outcome proof.
- Verified now
- A documented portfolio direction, user-intent model, and hub-and-spoke product narrative.
- Next evidence gate
- A tested intent set and transparent routing prototype with fallback and escalation evidence.
User / workflow evidence
Supported synthesisThe opportunity is grounded in a growing specialist-product estate where users know the outcome they need but not which governed capability should handle it.
Product decision
Supported synthesisOne door, specialist rooms: move discovery into the product while preserving distinct specialist workflows, constraints, and ownership.
AI / system boundary
Designed · not yet measuredCapability contracts, permissions, eligibility, fallback, and handoff state remain product controls; AI interprets intent and explains the route; high-impact ambiguity returns to a human owner.
Evaluation → change
Designed · not yet measuredThe proposed gate measures top-intent route precision, clarification quality, low-confidence fallback, and whether users understand why a specialist route was chosen.
Outcome / boundary
Open evidence gapNo implemented router, production accuracy, adoption, or enterprise deployment is claimed. The current evidence supports product direction, not delivery.
AI product decision record
The choices behind the product, architecture, and release gate.
Use AI to understand intent; keep capability eligibility, fallback, and explanation as governed portfolio infrastructure.
AI fit
Supported synthesisUse an LLM to interpret intent and ask a discriminating clarification question when multiple products could fit.
Users express outcomes in natural language and should not need to understand the product catalogue first.
Alternative consideredA fixed taxonomy creates dead ends; one general assistant erases specialist depth and ownership.
Knowledge & context
Designed · not yet measuredRetrieve capability contracts, permissions, required inputs, and boundaries rather than answer from a business-content corpus.
This product selects a governed destination; it is not itself the specialist knowledge engine.
Alternative consideredGeneral RAG would blur routing evidence with downstream domain evidence.
System architecture
Designed · not yet measuredSeparate the host router from specialist products and expose a visible no-route or human fallback.
The portfolio needs independent lifecycle and ownership while the user receives one coherent entry point.
Alternative consideredMCP may standardize discovery across hosts, but only after tool scopes, authorization, and capability metadata are stable.
Evaluation & release
Designed · not yet measuredMeasure route precision, clarification quality, low-confidence fallback, and user comprehension of the chosen route.
Correct routing is both a classification problem and a trust experience.
Alternative consideredClick-through or answer fluency cannot prove that the right governed product handled the request.
Evidence I still need from the private project record3 open items
- 01
Originating user interviews or request logs.
- 02
Implemented route and fallback states.
- 03
Accuracy, comprehension, and task-completion evidence from representative intents.
The product bet
One entry point for a growing ecosystem of specialist agents
A portfolio-level discovery and routing problem expressed as a simple user experience: begin with the decision, not the tool catalogue.
As specialist agents multiply, users struggle to understand where to begin and platform owners risk creating another fragmented tool landscape.
- Primary user
- An enterprise employee who knows the outcome they need but not which specialist agent can provide it.
- Trigger
- A business question enters a growing ecosystem of specialist AI products.
- Inputs
- User intent, task context, permissions, available specialist capabilities, confidence, and escalation rules.
- Product action
- Clarify the request, compare capability fit, explain the route, preserve context, and expose a fallback when no agent is appropriate.
- Output
- A transparent route into one specialist workflow or a human-owned alternative.
- Decision enabled
- Which product should handle the request, or whether the request needs clarification or human support.
Public-safe product demonstration
A complete workflow, without protected data.
The names, records, amounts, dates, scores, and thresholds inside this surface are fictional. The product logic is the point.
A fictional regional operations lead needs help with a delayed component before a product launch.
- Primary user
- Regional operations lead
- Decision enabled
- Route one ambiguous request to the right governed workflow.
Begin with the decision hidden inside the request.
The navigator translates a broad question into the decision, scope, deadline, and accountable user before suggesting a tool.
“A delayed component could affect our launch. Where should I start?”Region confirmed · supplier relationship missing
Names, requests, routes, and outputs are synthetic. This demonstrates the product logic, not an employer system or production result.
Product decision and trade-off
A single general assistant is easy to discover but weak at specialist work; a catalogue preserves depth but transfers the routing problem to the user.
- Product choice
- One door, specialist rooms: route from intent while keeping specialist workflows and ownership distinct.
- Rejected alternative
- A flat directory of tools or one undifferentiated chatbot.
- Product consequence
- Discovery becomes part of the product and routing quality becomes a measurable portfolio capability.
Two plausible specialist products match the same request and the system silently chooses one.
Ask one discriminating question, show the competing capability contracts, then disclose the selected route and fallback.
≥85% top-intent route precision on a representative intent set, with 100% visible fallback for low-confidence or out-of-scope requests.
The leadership pattern in its appropriate form.
This case is not retroactively enlarged into a Director mandate. It shows which product-lead behaviors were already present at this scope.
Turned an architecture question into a user decision and measurable experience.
Balanced local specialist value with ecosystem coherence and governance.
The same portfolio logic appears in platform strategy: standardize the reusable layer without flattening differentiated products.
Product contributor shaping the connected ecosystem narrative, user-intent model, and relationship between individual agents and a broader platform experience.
Evidence boundary
Impressive because the boundary is clear.
A coherent portfolio direction, intent model, and hub-and-spoke product narrative are documented.
- Created a coherent direction for agent discovery.
- Reduced conceptual fragmentation across the portfolio.
The routing workspace, sample request, route confidence, and benchmark are fictional demonstrations.
No production routing accuracy, adoption, or enterprise deployment is claimed.