# KORA Executive Summary

KORA is an open-source execution-control layer for AI workloads.

It does not build larger models.  
It changes when and why models are invoked.

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## The Core Problem

Most AI apps call the model too soon.

Every request becomes a prompt.
Every prompt becomes tokens.
Every token becomes latency, cost, and infrastructure pressure.

This leads to:

- Unnecessary model invocation
- Unnecessary token usage
- Latency unpredictability
- Centralized infrastructure dependence
- Weak execution governance

The limitation is not model capability.

It is invocation discipline.

---

## The Structural Shift

KORA separates two decisions:

1. Does this request require reasoning?
2. If so, what is the reasoning result?

Most systems answer both by invoking a model.

KORA answers the first structurally.

Structure first. Inference second.

Only necessary tasks reach model escalation.

---

## How It Works

KORA decomposes requests into atomic tasks.

Each task is:

- Typed
- Budget-bound
- Schema-validated
- Dependency-aware

Execution is graph-based, not prompt-based.

```mermaid
flowchart TD
    A[Request]
    A --> B[Task Graph]
    B --> C[Deterministic Tasks]
    B --> D[Model Tasks]
    C --> E[Aggregation]
    D --> E
    E --> F[Validated Output]
```

Deterministic tasks execute locally.  
Model tasks are invoked selectively.

---

## Key Architectural Guarantees

| Property | Meaning |
|-----------|---------|
| Deterministic-first | Trivial tasks never invoke model |
| Budget governance | max_tokens, max_time_ms enforced |
| Schema validation | Output must match explicit contract |
| Task isolation | Failures remain localized |
| Compute neutrality | Tasks may route across heterogeneous hardware |

Structure precedes scale.

---

## Measurement Model

If:

- P = proportion of deterministic work
- M = model invocation pressure
- O = structural overhead

KORA is beneficial when:

P * M > O

This inequality is measurable.

This is a measurement frame, not a production cost-reduction claim.

---

## Performance Implications

Latency becomes:

T_kora = T_overhead + (1 - P) * T_model

As deterministic coverage increases:

- Model load decreases
- Latency variance decreases
- Model invocation pressure becomes more inspectable

Parallel deterministic execution further improves throughput.

---

## Decentralized Compute

Because tasks are atomic and bounded, they can be routed to:

- Local CPU
- Lightweight on-device models
- Remote LLM APIs
- Distributed nodes

Monolithic prompts centralize compute.

Atomic tasks enable distribution.

---

## Long-Term Direction

KORA prepares the path toward:

- Distributed CPU cloud execution
- Task-level routing across devices
- Decomposition-native foundation models
- Budget-aware reasoning systems

It does not compete with models.

It governs them.

---

## Why It Matters

As models grow larger:

- Token costs increase.
- Infrastructure centralizes.
- Latency variability increases.

Structure becomes more important, not less.

KORA ensures that:

- Inference is deliberate.
- Budgets are enforced.
- Structure survives scale.

---

## Positioning

KORA is not:

- A chatbot wrapper
- A prompt toolkit
- An agent playground

KORA is:

- An execution architecture
- A decomposition-native system
- A budget-governed reasoning fabric

---

## Final Thesis

Modern AI scales reasoning.

KORA scales discipline.

**Structure first.  
Inference second.**
