# Core Concepts

Provon turns evidence from real agent runs into reviewable artifacts that support repair, knowledge
retention, evaluation, and model improvement.

## The Improvement Loop

```mermaid
flowchart LR
  agent["Agent"] --> capture["Gateway, OTLP, or transcript sync"]
  capture --> traces["Trace evidence"]
  traces --> findings["Findings"]
  traces --> knowledge["Knowledge"]
  traces --> datasets["Datasets"]
  findings --> repair["Repair workflow"]
  knowledge --> agent
  datasets --> evaluate["Evaluation or fine-tuning"]
  evaluate --> models["Improved model"]
  models --> gateway["AI Gateway"]
  gateway --> agent
```

Every derived artifact keeps a path back to the evidence that motivated it. This makes improvements
reviewable instead of treating model output or automated diagnosis as unquestioned truth.

## Traces

A Trace records one causally connected execution. For an agent, that can include model calls, tool
calls, retrieval, handoffs, latency, token usage, cost, errors, and the final response.

Multiple traces can belong to one conversation. Stable conversation identity lets Provon evaluate a
multi-turn outcome instead of judging isolated requests.

See [Tracing](../tracing/index.md) and the [Trace model](../tracing/trace-model.md).

## Findings

A Finding is a supported, actionable problem distilled from one or more conversation occurrences.
It combines a cause assessment, severity, confidence, remediation direction, and evidence
references.

Findings are intended for review and repair. They can be confirmed, dismissed, resolved, or handed
off to an issue tracker.

See [Findings](../findings/index.md).

## Knowledge

Knowledge captures reusable facts, constraints, and preferences found in conversations. Projects
define Learning Goals that tell Provon what is worth retaining.

Knowledge items remain linked to their source evidence and can be reviewed before being consumed by
people, coding agents, or Dataset collection policies.

See [Knowledge](../knowledge/index.md).

## Datasets

A Dataset owns structured Examples for evaluation and model improvement. Examples can preserve
successful behavior, correct failed conversations, learn from user corrections, or teach project
knowledge.

Datasets are consumption-neutral: the same reviewed examples can support evaluation, supervised
fine-tuning, or export to another system.

See [Datasets](../datasets/index.md).

## AI Gateway

The AI Gateway is an OpenAI-compatible model boundary. It resolves model targets, applies routing
and governance policy, records provider attempts, and emits trace evidence.

The Gateway is optional. Applications can continue calling providers directly and export
OpenTelemetry instead.

See [AI Gateway](../ai-gateway/index.md).

## AI Models

The Node deployment can import and serve open-weight models, train project-specific checkpoints
from Datasets, and expose running models behind the same Gateway contract used for cloud providers.

Model hosting and fine-tuning are separate from hosting the Provon control plane. See
[AI Models](../ai-models/index.md) and [Self-hosting](../self-hosting/index.md).

## Next Steps

- Follow the [Quickstart](./quickstart.md) for the first trace-to-Finding workflow.
- [Choose a capture path](./choose-capture-path.md) for an existing application.
- Run the [repair loop](./repair-loop.md) or the [model improvement loop](./model-improvement-loop.md).