For architects
Inspect the system.
Large enterprises don't fail for lack of models. They fail at the seams – between functions, systems, incentives, and operating histories. The Nablon stack, and the operators who deploy it, are built for the seams.
The operating graph
The enterprise operating graph.
Nine layers sit between your enterprise systems and your operating outcomes. The six in the middle (L2-L7) are the Nablon stack. L1, L8, and L9 are your systems, your people, and your P&L.
EVAL CONTROL PLANE
The gate that decides what is allowed to run in production.
- Rubric scoring per agent & per use-case
- Tiered policy enforcement (advisory → enforced)
- Override taxonomy + drift detection
- Production gating on regression-set pass
- Live / review / blocked status surfaced to operators
Touches
eval(agent, case) → {score, policy, drift, status}Illustrative shape · not production internals
Why it's shaped this way
Built for the complexity of the Fortune 500.
Large enterprises don't fail because they lack models. They fail because work is fragmented across functions, systems, incentives, and operating histories. Every layer above is a response to that.
Compounding domain intelligence
Ontology, evals, traces, and RL aren't features bolted on. They're the layers that make each deployment cheaper than the last.
Fortune 500 decision arc
Designed by people who've run platform migrations and survived the global-versus-local seam – not theorized about it.
Operator-grade precision
pass^k reliability, production gating, and outcome SLAs. The system is a sequence of operator decisions, not an architecture diagram.
Closed-loop improvement
Traces. Evals. RL.
The loop that compounds.
Production workflow → human in the loop → decision trace → eval → RL gym → improved agent → back into production. The loop is what makes the system compound: each new flow ships faster than the last because the trace corpus, the eval suite, and the RL environment all grow monotonically with every decision.
Evals control the system.
- Rubric scoring per agent & per use-case
- Policy enforcement tiered: advisory → enforced
- Override taxonomy & drift detection
- Production gating on regression-set pass
- Live / review / blocked status surfaced to operators
eval(agent, case) → {score, policy, drift, status}RL improves the system.
- State · POs, inventory, demand, margin band, policy
- Action · reroute, replen, concession, escalate, defer
- Reward · KPI Σ − λ·violations − override penalty
- Policy · offline traces → sim → shadow → eval-gated
- Workflow simulators per domain · synthetic + replay
π = argmax E[Σ γⁱ rᵢ]Traces feed both.
- Structured why-the-decision data captured per agent run
- Agent recommendation · human selection · reason for override
- Action taken · outcome observed · reward · eval score
- Every override becomes an eval case (suite grows monotonically)
- Every (s, a, r) tuple feeds the RL gym
trace = eval_case · rl_example · attributionPartners
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